From 46bf8b0b48c54f575634ce19ec17747446e99908 Mon Sep 17 00:00:00 2001 From: Floke Date: Tue, 23 Dec 2025 10:40:48 +0000 Subject: [PATCH] feat(b2b-marketing): Finalize grounding architecture and frontend improvements - Upgrade backend to use gemini-2.5-flash with sanitized HTML parsing (no token limit). - Implement robust retry logic and increased timeouts (600s) for deep analysis. - Add file-based logging for prompts and responses. - Fix API endpoint (v1) and regex parsing issues. - Frontend: Optimize PDF export (landscape, no scrollbars), fix copy-paste button, add 'Repeat Step 6' feature. - Update documentation to 'Completed' status. --- b2b-marketing-assistant/App.tsx | 218 +- b2b-marketing-assistant/Dockerfile | 55 + .../components/StepDisplay.tsx | 44 +- b2b-marketing-assistant/constants.ts | 259 -- b2b-marketing-assistant/index.html | 62 +- b2b-marketing-assistant/package-lock.json | 2694 +++++++++++++++++ b2b-marketing-assistant/package.json | 25 +- b2b-marketing-assistant/requirements.txt | 5 + b2b-marketing-assistant/server.cjs | 175 ++ b2b-marketing-assistant/services/parser.ts | 129 - b2b_marketing_assistant_plan.md | 102 + b2b_marketing_orchestrator.py | 559 ++++ 12 files changed, 3758 insertions(+), 569 deletions(-) create mode 100644 b2b-marketing-assistant/Dockerfile create mode 100644 b2b-marketing-assistant/package-lock.json create mode 100644 b2b-marketing-assistant/requirements.txt create mode 100644 b2b-marketing-assistant/server.cjs delete mode 100644 b2b-marketing-assistant/services/parser.ts create mode 100644 b2b_marketing_assistant_plan.md create mode 100644 b2b_marketing_orchestrator.py diff --git a/b2b-marketing-assistant/App.tsx b/b2b-marketing-assistant/App.tsx index 797322ec..50d2d158 100644 --- a/b2b-marketing-assistant/App.tsx +++ b/b2b-marketing-assistant/App.tsx @@ -1,14 +1,14 @@ -import React, { useState, useCallback, useRef } from 'react'; -import { GoogleGenAI, Chat } from "@google/genai"; +import React, { useState, useCallback } from 'react'; import { InputForm } from './components/InputForm'; import { StepDisplay } from './components/StepDisplay'; import { LoadingSpinner, BotIcon, SparklesIcon, MarkdownIcon, PrintIcon } from './components/Icons'; import { ExportMenu } from './components/ExportMenu'; -import { PROMPTS, translations } from './constants'; +import { translations } from './constants'; import type { AnalysisStep, AnalysisData, InputData } from './types'; -import { parseGeminiStepResponse } from './services/parser'; -import { generateMarkdown, downloadFile, tableToMarkdown } from './services/export'; +import { generateMarkdown, downloadFile } from './services/export'; + +const API_BASE_URL = '/api'; const App: React.FC = () => { const [inputData, setInputData] = useState({ @@ -20,10 +20,8 @@ const App: React.FC = () => { }); const [analysisData, setAnalysisData] = useState>({}); const [isLoading, setIsLoading] = useState(false); - const [isEnriching, setIsEnriching] = useState(false); const [error, setError] = useState(null); const [generationStep, setGenerationStep] = useState(0); // 0: idle, 1-6: step X is complete - const chatRef = useRef(null); const t = translations[inputData.language]; const STEP_TITLES = t.stepTitles; @@ -41,26 +39,28 @@ const App: React.FC = () => { setGenerationStep(0); try { - const ai = new GoogleGenAI({ apiKey: process.env.API_KEY }); - const currentPrompts = PROMPTS[inputData.language]; - - const newChat = ai.chats.create({ - model: 'gemini-2.5-pro', - config: { - systemInstruction: currentPrompts.SYSTEM_PROMPT.replace('{{language}}', inputData.language) - } + const response = await fetch(`${API_BASE_URL}/start-generation`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + companyUrl: inputData.companyUrl, + language: inputData.language, + regions: inputData.regions, + focus: inputData.focus, + }), }); - chatRef.current = newChat; - - let prompt = currentPrompts.STEP_PROMPTS[0]; - prompt = prompt.replace('{{company_url}}', inputData.companyUrl); - prompt = prompt.replace('{{language}}', inputData.language); - prompt = prompt.replace('{{regions}}', inputData.regions); - prompt = prompt.replace('{{focus}}', inputData.focus); - - const response = await newChat.sendMessage({ message: prompt }); - const parsedData = parseGeminiStepResponse(response.text); + if (!response.ok) { + const errorData = await response.json(); + throw new Error(errorData.details || `HTTP error! status: ${response.status}`); + } + + const parsedData = await response.json(); + + if (parsedData.error) { + throw new Error(parsedData.error); + } + setAnalysisData(parsedData); setGenerationStep(1); @@ -73,35 +73,33 @@ const App: React.FC = () => { }, [inputData]); const handleGenerateNextStep = useCallback(async () => { - if (!chatRef.current || generationStep >= 6) return; + if (generationStep >= 6) return; setIsLoading(true); setError(null); - const nextStepIndex = generationStep; try { - let context = ''; - for (let i = 0; i < generationStep; i++) { - const stepKey = STEP_KEYS[i]; - const stepObject = analysisData[stepKey]; - if (stepObject) { - context += `\n\n## ${STEP_TITLES[stepKey]}\n\n`; - const summary = stepObject.summary && stepObject.summary.length > 0 ? `**${t.summaryTitle}**\n${stepObject.summary.map(s => `* ${s}`).join('\n')}\n\n` : ''; - const table = tableToMarkdown(stepObject); - context += `${summary}${table}`; - } - } - - const currentPrompts = PROMPTS[inputData.language]; - let prompt = currentPrompts.STEP_PROMPTS[nextStepIndex]; - prompt = prompt.replace('{{previous_steps_data}}', context); + const response = await fetch(`${API_BASE_URL}/next-step`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + analysisData, + language: inputData.language, + channels: inputData.channels, + generationStep: generationStep + 1, // Pass the step we want to generate + }), + }); - if (nextStepIndex === 5) { // Step 6 is index 5 - prompt = prompt.replace('{{channels}}', inputData.channels.join(', ')); + if (!response.ok) { + const errorData = await response.json(); + throw new Error(errorData.details || `HTTP error! status: ${response.status}`); } + + const parsedData = await response.json(); - const response = await chatRef.current.sendMessage({ message: prompt }); - const parsedData = parseGeminiStepResponse(response.text); + if (parsedData.error) { + throw new Error(parsedData.error); + } setAnalysisData(prev => ({ ...prev, ...parsedData })); setGenerationStep(prev => prev + 1); @@ -113,120 +111,14 @@ const App: React.FC = () => { } finally { setIsLoading(false); } - }, [analysisData, generationStep, inputData.channels, inputData.language, STEP_KEYS, STEP_TITLES, t.summaryTitle]); + }, [analysisData, generationStep, inputData.channels, inputData.language]); const handleDataChange = (step: K, newData: AnalysisData[K]) => { if (analysisData[step]) { setAnalysisData(prev => prev ? { ...prev, [step]: newData } : { [step]: newData }); } }; - - const getEnrichPrompt = (lang: 'de' | 'en', url: string, productName: string) => { - if (lang === 'en') { - return `# Task -Fill in the information for the following product/solution based on the website ${url}. Respond ONLY with the content for the remaining 4 columns, separated by '|||'. - -# Product/Solution -${productName} - -# Column Order -1. Description (1–2 sentences) -2. Core Features -3. Differentiation -4. Primary Source (URL) - -# Important -Respond *only* with the text for the 4 columns, separated by '|||'. Do not output any headers or explanations.`; - } - // German (original) - return `# Aufgabe -Fülle die Informationen für das folgende Produkt/Lösung basierend auf der Webseite ${url} aus. Antworte NUR mit dem Inhalt für die restlichen 4 Spalten, getrennt durch '|||'. - -# Produkt/Lösung -${productName} - -# Spalten-Reihenfolge -1. Beschreibung (1–2 Sätze) -2. Kernfunktionen -3. Differenzierung -4. Primäre Quelle (URL) - -# Wichtig -Antworte *nur* mit dem Text für die 4 Spalten, getrennt durch '|||'. Gib keine Überschriften oder Erklärungen aus.`; - }; - - - const handleEnrichOfferRow = useCallback(async (productName: string, productUrl?: string) => { - if (!analysisData.offer) return; - - setIsEnriching(true); - setError(null); - - const loadingText = t.loadingButton.replace('...', ''); - const placeholderRow = [productName, loadingText, loadingText, loadingText, loadingText]; - - setAnalysisData(prev => { - if (!prev.offer) return prev; - const currentOffer = prev.offer; - const updatedRows = [...currentOffer.rows, placeholderRow]; - return { - ...prev, - offer: { - ...currentOffer, - rows: updatedRows, - } - }; - }); - - try { - const ai = new GoogleGenAI({ apiKey: process.env.API_KEY }); - // Use the specific product URL if provided, otherwise fallback to the main company URL - const targetUrl = productUrl && productUrl.trim() !== '' ? productUrl.trim() : inputData.companyUrl; - - const enrichPrompt = getEnrichPrompt(inputData.language, targetUrl, productName); - - const response = await ai.models.generateContent({ - model: 'gemini-2.5-flash', - contents: enrichPrompt - }); - const text = response.text || ''; - const enrichedParts = text.split('|||').map(s => s.trim()); - - const finalEnrichedData = Array(4).fill(''); - for (let i = 0; i < 4; i++) { - if (enrichedParts[i]) { - finalEnrichedData[i] = enrichedParts[i]; - } - } - - const newRow = [productName, ...finalEnrichedData]; - - setAnalysisData(prev => { - const offerData = prev.offer; - if (!offerData) return prev; - const finalRows = offerData.rows.map(row => - row[0] === productName && row[1] === loadingText ? newRow : row - ); - return { ...prev, offer: { ...offerData, rows: finalRows } }; - }); - - } catch (e) { - console.error(e); - // On error, clear loading text so user can edit manually - setAnalysisData(prev => { - const offerData = prev.offer; - if (!offerData) return prev; - const emptyRow = [productName, '', '', '', '']; - const finalRows = offerData.rows.map(row => - row[0] === productName && row[1] === loadingText ? emptyRow : row - ); - return { ...prev, offer: { ...offerData, rows: finalRows } }; - }); - } finally { - setIsEnriching(false); - } - }, [analysisData.offer, inputData.companyUrl, inputData.language, t.loadingButton]); - + const handleDownloadMarkdown = () => { if (!analysisData) return; const markdownContent = generateMarkdown(analysisData as AnalysisData, STEP_TITLES, t.summaryTitle); @@ -251,9 +143,9 @@ Antworte *nur* mit dem Text für die 4 Spalten, getrennt durch '|||'. Gib keine headers={step.headers} rows={step.rows} onDataChange={(newRows) => handleDataChange(stepKey, { ...step, rows: newRows })} - canAddRows={stepKey === 'offer'} - onEnrichRow={stepKey === 'offer' ? handleEnrichOfferRow : undefined} - isEnriching={isEnriching} + canAddRows={false} // Disabled enrich functionality + onEnrichRow={undefined} + isEnriching={false} canDeleteRows={canDelete} t={t} /> @@ -359,7 +251,7 @@ Antworte *nur* mit dem Text für die 4 Spalten, getrennt durch '|||'. Gib keine {t.analysisCompleteText.replace('{{otherLanguage}}', t.otherLanguage)}

-
+
+ +
+

Ergebnis verfeinern?

+ +
)} diff --git a/b2b-marketing-assistant/Dockerfile b/b2b-marketing-assistant/Dockerfile new file mode 100644 index 00000000..59502482 --- /dev/null +++ b/b2b-marketing-assistant/Dockerfile @@ -0,0 +1,55 @@ +# Stage 1: Build the React frontend +FROM node:20-slim AS frontend-builder + +WORKDIR /app + +# Copy package.json and install all dependencies +# Paths are relative to the build context (project root) +COPY b2b-marketing-assistant/package.json ./ +RUN npm install + +# Copy the rest of the frontend application code +COPY b2b-marketing-assistant/ . + +# Build the application +RUN npm run build + +# --- + +# Stage 2: Final application image +FROM python:3.11-slim + +WORKDIR /app + +# Install system dependencies (minimal) +# We use NodeSource to get a clean, modern Node.js install without bloat +RUN apt-get update && \ + apt-get install -y --no-install-recommends curl ca-certificates && \ + curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && \ + apt-get install -y --no-install-recommends nodejs && \ + rm -rf /var/lib/apt/lists/* + +# Install Python dependencies +COPY b2b-marketing-assistant/requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +# Copy the Node.js server and its production dependencies manifest +COPY b2b-marketing-assistant/server.cjs . +COPY b2b-marketing-assistant/package.json . + +# Install only production dependencies for the Node.js server +RUN npm install --omit=dev + +# Copy the built React app from the builder stage +COPY --from=frontend-builder /app/dist ./dist + +# Copy the main Python orchestrator script from the project root +COPY b2b_marketing_orchestrator.py . +# Copy Gemini API Key file if it exists in root +COPY gemini_api_key.txt . + +# Expose the port the Node.js server will run on +EXPOSE 3002 + +# The command to run the application +CMD ["node", "server.cjs"] \ No newline at end of file diff --git a/b2b-marketing-assistant/components/StepDisplay.tsx b/b2b-marketing-assistant/components/StepDisplay.tsx index e79aeb4e..2a417f61 100644 --- a/b2b-marketing-assistant/components/StepDisplay.tsx +++ b/b2b-marketing-assistant/components/StepDisplay.tsx @@ -51,19 +51,55 @@ export const StepDisplay: React.FC = ({ title, summary, header onDataChange(newRows); }; + const fallbackCopyTextToClipboard = (text: string) => { + const textArea = document.createElement("textarea"); + textArea.value = text; + + // Ensure textarea is not visible but part of DOM + textArea.style.position = "fixed"; + textArea.style.left = "-9999px"; + textArea.style.top = "0"; + + document.body.appendChild(textArea); + textArea.focus(); + textArea.select(); + + try { + document.execCommand('copy'); + setCopySuccess(t.copySuccess); + setTimeout(() => setCopySuccess(''), 2000); + } catch (err) { + console.error('Fallback: Oops, unable to copy', err); + setCopySuccess(t.copyFailure); + setTimeout(() => setCopySuccess(''), 2000); + } + + document.body.removeChild(textArea); + }; + const handleCopyToClipboard = (text: string) => { - navigator.clipboard.writeText(text).catch(err => console.error('Failed to copy text: ', err)); + if (!navigator.clipboard) { + fallbackCopyTextToClipboard(text); + return; + } + navigator.clipboard.writeText(text).catch(err => { + console.error('Failed to copy text: ', err); + fallbackCopyTextToClipboard(text); + }); }; const handleCopyTable = () => { const tsvString = convertArrayToTsv(headers, filteredRows); + if (!navigator.clipboard) { + fallbackCopyTextToClipboard(tsvString); + return; + } navigator.clipboard.writeText(tsvString).then(() => { setCopySuccess(t.copySuccess); setTimeout(() => setCopySuccess(''), 2000); }).catch(err => { console.error('Failed to copy table: ', err); - setCopySuccess(t.copyFailure); - setTimeout(() => setCopySuccess(''), 2000); + fallbackCopyTextToClipboard(tsvString); }); }; @@ -173,7 +209,7 @@ export const StepDisplay: React.FC = ({ title, summary, header -
+
diff --git a/b2b-marketing-assistant/constants.ts b/b2b-marketing-assistant/constants.ts index bbc480f6..407383a5 100644 --- a/b2b-marketing-assistant/constants.ts +++ b/b2b-marketing-assistant/constants.ts @@ -104,262 +104,3 @@ export const translations = { } } }; - - -const SYSTEM_PROMPT_DE = ` -# Systemrolle - -Du bist ein **B2B-Marketing-Researcher & Copywriter**. Du analysierst eine Unternehmens-URL, identifizierst Angebot, Zielgruppen, konkrete Zielrollen, deren Painpoints sowie Gains und formulierst darauf basierend eine wertschätzende, fachkundige Marketingbotschaft. **Antworte nur mit Ergebnissen, keine Gedankengänge.** Belege jede Aussage mit einer konkreten Seiten-URL der analysierten Domain. Kennzeichne Unsicherheiten explizit. Deine Antwort muss immer in der Zielsprache {{language}} sein. - -# Arbeitsprinzipien - -1. **Quellenpriorisierung:** Produktseite → Lösungsseite → Branchen/Industrien/Referenzen → Unternehmens-/Über-uns-Seite → Blog/News (nur zur Verifikation). -2. **Faktenpolicy:** Nur aus der Domain der bereitgestellten URL ableiten; bei Vermutungen: als *Hypothese* kennzeichnen. -3. **B2B-Ton:** sachkundig, respektvoll, nicht marktschreierisch. -4. **Klarheit & Struktur:** Jede Stufe als Markdown-Tabelle + optionales Kurzresümee dokumentieren. -5. **Mindestens 4 spezifische Rollen** je Zielgruppe (nicht generisch). -6. **Kompakt & nützlich:** Präzise Formulierungen; keine Floskeln. -`; - -const STEP_PROMPTS_DE = [ - // Step 1: Offer - `# Aufgabe -Führe **Schritt 1 – Angebot verstehen (WAS)** für das folgende Unternehmen durch. - -# Eingaben -* **Unternehmens-URL:** \`{{company_url}}\` -* **Zielsprache der Ausgabe:** \`{{language}}\` -* **Region(en) / Märkte (optional):** \`{{regions}}\` -* **Produkt-/Lösungsfokus (optional):** \`{{focus}}\` - -# Anweisungen für Schritt 1 -* Extrahiere Produkt(e)/Leistung(en), Kernfunktionen, Differenzierung, relevante Werteversprechen. -* Erstelle ein kurzes Resümee (max. 4 Bulletpoints) der wichtigsten Erkenntnisse. -* **Output:** Tabelle mit Spalten: *Produkt/Lösung | Beschreibung (1–2 Sätze) | Kernfunktionen | Differenzierung | Primäre Quelle (URL)*. -* **Format-Anforderung:** Antworte NUR mit den Ergebnissen für diesen einen Schritt. Deine Antwort muss mit der Überschrift "## Schritt 1: Angebot (WAS)" beginnen und das Kurzresümee sowie die Markdown-Tabelle enthalten. Gib keine weiteren Erklärungen ab.`, - - // Step 2: Target Groups - `# Aufgabe -Führe nun **Schritt 2 – Zielgruppen (WER – Unternehmen)** durch. - -# Kontext: Validierte Ergebnisse aus vorherigen Schritten -{{previous_steps_data}} - -# Anweisungen für Schritt 2 -* Identifiziere B2B-Zielsegmente (Branchen/Unternehmensarten/Größen/Regionen) basierend auf dem gegebenen Angebot. -* **Output:** Tabelle: *Zielbranche/Segment | Typische Unternehmensmerkmale | Region(en) | Relevanzbeleg (URL)*. -* **Format-Anforderung:** Antworte NUR mit den Ergebnissen für diesen einen Schritt. Deine Antwort muss mit der Überschrift "## Schritt 2: Zielgruppen (Unternehmen)" beginnen und die Markdown-Tabelle enthalten.`, - - // Step 3: Personas - `# Aufgabe -Führe nun **Schritt 3 – Zielpersonen/Rollen (WER – Personen)** durch. - -# Kontext: Validierte Ergebnisse aus vorherigen Schritten -{{previous_steps_data}} - -# Anweisungen für Schritt 3 -* Für jede Zielbranche: mind. 4 **spezifische** Rollen mit Verantwortungsbereich und Kaufbeteiligung (E, I, D, U nach RACI-Logik). Erfinde **keine** Personen; leite Rollen logisch aus Problem-/Prozessbezug ab. -* **Output:** Tabelle: *Rolle (präzise) | Verantwortungsbereich | Warum relevant für Produkt | Kaufbeteiligung (E/I/D/U) | Quelle/Indiz (URL oder Referenz)*. -* **Format-Anforderung:** Antworte NUR mit den Ergebnissen für diesen einen Schritt. Deine Antwort muss mit der Überschrift "## Schritt 3: Zielpersonen (Rollen)" beginnen.`, - - // Step 4: Pain Points - `# Aufgabe -Führe nun **Schritt 4 – Painpoints je Rolle (WARUM)** durch. - -# Kontext: Validierte Ergebnisse aus vorherigen Schritten -{{previous_steps_data}} - -# Anweisungen für Schritt 4 -* Formuliere pro Rolle 3–5 konkrete Painpoints (Beobachtungen, keine Features). -* Tagge jeden Painpoint mit einer Kategorie: **Kosten | Zeit | Risiko | Compliance | Qualität | Mitarbeiterbindung.** -* Füge eine **Impact-Schätzung (€, h, %)** als Hypothese hinzu. -* **Output:** Tabelle: *Rolle | Painpoint (konkret, messbar/operativ) | Kategorie | Auswirkung (Kosten, Risiko, Zeit) | Impact-Schätzung (€, h, %) | Dringlichkeit (hoch/mittel/niedrig) | Quelle/Indiz (URL)*. -* **Format-Anforderung:** Antworte NUR mit den Ergebnissen für diesen einen Schritt. Deine Antwort muss mit der Überschrift "## Schritt 4: Painpoints je Rolle" beginnen.`, - - // Step 5: Gains - `# Aufgabe -Führe nun **Schritt 5 – Gains & Nutzen je Rolle (WARUM wechseln)** durch. - -# Kontext: Validierte Ergebnisse aus vorherigen Schritten -{{previous_steps_data}} - -# Anweisungen für Schritt 5 -* Basierend auf den identifizierten Painpoints, formuliere pro Rolle 2-3 konkrete Gains (Vorteile/Nutzen). -* Quantifiziere den Nutzen als Hypothese (z.B. Einsparung in €, Zeitgewinn in h, Effizienzsteigerung in %). -* **Output:** Tabelle: *Rolle | Gain (konkreter Nutzen) | Quantifizierung (Hypothese in €, h, %) | Quelle/Indiz (URL)*. -* **Format-Anforderung:** Antworte NUR mit den Ergebnissen für diesen einen Schritt. Deine Antwort muss mit der Überschrift "## Schritt 5: Gains & Nutzen je Rolle" beginnen.`, - - // Step 6: Messages - `# Aufgabe -Führe nun **Schritt 6 – Marketingbotschaft (WIE sprechen)** durch. - -# Kontext: Validierte Ergebnisse aus vorherigen Schritten -{{previous_steps_data}} - -# Eingaben für diesen Schritt -* **Gewünschte Kanäle für die Botschaft:** \`{{channels}}\` - -# Anweisungen für Schritt 6: Chain-of-Thought-Analyse & Texterstellung - -**Dein Ziel ist es, für JEDE Zielbranche aus Schritt 2 eine EIGENE, spezifische Botschaft für JEDE Rolle aus Schritt 3 zu erstellen. Das Ergebnis MUSS eine vollständige Matrix sein (z.B. 3 Zielbranchen x 4 Rollen = 12 einzigartige Botschaften).** - -Führe für jede Kombination aus **[Zielbranche/Segment]** und **[Rolle]** den folgenden Denkprozess durch, bevor du die finale Botschaft formulierst: - -1. **Schritt 6.1 (Analyse): Produkt-Rollen-Fit.** - * Welches Produkt/welche Lösung aus der "Angebot"-Tabelle (Schritt 1) ist für die **[Rolle]** am relevantesten? - * *Beispiel-Gedanke:* "Für den Einsatzleiter im Kundenservice ist die Software zur Einsatzplanung relevanter als die mobile App, da er für die Disposition zuständig ist." - -2. **Schritt 6.2 (Analyse): Branchen-Use-Case.** - * Was sind 1-2 typische Anwendungsfälle für das ausgewählte Produkt in der **[Zielbranche/Segment]**? Was macht die **[Rolle]** damit konkret? - * *Beispiel-Gedanke:* "Ein Servicetechniker im Maschinenbau nutzt die mobile App typischerweise, um auf Wartungsprotokolle zuzugreifen und digitale Serviceberichte direkt beim Kunden zu erstellen." - -3. **Schritt 6.3 (Analyse): Nutzen-Quantifizierung.** - * Betrachte die Painpoints (Schritt 4) und Gains (Schritt 5) für die **[Rolle]**. Welcher Painpoint ist am dringendsten, welcher Gain am überzeugendsten? - * Leite daraus einen konkreten, für die **[Rolle]** relevanten KPI ab (z.B. First-Contact-Resolution-Rate, Zeit-bis-zur-Lösung, Anlagenausfallzeit). - * Formuliere den quantifizierbaren Nutzen aus Schritt 5 in Bezug auf diesen KPI. - * *Beispiel-Gedanke:* "Der Painpoint des Leiters Instandhaltung ist 'ungeplante Anlagenausfälle'. Der Gain ist 'Reduzierung der Ausfallzeiten um 15%'. Der relevante KPI ist die 'Overall Equipment Effectiveness (OEE)'. Der Nutzen ist die Steigerung der OEE durch vorausschauende Wartungsplanung." - -4. **Schritt 6.4 (Synthese): Botschaft formulieren.** - * Synthetisiere die Erkenntnisse aus 6.1-6.3 zu einer prägnanten Kernbotschaft (2-3 Sätze) nach der Struktur: **Beobachtung (Problem) → Niedrigschwellige Lösungsidee → Produkt-Brücke → Quantifizierter Nutzen.** - * Erstelle Varianten dieser Botschaft für die Kanäle: {{channels}}. - -# Output-Format -Erstelle NUR die finale Markdown-Tabelle. Der Denkprozess (6.1-6.3) ist eine interne Anweisung an dich und soll NICHT im Output erscheinen. -* **Tabelle-Spalten:** *Zielbranche/Segment | Rolle | Kernbotschaft (2–3 Sätze) | {{channels}}*. -* **Anforderung:** Deine Antwort muss mit der Überschrift "## Schritt 6: Botschaften" beginnen und NUR die vollständige Markdown-Tabelle enthalten.`, -]; - - -const SYSTEM_PROMPT_EN = ` -# System Role - -You are a **B2B Marketing Researcher & Copywriter**. You analyze a company URL, identify the offer, target groups, specific target roles, their pain points and gains, and based on this, you formulate an appreciative, expert marketing message. **Answer only with results, no thought processes.** Support every statement with a specific page URL from the analyzed domain. Explicitly mark uncertainties. Your response must always be in the target language {{language}}. - -# Working Principles - -1. **Source Prioritization:** Product Page → Solutions Page → Industries/References → Company/About Us Page → Blog/News (for verification only). -2. **Fact Policy:** Only derive from the domain of the provided URL; for assumptions, mark them as a *hypothesis*. -3. **B2B Tone:** Knowledgeable, respectful, not salesy. -4. **Clarity & Structure:** Document each stage as a Markdown table + an optional short summary. -5. **At least 4 specific roles** per target group (not generic). -6. **Concise & Useful:** Precise wording; no clichés. -`; - -const STEP_PROMPTS_EN = [ - // Step 1: Offer - `# Task -Perform **Step 1 – Understand the Offer (WHAT)** for the following company. - -# Inputs -* **Company URL:** \`{{company_url}}\` -* **Target Language of Output:** \`{{language}}\` -* **Region(s) / Markets (optional):** \`{{regions}}\` -* **Product/Solution Focus (optional):** \`{{focus}}\` - -# Instructions for Step 1 -* Extract product(s)/service(s), core features, differentiation, and relevant value propositions. -* Create a short summary (max. 4 bullet points) of the key findings. -* **Output:** Table with columns: *Product/Solution | Description (1–2 sentences) | Core Features | Differentiation | Primary Source (URL)*. -* **Format Requirement:** Respond ONLY with the results for this single step. Your response must start with the heading "## Step 1: Offer (WHAT)" and include the short summary and the Markdown table. Do not provide any other explanations.`, - - // Step 2: Target Groups - `# Task -Now perform **Step 2 – Target Groups (WHO – Companies)**. - -# Context: Validated results from previous steps -{{previous_steps_data}} - -# Instructions for Step 2 -* Identify B2B target segments (industries/company types/sizes/regions) based on the given offer. -* **Output:** Table: *Target Industry/Segment | Typical Company Characteristics | Region(s) | Proof of Relevance (URL)*. -* **Format Requirement:** Respond ONLY with the results for this single step. Your response must start with the heading "## Step 2: Target Groups (Companies)" and include the Markdown table.`, - - // Step 3: Personas - `# Task -Now perform **Step 3 – Personas/Roles (WHO – People)**. - -# Context: Validated results from previous steps -{{previous_steps_data}} - -# Instructions for Step 3 -* For each target industry: at least 4 **specific** roles with their area of responsibility and involvement in purchasing (E, I, D, U based on RACI logic). Do **not** invent people; logically derive roles from problem/process context. -* **Output:** Table: *Role (precise) | Area of Responsibility | Why relevant for the product | Buying Involvement (E/I/D/U) | Source/Indication (URL or reference)*. -* **Format Requirement:** Respond ONLY with the results for this single step. Your response must start with the heading "## Step 3: Personas (Roles)".`, - - // Step 4: Pain Points - `# Task -Now perform **Step 4 – Pain Points per Role (WHY)**. - -# Context: Validated results from previous steps -{{previous_steps_data}} - -# Instructions for Step 4 -* For each role, formulate 3–5 specific pain points (observations, not features). -* Tag each pain point with a category: **Cost | Time | Risk | Compliance | Quality | Employee Retention.** -* Add an **Impact Estimate (€, h, %)** as a hypothesis. -* **Output:** Table: *Role | Pain Point (specific, measurable/operational) | Category | Impact (Cost, Risk, Time) | Impact Estimate (€, h, %) | Urgency (high/medium/low) | Source/Indication (URL)*. -* **Format Requirement:** Respond ONLY with the results for this single step. Your response must start with the heading "## Step 4: Pain Points per Role".`, - - // Step 5: Gains - `# Task -Now perform **Step 5 – Gains & Benefits per Role (WHY switch)**. - -# Context: Validated results from previous steps -{{previous_steps_data}} - -# Instructions for Step 5 -* Based on the identified pain points, formulate 2-3 concrete gains (advantages/benefits) for each role. -* Quantify the benefit as a hypothesis (e.g., savings in €, time gained in h, efficiency increase in %). -* **Output:** Table: *Role | Gain (specific benefit) | Quantification (Hypothesis in €, h, %) | Source/Indication (URL)*. -* **Format Requirement:** Respond ONLY with the results for this single step. Your response must start with the heading "## Step 5: Gains & Benefits per Role".`, - - // Step 6: Messages - `# Task -Now perform **Step 6 – Marketing Message (HOW to speak)**. - -# Context: Validated results from previous steps -{{previous_steps_data}} - -# Inputs for this step -* **Desired channels for the message:** \`{{channels}}\` - -# Instructions for Step 6: Chain-of-Thought Analysis & Copywriting - -**Your goal is to create a SEPARATE, specific message for EACH role from Step 3 within EACH target industry from Step 2. The result MUST be a complete matrix (e.g., 3 target industries x 4 roles = 12 unique messages).** - -For each combination of **[Target Industry/Segment]** and **[Role]**, perform the following thought process before writing the final message: - -1. **Step 6.1 (Analysis): Product-Role Fit.** - * Which product/solution from the "Offer" table (Step 1) is most relevant to the **[Role]**? - * *Example thought:* "For the Customer Service Manager, the scheduling software is more relevant than the mobile app because they are responsible for dispatching." - -2. **Step 6.2 (Analysis): Industry Use Case.** - * What are 1-2 typical use cases for the selected product in the **[Target Industry/Segment]**? What does the **[Role]** specifically do with it? - * *Example thought:* "A service technician in mechanical engineering typically uses the mobile app to access maintenance logs and create digital service reports directly on-site with the customer." - -3. **Step 6.3 (Analysis): Benefit Quantification.** - * Look at the pain points (Step 4) and gains (Step 5) for the **[Role]**. Which pain point is most urgent, which gain most convincing? - * Derive a concrete KPI relevant to the **[Role]** (e.g., First-Contact Resolution Rate, Time-to-Resolution, plant downtime). - * Formulate the quantifiable benefit from Step 5 in relation to this KPI. - * *Example thought:* "The Maintenance Manager's pain point is 'unplanned plant downtime'. The gain is 'reducing downtime by 15%'. The relevant KPI is 'Overall Equipment Effectiveness (OEE)'. The benefit is increasing OEE through predictive maintenance planning." - -4. **Step 6.4 (Synthesis): Formulate Message.** - * Synthesize the findings from 6.1-6.3 into a concise core message (2-3 sentences) following the structure: **Observation (Problem) → Low-threshold solution idea → Product bridge → Quantified benefit.** - * Create variations of this message for the channels: {{channels}}. - -# Output Format -Create ONLY the final Markdown table. The thought process (6.1-6.3) is an internal instruction for you and should NOT appear in the output. -* **Table Columns:** *Target Industry/Segment | Role | Core Message (2–3 sentences) | {{channels}}*. -* **Requirement:** Your response must start with the heading "## Step 6: Messages" and contain ONLY the complete Markdown table.`, -]; - -export const PROMPTS = { - de: { - SYSTEM_PROMPT: SYSTEM_PROMPT_DE, - STEP_PROMPTS: STEP_PROMPTS_DE, - }, - en: { - SYSTEM_PROMPT: SYSTEM_PROMPT_EN, - STEP_PROMPTS: STEP_PROMPTS_EN, - } -}; diff --git a/b2b-marketing-assistant/index.html b/b2b-marketing-assistant/index.html index 8760dbfa..3983331e 100644 --- a/b2b-marketing-assistant/index.html +++ b/b2b-marketing-assistant/index.html @@ -18,35 +18,77 @@ diff --git a/b2b-marketing-assistant/package-lock.json b/b2b-marketing-assistant/package-lock.json new file mode 100644 index 00000000..8e370f60 --- /dev/null +++ b/b2b-marketing-assistant/package-lock.json @@ -0,0 +1,2694 @@ +{ + "name": 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"vite": "^6.2.0" + "@types/cors": "^2.8.17", + "@types/express": "^4.17.21", + "@types/node": "^20.10.4", + "@types/react": "^18.2.43", + "@types/react-dom": "^18.2.17", + "@vitejs/plugin-react": "^4.2.1", + "typescript": "^5.2.2", + "vite": "^5.0.8" } -} +} \ No newline at end of file diff --git a/b2b-marketing-assistant/requirements.txt b/b2b-marketing-assistant/requirements.txt new file mode 100644 index 00000000..478d508b --- /dev/null +++ b/b2b-marketing-assistant/requirements.txt @@ -0,0 +1,5 @@ +google-generativeai +requests +beautifulsoup4 +lxml +python-dotenv \ No newline at end of file diff --git a/b2b-marketing-assistant/server.cjs b/b2b-marketing-assistant/server.cjs new file mode 100644 index 00000000..1d38fe7b --- /dev/null +++ b/b2b-marketing-assistant/server.cjs @@ -0,0 +1,175 @@ +const express = require('express'); +const { spawn } = require('child_process'); +const bodyParser = require('body-parser'); +const cors = require('cors'); +const fs = require('fs'); +const path = require('path'); + +const app = express(); +// Port 3002, um Konflikte mit dem Market Intelligence Tool (3001) und dem React Dev Server (3000) zu vermeiden +const PORT = 3002; + +// Middleware +app.use(cors()); +app.use(bodyParser.json({ limit: '10mb' })); // Erhöhe das Limit für potenziell große Payloads + +const PYTHON_EXECUTABLE = 'python3'; // Annahme, dass python3 im PATH des Containers ist +// Im Docker-Container liegen server.cjs und das Python-Skript im selben Verzeichnis (/app) +const SCRIPT_PATH = path.join(__dirname, 'b2b_marketing_orchestrator.py'); + + +// Helper-Funktion zum Ausführen des Python-Skripts +const runPythonScript = (args, res) => { + console.log(`[${new Date().toISOString()}] Spawning: ${PYTHON_EXECUTABLE} ${args.join(' ')}`); + + const pythonProcess = spawn(PYTHON_EXECUTABLE, args); + + let pythonOutput = ''; + let pythonError = ''; + + pythonProcess.stdout.on('data', (data) => { + pythonOutput += data.toString(); + }); + + pythonProcess.stderr.on('data', (data) => { + pythonError += data.toString(); + }); + + pythonProcess.on('close', (code) => { + console.log(`[${new Date().toISOString()}] Python script finished with exit code: ${code}`); + + if (pythonError) { + console.log(`--- STDERR ---`); + console.log(pythonError); + console.log(`----------------`); + } + + if (code !== 0) { + console.error('Python script exited with an error.'); + return res.status(500).json({ + error: 'An error occurred in the backend script.', + details: pythonError + }); + } + + try { + const result = JSON.parse(pythonOutput); + res.json(result); + } catch (parseError) { + console.error('Failed to parse Python output as JSON:', parseError); + res.status(500).json({ + error: 'Invalid JSON response from the backend script.', + rawOutput: pythonOutput, + details: pythonError + }); + } + }); + + pythonProcess.on('error', (err) => { + console.error('FATAL: Failed to start the python process itself.', err); + res.status(500).json({ + error: 'Failed to start the backend process.', + details: err.message + }); + }); +}; + + +// API-Endpunkt, um eine neue Analyse zu starten (Schritt 1) +app.post('/api/start-generation', (req, res) => { + console.log(`[${new Date().toISOString()}] HIT: /api/start-generation`); + const { companyUrl, language, regions, focus } = req.body; + + if (!companyUrl || !language) { + return res.status(400).json({ error: 'Missing required parameters: companyUrl and language.' }); + } + + const args = [ + SCRIPT_PATH, + '--mode', 'start_generation', + '--url', companyUrl, + '--language', language + ]; + + if (regions) args.push('--regions', regions); + if (focus) args.push('--focus', focus); + + runPythonScript(args, res); +}); + + +// API-Endpunkt, um den nächsten Schritt zu generieren +app.post('/api/next-step', (req, res) => { + console.log(`[${new Date().toISOString()}] HIT: /api/next-step`); + const { analysisData, language, channels, generationStep } = req.body; + + if (!analysisData || !language || generationStep === undefined) { + return res.status(400).json({ error: 'Missing required parameters: analysisData, language, generationStep.' }); + } + + // Wir schreiben die komplexen Kontext-Daten in eine temporäre Datei, um die Kommandozeile sauber zu halten. + const tmpDir = path.join(__dirname, 'tmp'); + if (!fs.existsSync(tmpDir)) { + fs.mkdirSync(tmpDir); + } + const contextFilePath = path.join(tmpDir, `context_${Date.now()}.json`); + + try { + fs.writeFileSync(contextFilePath, JSON.stringify(analysisData)); + + const args = [ + SCRIPT_PATH, + '--mode', 'next_step', + '--language', language, + '--context_file', contextFilePath, + '--generation_step', generationStep.toString() + ]; + + if (channels && Array.isArray(channels)) { + args.push('--channels', channels.join(',')); + } + + // Da die runPythonScript-Funktion res behandelt, fügen wir hier die Bereinigung hinzu + const originalJson = res.json.bind(res); + res.json = (data) => { + if (fs.existsSync(contextFilePath)) { + fs.unlinkSync(contextFilePath); + } + originalJson(data); + }; + + const originalStatus = res.status.bind(res); + res.status = (code) => { + // Wenn ein Fehler auftritt, rufen wir send auf, um die Bereinigung auszulösen + const originalSend = res.send.bind(res); + res.send = (body) => { + if (fs.existsSync(contextFilePath)) { + fs.unlinkSync(contextFilePath); + } + originalSend(body); + } + return originalStatus(code); + } + + + runPythonScript(args, res); + + } catch (error) { + console.error('Failed to write temporary context file:', error); + return res.status(500).json({ error: 'Failed to process request context.', details: error.message }); + } +}); + +// --- SERVE STATIC FRONTEND --- +// Serve static files from the 'dist' directory created by `npm run build` +app.use(express.static(path.join(__dirname, 'dist'))); + +// Handle client-side routing: return index.html for all non-API routes +app.get('*', (req, res) => { + res.sendFile(path.join(__dirname, 'dist', 'index.html')); +}); + +// Start des Servers +app.listen(PORT, () => { + console.log(`B2B Marketing Assistant API Bridge running on http://localhost:${PORT}`); +}); diff --git a/b2b-marketing-assistant/services/parser.ts b/b2b-marketing-assistant/services/parser.ts deleted file mode 100644 index 088b1e52..00000000 --- a/b2b-marketing-assistant/services/parser.ts +++ /dev/null @@ -1,129 +0,0 @@ -import type { AnalysisData, AnalysisStep } from '../types'; - -function parseTable(tableLines: string[]): { headers: string[], rows:string[][] } { - if (tableLines.length < 2) return { headers: [], rows: [] }; - - // Removes leading/trailing markdown asterisks (for bold/italic) from a string. - const cleanMarkdownFormatting = (text: string) => { - return text.trim().replace(/^\*+|\*+$/g, ''); - }; - - const headers = tableLines[0] - .split('|') - .map(h => cleanMarkdownFormatting(h)) - .filter(h => h); - - const rows = tableLines.slice(2) // Skip header and separator line - .map(rowLine => { - const cells = rowLine - .split('|') - .map(c => cleanMarkdownFormatting(c)) - .filter((c, i, arr) => i > 0 && i < arr.length - 1); // remove first and last empty string from split - return cells; - }) - .filter(row => { - if (row.length === 0 || !row.some(cell => cell.trim() !== '')) return false; - // Filter out any row that looks like a markdown separator, e.g. |:---|:---| - // This checks if all cells are composed of dashes and optional colons - const isSeparator = row.every(cell => /^:?-+:?$/.test(cell.trim())); - return !isSeparator; - }); - - return { headers, rows }; -} - -function parseSection(sectionText: string): AnalysisStep { - const lines = sectionText.split('\n').filter(line => line.trim() !== ''); - let summary: string[] = []; - let tableStartIndex = -1; - - const summaryMatch = sectionText.match(/\*\*(?:Kurzresümee|Summary).*?:\*\*\s*([\s\S]*?)(\n\n|\| ---|## (?:Schritt|Step))/i); - if(summaryMatch && summaryMatch[1]) { - summary = summaryMatch[1].split('\n').map(s => s.replace(/^[*\-]\s*/, '').trim()).filter(Boolean); - } - - for (let i = 0; i < lines.length; i++) { - if (lines[i].includes('|') && lines[i+1]?.includes('---')) { - tableStartIndex = i; - break; - } - } - - if (tableStartIndex === -1) { - return { headers: [], rows: [], summary }; - } - - const tableLines = lines.slice(tableStartIndex); - const { headers, rows } = parseTable(tableLines); - - // In step 6, headers can be dynamic based on user channel selection. Let's fix them. - if (sectionText.match(/## (?:Schritt|Step) 6:/i)) { - const foundChannels = headers.slice(3); // Channels start after Zielbranche, Rolle, Kernbotschaft - if(foundChannels.length > 0) { - const defaultHeaders = headers.slice(0, 3); - const dynamicHeaders = foundChannels.join(', '); - headers.splice(3, foundChannels.length, dynamicHeaders); - } - } - - - return { headers, rows, summary }; -} - -/** - * Fills empty cells in a specified column by carrying down the last non-empty value from preceding rows. - * This is useful for tables where grouping values are not repeated for each row. - * @param step The analysis step object containing headers and rows. - * @param columnNames An array of possible column names (e.g., ['Rolle', 'Role']) to fill down. - * @returns The analysis step with the specified column filled. - */ -const fillDownColumn = (step: AnalysisStep, columnNames: string[]): AnalysisStep => { - const columnIndex = step.headers.findIndex(h => - columnNames.some(name => h.toLowerCase().trim().startsWith(name.toLowerCase().trim())) - ); - - if (columnIndex === -1 || !step.rows || step.rows.length === 0) { - return step; - } - - let lastSeenValue = ''; - const filledRows = step.rows.map(row => { - const newRow = [...row]; - if (newRow.length > columnIndex) { - const currentCell = newRow[columnIndex] || ''; // handle undefined - if (currentCell.trim() !== '') { - lastSeenValue = currentCell; - } else { - newRow[columnIndex] = lastSeenValue; - } - } - return newRow; - }); - - return { ...step, rows: filledRows }; -}; - - -export const parseGeminiStepResponse = (markdown: string): Partial => { - const stepMatch = markdown.match(/## (?:Schritt|Step) (\d+):/i); - if (!stepMatch) return {}; - - const stepNumber = parseInt(stepMatch[1], 10); - const stepKeys: (keyof AnalysisData)[] = ['offer', 'targetGroups', 'personas', 'painPoints', 'gains', 'messages']; - const currentStepKey = stepKeys[stepNumber - 1]; - - if (!currentStepKey) return {}; - - let stepData = parseSection(markdown); - - // Post-processing to fill down empty cells in key columns for better filtering and readability. - if (['personas', 'painPoints', 'gains'].includes(currentStepKey)) { - stepData = fillDownColumn(stepData, ['Rolle', 'Role']); - } - if (currentStepKey === 'messages') { - stepData = fillDownColumn(stepData, ['Zielbranche/Segment', 'Target Industry/Segment']); - stepData = fillDownColumn(stepData, ['Rolle', 'Role']); - } - - return { [currentStepKey]: stepData }; -}; diff --git a/b2b_marketing_assistant_plan.md b/b2b_marketing_assistant_plan.md new file mode 100644 index 00000000..089b6c65 --- /dev/null +++ b/b2b_marketing_assistant_plan.md @@ -0,0 +1,102 @@ +# Plan: Umsetzung des "B2B Marketing Assistant" Backends + +Dieses Dokument beschreibt den Plan zur Umsetzung der Backend-Logik für die React-Anwendung unter `/b2b-marketing-assistant` als robusten, faktenbasierten Python-Service. Das primäre Ziel ist es, die Konsistenz und Zuverlässigkeit der Analyseergebnisse durch "Grounding" (Verankerung in realen Daten) signifikant zu erhöhen. + +## 1. Zielsetzung & Architektur + +- **Ziel:** Transformation der reinen Frontend-Anwendung in einen Service mit einem Python-Backend, das vor jeder KI-Analyse eine solide Faktenbasis durch Web-Scraping schafft. Dadurch werden die Ergebnisse reproduzierbar und basieren auf den tatsächlichen Inhalten der Unternehmens-Website. +- **Architektur:** Wir replizieren den bewährten Aufbau des "Market Intelligence" Tools: + 1. **React-Frontend:** Die Benutzeroberfläche in `/b2b-marketing-assistant` bleibt bestehen, wird aber von direkten KI-Aufrufen befreit. + 2. **Node.js API-Brücke (`server.cjs`):** Ein minimaler Express.js-Server, der Anfragen vom Frontend annimmt und an das Python-Backend weiterleitet. + 3. **Python-Orchestrator (`b2b_marketing_orchestrator.py`):** Das neue Herzstück, das die gesamte Logik kapselt. + +## 2. Kernprozess mit "Grounding" + +Der 6-stufige Prozess der App wird im Backend abgebildet, wobei die ersten Schritte fundamental geändert werden: + +1. **Schritt 1 (Angebot) & 2 (Zielgruppen):** + * **Intelligentes Scraping:** Das Python-Skript crawlt die gegebene URL und sucht aktiv nach Unterseiten wie "Produkte", "Lösungen", "Branchen" etc. + * **Text-Extraktion:** Der relevante Inhalt dieser Seiten wird extrahiert und zu einem "Grounding-Dokument" zusammengefasst. + * **KI als Extraktions-Engine:** Die KI wird angewiesen, **ausschließlich auf Basis dieses extrahierten Textes** das Angebot und die Zielgruppen zu identifizieren und zu strukturieren. Halluzinationen werden so unterbunden. + +2. **Schritt 3-6 (Personas, Pains, Gains, Messages):** + * Diese Schritte bauen auf den validierten, faktenbasierten Ergebnissen aus Schritt 1 & 2 auf. Die gesamte Logikkette wird dadurch stabiler und konsistenter. + +## 3. Strategische Vision: Integration der Tools + +Dieses Projekt ist der erste Schritt zur Schaffung eines einheitlichen "Strategy & Audit"-Workflows. + +- **Phase 1 (Aktuelles Projekt):** Wir bauen den "B2B Marketing Assistant" als eigenständigen Service mit einem modularen Python-Backend. +- **Phase 2 (Zukünftig):** Die wiederverwendbaren Python-Module (Scraping, API-Handler etc.) werden mit dem `market_intel_orchestrator.py` zu einem einzigen, leistungsfähigen Backend verschmolzen. Der Workflow wäre dann nahtlos: + 1. **Strategie definieren:** Mit dem B2B Marketing Assistant eine Tiefenanalyse eines Referenzkunden durchführen. + 2. **Markt auditieren:** Die erstellte Strategie direkt nutzen, um Lookalikes zu finden und zu bewerten. + +## 4. Fortschritts-Log + +### Phase 1: Initialisierung & Planung + +- [x] Anforderungsanalyse und Zieldefinition (Grounding, Konsistenz). + +- [x] Architektur nach Vorbild `market-intel-backend` festgelegt. + +- [x] Diesen Schlachtplan in `b2b_marketing_assistant_plan.md` erstellt. + +- [x] Aufbau der Grundstruktur: Erstellen der `b2b_marketing_orchestrator.py`, der `server.cjs` in `/b2b-marketing-assistant` und des `Dockerfile`. + +- [x] Erstellung von `package.json` und `requirements.txt`. + +- [x] Anpassung des Frontends (`App.tsx`) für die Kommunikation mit dem neuen Backend. + +- [x] Entfernen von Frontend-Dateien und -Inhalten, die nicht mehr benötigt werden (`parser.ts`, Prompts aus `constants.ts`). + +- [x] Implementierung der `start_generation`-Logik im Python-Backend (Scraping, Grounding, initialer Gemini-Aufruf für Schritt 1). + +- [x] Implementierung der `next_step`-Logik im Python-Backend (mehrstufige Gemini-Aufrufe für Schritte 2-6, Kontext-Management). + +- [x] Fehlerbehebung: Alle Python-Syntaxfehler (Encoding, Strings) behoben. + +- [x] Validierung: Das Tool lädt das Frontend und führt das Web-Scraping erfolgreich durch. + +- [x] **API-Fix:** Umstellung auf Gemini v1 API und Modell `gemini-2.5-flash` (1M Token Context). + + + +### Phase 2: Validierung & Optimierung (Abgeschlossen) + +- [x] Docker-Container gebaut und gestartet. + +- [x] Zugriff auf die UI über Port 3004 erfolgreich. + +- [x] **Grounding Upgrade:** Umstellung von Plain-Text auf "Sanitized HTML" (H1-H6, Links erhalten) für präzise Produkterkennung. + +- [x] **Kontext-Erweiterung:** Entfernung des 30.000 Zeichen Limits für vollständige Website-Analyse. + +- [x] **Robustheit:** Implementierung von Retry-Logik (Exponential Backoff) und Timeout-Erhöhung (600s) für komplexe Analysen. + +- [x] **Frontend Fixes:** + + - [x] Robuster "Copy Table" Button (Fallback für Non-HTTPS). + + - [x] PDF-Export optimiert (Landscape, keine Scrollbalken). + + - [x] "Schritt 6 Wiederholen"-Funktion eingebaut. + +- [x] **Prozess-Optimierung:** Schritt 6 fokussiert nun automatisch auf die Top-Branche, um Token-Limits und Lesezeit zu optimieren. + +- [x] **Logging:** Detailliertes File-Logging (`Log_from_docker`) für Prompts und Antworten implementiert. + + + +## 5. Status: Produktionsbereit + + + +Das System liefert nun hochqualitative, faktenbasierte Analysen ("Grounding"), die weit über die ursprüngliche Online-Version hinausgehen. Alle bekannten Fehler (Timeouts, API 404, Copy-Paste) sind behoben. + + + +### Nächste Schritte (Optional) + +- Erweiterung auf Multi-Language Support im Frontend (aktuell DE fokussiert). + +- Integration von SerpAPI für noch breitere Marktrecherchen (analog Market Intel). diff --git a/b2b_marketing_orchestrator.py b/b2b_marketing_orchestrator.py new file mode 100644 index 00000000..4330740c --- /dev/null +++ b/b2b_marketing_orchestrator.py @@ -0,0 +1,559 @@ +# -*- coding: utf-8 -*- +import argparse +import json +import sys +import logging +import os +import re +from urllib.parse import urljoin, urlparse + +import requests +import time +from bs4 import BeautifulSoup +from datetime import datetime + +# Logging Setup +log_dir = "Log_from_docker" +if not os.path.exists(log_dir): + os.makedirs(log_dir) + +timestamp = datetime.now().strftime("%Y-%m-%d") +log_file = os.path.join(log_dir, f"{timestamp}_b2b_marketing.log") + +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(levelname)s - %(message)s', + handlers=[ + logging.FileHandler(log_file, mode='a', encoding='utf-8'), + logging.StreamHandler(sys.stderr) + ] +) + +def save_detailed_log(step_name, content_type, content): + """Saves detailed logs (prompts, raw responses) to separate files for inspection.""" + try: + ts = datetime.now().strftime("%H-%M-%S") + filename = f"{ts}_{step_name}_{content_type}.txt" + filepath = os.path.join(log_dir, filename) + with open(filepath, "w", encoding="utf-8") as f: + f.write(content) + logging.info(f"Detailed log saved: {filepath}") + except Exception as e: + logging.error(f"Failed to save detailed log: {e}") + +LINK_KEYWORDS = ['product', 'solution', 'service', 'industrie', 'branche', 'anwendung', 'produkt', 'loesung', 'dienstleistung', 'portfolio', 'angebot'] + +# --- PROMPTS (Single line strings with explicit newlines for maximum compatibility) --- + +SYSTEM_PROMPT_DE = """# Systemrolle + +Du bist ein **B2B-Marketing-Researcher & Copywriter**. Du analysierst eine Unternehmens-URL, identifizierst Angebot, Zielgruppen, konkrete Zielrollen, deren Painpoints sowie Gains und formulierst darauf basierend eine wertschaetzende, fachkundige Marketingbotschaft. **Antworte nur mit Ergebnissen, keine Gedankengaenge.** Belege jede Aussage mit einer konkreten Seiten-URL der analysierten Domain. Kennzeichne Unsicherheiten explizit. Deine Antwort muss immer in der Zielsprache {{language}} sein. + +# Arbeitsprinzipien + +1. **Quellenpriorisierung:** Produktseite -> Loesungsseite -> Branchen/Industrien/Referenzen -> Unternehmens-/Ueber-uns-Seite -> Blog/News (nur zur Verifikation). +2. **Faktenpolicy:** Nur aus der Domain der bereitgestellten URL ableiten; bei Vermutungen: als *Hypothese* kennzeichnen. +3. **B2B-Ton:** sachkundig, respektvoll, nicht marktschreierisch. +4. **Klarheit & Struktur:** Jede Stufe als Markdown-Tabelle + optionales Kurzresuemee dokumentieren. +5. **Mindestens 4 spezifische Rollen** je Zielgruppe (nicht generisch). +6. **Kompakt & nuetzlich:** Praezise Formulierungen; keine Floskeln.""" + +STEP_PROMPTS_DE = [ + """# Aufgabe +Fuehre **Schritt 1 - Angebot verstehen (WAS)** fuer das folgende Unternehmen durch. + +# Eingaben +* **Unternehmens-URL:** `{{company_url}}` +* **Zielsprache der Ausgabe:** `{{language}}` +* **Region(en) / Maerkte (optional):** `{{regions}}` +* **Produkt-/Loesungsfokus (optional):** `{{focus}}` + +# Anweisungen fuer Schritt 1 +* Extrahiere Produkt(e)/Leistung(en), Kernfunktionen, Differenzierung, relevante Werteversprechen. +* Erstelle ein kurzes Resuemee (max. 4 Bulletpoints) der wichtigsten Erkenntnisse. +* **Output:** Tabelle mit Spalten: *Produkt/Loesung | Beschreibung (1-2 Saetze) | Kernfunktionen | Differenzierung | Primaere Quelle (URL)*. +* **Format-Anforderung:** Antworte NUR mit den Ergebnissen fuer diesen einen Schritt. Deine Antwort muss mit der Ueberschrift \"## Schritt 1: Angebot (WAS)\" beginnen und das Kurzresuemee sowie die Markdown-Tabelle enthalten. Gib keine weiteren Erklaerungen ab.""", + """# Aufgabe +Fuehre nun **Schritt 2 - Zielgruppen (WER - Unternehmen)** durch. + +# Kontext: Validierte Ergebnisse aus vorherigen Schritten +{{previous_steps_data}} + +# Anweisungen fuer Schritt 2 +* Identifiziere B2B-Zielsegmente (Branchen/Unternehmensarten/Groessen/Regionen) basierend auf dem gegebenen Angebot. +* **Output:** Tabelle: *Zielbranche/Segment | Typische Unternehmensmerkmale | Region(en) | Relevanzbeleg (URL)*. +* **Format-Anforderung:** Antworte NUR mit den Ergebnissen fuer diesen einen Schritt. Deine Antwort muss mit der Ueberschrift \"## Schritt 2: Zielgruppen (Unternehmen)\" beginnen und die Markdown-Tabelle enthalten.""", + """# Aufgabe +Fuehre nun **Schritt 3 - Zielpersonen/Rollen (WER - Personen)** durch. + +# Kontext: Validierte Ergebnisse aus vorherigen Schritten +{{previous_steps_data}} + +# Anweisungen fuer Schritt 3 +* Fuer jede Zielbranche: mind. 4 **spezifische** Rollen mit Verantwortungsbereich und Kaufbeteiligung (E, I, D, U nach RACI-Logik). Erfinde **keine** Personen; leite Rollen logisch aus Problem-/Prozessbezug ab. +* **Output:** Tabelle: *Rolle (praezise) | Verantwortungsbereich | Warum relevant fuer Produkt | Kaufbeteiligung (E/I/D/U) | Quelle/Indiz (URL oder Referenz)*. +* **Format-Anforderung:** Antworte NUR mit den Ergebnissen fuer diesen einen Schritt. Deine Antwort muss mit der Ueberschrift \"## Schritt 3: Zielpersonen (Rollen)\" beginnen.""", + """# Aufgabe +Fuehre nun **Schritt 4 - Painpoints je Rolle (WARUM)** durch. + +# Kontext: Validierte Ergebnisse aus vorherigen Schritten +{{previous_steps_data}} + +# Anweisungen fuer Schritt 4 +* Formuliere pro Rolle 3-5 konkrete Painpoints (Beobachtungen, keine Features). +* Tagge jeden Painpoint mit einer Kategorie: **Kosten | Zeit | Risiko | Compliance | Qualitaet | Mitarbeiterbindung.** +* Fuege eine **Impact-Schaetzung (EUR, h, %)** als Hypothese hinzu. +* **Output:** Tabelle: *Rolle | Painpoint (konkret, messbar/operativ) | Kategorie | Auswirkung (Kosten, Risiko, Zeit) | Impact-Schaetzung (EUR, h, %) | Dringlichkeit (hoch/mittel/niedrig) | Quelle/Indiz (URL)*. +* **Format-Anforderung:** Antworte NUR mit den Ergebnissen fuer diesen einen Schritt. Deine Antwort muss mit der Ueberschrift \"## Schritt 4: Painpoints je Rolle\" beginnen.""", + """# Aufgabe +Fuehre nun **Schritt 5 - Gains & Nutzen je Rolle (WARUM wechseln)** durch. + +# Kontext: Validierte Ergebnisse aus vorherigen Schritten +{{previous_steps_data}} + +# Anweisungen fuer Schritt 5 +* Basierend auf den identifizierten Painpoints, formuliere pro Rolle 2-3 konkrete Gains (Vorteile/Nutzen). +* Quantifiziere den Nutzen als Hypothese (z.B. Einsparung in EUR, Zeitgewinn in h, Effizienzsteigerung in %). +* **Output:** Tabelle: *Rolle | Gain (konkreter Nutzen) | Quantifizierung (Hypothese in EUR, h, %) | Quelle/Indiz (URL)*. +* **Format-Anforderung:** Antworte NUR mit den Ergebnissen fuer diesen einen Schritt. Deine Antwort muss mit der Ueberschrift \"## Schritt 5: Gains & Nutzen je Rolle\" beginnen.""", + """# Aufgabe +Fuehre nun **Schritt 6 - Marketingbotschaft (WIE sprechen)** durch. + +# Kontext: Validierte Ergebnisse aus vorherigen Schritten +{{previous_steps_data}} + +# Eingaben fuer diesen Schritt +* **Gewuenschte Kanaele fuer die Botschaft:** `{{channels}}` + +# Anweisungen fuer Schritt 6: Chain-of-Thought-Analyse & Texterstellung + +**FOKUS:** Um die Analyse handhabbar zu machen, waehle aus Schritt 2 die **eine (1) relevanteste und vielversprechendste Zielbranche** (Primary Industry) aus. +Dein Ziel ist es, NUR fuer diese EINE Fokus-Branche eine spezifische Botschaft fuer JEDE Rolle aus Schritt 3 zu erstellen. + +Fuehre fuer jede **[Rolle]** innerhalb der ausgewaehlten **[Fokus-Branche]** den folgenden Denkprozess durch: + +1. **Schritt 6.1 (Analyse): Produkt-Rollen-Fit.** + * Welches Produkt/welche Loesung aus der "Angebot"-Tabelle (Schritt 1) ist fuer die **[Rolle]** am relevantesten? + +2. **Schritt 6.2 (Analyse): Branchen-Use-Case.** + * Was sind 1-2 typische Anwendungsfaelle fuer das ausgewaehlte Produkt in der **[Fokus-Branche]**? Was macht die **[Rolle]** damit konkret? + +3. **Schritt 6.3 (Analyse): Nutzen-Quantifizierung.** + * Betrachte die Painpoints (Schritt 4) und Gains (Schritt 5) fuer die **[Rolle]**. + * Leite daraus einen konkreten, fuer die **[Rolle]** relevanten KPI ab. + +4. **Schritt 6.4 (Synthese): Botschaft formulieren.** + * Synthetisiere die Erkenntnisse aus 6.1-6.3 zu einer praegnanten Kernbotschaft (2-3 Saetze) nach der Struktur: **Beobachtung (Problem) -> Niedrigschwellige Loesungsidee -> Produkt-Bruecke -> Quantifizierter Nutzen.** + * Erstelle Varianten dieser Botschaft fuer die Kanaele: {{channels}}. + +# Output Format +Erstelle ONLY die finale Markdown-Tabelle. +* **Table Columns:** *Fokus-Branche | Rolle | Kernbotschaft (2-3 sentences) | {{channels}}*. +* **Requirement:** Your response must start with the heading \"## Schritt 6: Botschaften\" and contain ONLY die vollstaendige Markdown-Tabelle.""", +] + +SYSTEM_PROMPT_EN = """# System Role + +You are a **B2B Marketing Researcher & Copywriter**. You analyze a company URL, identify the offer, target groups, specific target roles, their pain points and gains, and based on this, you formulate an appreciative, expert marketing message. **Answer only with results, no thought processes.** Support every statement with a specific page URL from the analyzed domain. Explicitly mark uncertainties. Your response must always be in the target language {{language}}. + +# Working Principles + +1. **Source Prioritization:** Product Page -> Solutions Page -> Industries/References -> Company/About Us Page -> Blog/News (for verification only). +2. **Fact Policy:** Only derive from the domain of the provided URL; for assumptions, mark them as a *hypothesis*. +3. B2B Tone: Knowledgeable, respectful, not salesy. +4. Clarity & Structure: Document each stage as a Markdown table + an optional short summary. +5. At least 4 specific roles per target group (not generic). +6. Concise & Useful: Precise wording; no clichés.""" + +STEP_PROMPTS_EN = [ + """# Task +Perform **Step 1 - Understand the Offer (WHAT)** for the following company. + +# Inputs +* **Company URL:** `{{company_url}}` +* **Target Language of Output:** `{{language}}` +* **Region(s) / Markets (optional):** `{{regions}}` +* **Product/Solution Focus (optional):** `{{focus}}` + +# Instructions for Step 1 +* Extract product(s)/service(s), core features, differentiation, and relevant value propositions. +* Create a short summary (max. 4 bullet points) of the key findings. +* **Output:** Table with columns: *Product/Solution | Description (1-2 sentences) | Core Features | Differentiation | Primary Source (URL)*. +* **Format Requirement:** Respond ONLY with the results for this single step. Your response must start with the heading \"## Step 1: Offer (WHAT)\" and include the short summary and the Markdown table. Do not provide any other explanations.""", + """# Task +Now perform **Step 2 - Target Groups (WHO - Companies)**. + +# Context: Validated results from previous steps +{{previous_steps_data}} + +# Instructions for Step 2 +* Identify B2B target segments (industries/company types/sizes/regions) based on the given offer. +* **Output:** Table: *Target Industry/Segment | Typical Company Characteristics | Region(s) | Proof of Relevance (URL)*. +* **Format Requirement:** Respond ONLY with the results for this single step. Your response must start with the heading \"## Step 2: Target Groups (Companies)\" and include the Markdown table.""", + """# Task +Now perform **Step 3 - Personas/Roles (WHO - People)**. + +# Context: Validated results from previous steps +{{previous_steps_data}} + +# Instructions for Step 3 +* For each target industry: at least 4 **specific** roles with their area of responsibility and involvement in purchasing (E, I, D, U based on RACI logic). Do **not** invent people; logically derive roles from problem/process context. +* **Output:** Table: *Role (precise) | Area of Responsibility | Why relevant for the product | Buying Involvement (E/I/D/U) | Source/Indication (URL or reference)*. +* **Format Requirement:** Respond ONLY with the results for this single step. Your response must start with the heading \"## Step 3: Personas (Roles)\".""", + """# Task +Now perform **Step 4 - Pain Points per Role (WHY)**. + +# Context: Validated results from previous steps +{{previous_steps_data}} + +# Instructions for Step 4 +* For each role, formulate 3-5 specific pain points (observations, not features). +* Tag each pain point with a category: **Cost | Time | Risk | Compliance | Quality | Employee Retention.** +* Add an **Impact Estimate (EUR, h, %)** as a hypothesis. +* **Output:** Table: *Role | Pain Point (specific, measurable/operational) | Category | Impact (Cost, Risk, Time) | Impact Estimate (EUR, h, %) | Urgency (high/medium/low) | Source/Indication (URL)*. +* **Format Requirement:** Respond ONLY with the results for this single step. Your response must start with the heading \"## Step 4: Pain Points per Role\".""", + """# Task +Now perform **Step 5 - Gains & Benefits per Role (WHY switch)**. + +# Context: Validated results from previous steps +{{previous_steps_data}} + +# Instructions for Step 5 +* Based on the identified pain points, formulate 2-3 concrete gains (advantages/benefits) for each role. +* Quantify the benefit as a hypothesis (e.g., savings in EUR, time gained in h, efficiency increase in %). +* **Output:** Table: *Role | Gain (specific benefit) | Quantification (Hypothesis in EUR, h, %) | Source/Indication (URL)*. +* **Format Requirement:** Respond ONLY with the results for this single step. Your response must start with the heading \"## Step 5: Gains & Benefits per Role\".""", + """# Task +Now perform **Step 6 - Marketing Message (HOW to speak)**. + +# Context: Validated results from previous steps +{{previous_steps_data}} + +# Inputs for this step +* **Desired channels for the message:** `{{channels}}` + +# Instructions for Step 6: Chain-of-Thought Analysis & Copywriting + +**FOCUS:** To make the analysis manageable, select the **one (1) most relevant and promising target industry** (Primary Industry) from Step 2. +Your goal is to create a specific message for EACH role from Step 3 ONLY for this ONE focus industry. + +For each **[Role]** within the selected **[Focus Industry]**, perform the following thought process: + +1. **Step 6.1 (Analysis): Product-Role Fit.** + * Which product/solution from the "Offer" table (Step 1) is most relevant for the **[Role]**? + +2. **Step 6.2 (Analysis): Industry Use Case.** + * What are 1-2 typical use cases for the selected product in the **[Focus Industry]**? What does the **[Role]** actually do with it? + +3. **Step 6.3 (Analysis): Benefit Quantification.** + * Look at the Pain Points (Step 4) and Gains (Step 5) for the **[Role]**. + * Derive a concrete KPI relevant to the **[Role]**. + +4. **Step 6.4 (Synthesis): Formulate Message.** + * Synthesize the findings from 6.1-6.3 into a concise core message (2-3 sentences) following the structure: **Observation (Problem) -> Low-threshold Solution Idea -> Product Bridge -> Quantified Benefit.** + * Create variants of this message for the channels: {{channels}}. + +# Output Format +Create ONLY the final Markdown table. +* **Table Columns:** *Focus Industry | Role | Core Message (2-3 sentences) | {{channels}}*. +* **Requirement:** Your response must start with the heading \"## Step 6: Messages\" and contain ONLY the complete Markdown table.""" +] + +PROMPTS = { + 'de': { + 'SYSTEM_PROMPT': SYSTEM_PROMPT_DE, + 'STEP_PROMPTS': STEP_PROMPTS_DE, + 'STEP_TITLES': { + 'offer': 'Schritt 1: Angebot (WAS)', + 'targetGroups': 'Schritt 2: Zielgruppen (WER - Unternehmen)', + 'personas': 'Schritt 3: Zielpersonen/Rollen (WER - Personen)', + 'painPoints': 'Schritt 4: Painpoints je Rolle (WARUM)', + 'gains': 'Schritt 5: Gains & Nutzen je Rolle (WARUM wechseln)', + 'messages': 'Schritt 6: Marketingbotschaften je Segment & Rolle (WIE sprechen)', + }, + 'SUMMARY_TITLE': 'Kurzresuemee:', + 'SUMMARY_TEXT_FOR_STEP1': [ + "Die Angebotsanalyse wurde erfolgreich auf Basis der Website-Inhalte generiert.", + "Dies ist der erste Schritt des Prozesses, der vom neuen Python-Backend ausgefuehrt wird." + ] + }, + 'en': { + 'SYSTEM_PROMPT': SYSTEM_PROMPT_EN, + 'STEP_PROMPTS': STEP_PROMPTS_EN, + 'STEP_TITLES': { + 'offer': 'Step 1: Offer (WHAT)', + 'targetGroups': 'Step 2: Target Groups (WHO - Companies)', + 'personas': 'Step 3: Personas/Roles (WHO - People)', + 'painPoints': 'Step 4: Pain Points per Role (WHY)', + 'gains': 'Step 5: Gains & Benefits per Role (WHY switch)', + 'messages': 'Step 6: Marketing Messages per Segment & Role (HOW to speak)', + }, + 'SUMMARY_TITLE': 'Summary:', + 'SUMMARY_TEXT_FOR_STEP1': [ + "The offer analysis has been successfully generated based on website content.", + "This is the first step of the process, executed by the new Python backend." + ] + } +} + +# --- API & SCRAPING HELPERS --- + +def load_api_key(): + try: + with open("gemini_api_key.txt", "r") as f: + return f.read().strip() + except FileNotFoundError: + logging.error("API key file 'gemini_api_key.txt' not found.") + return None + +def call_gemini_api(prompt, api_key, retries=3): + url = f"https://generativelanguage.googleapis.com/v1/models/gemini-2.5-flash:generateContent?key={api_key}" + headers = {'Content-Type': 'application/json'} + payload = {"contents": [{"parts": [{"text": prompt}]}]} + + for attempt in range(retries): + try: + # Increased timeout to 600s (10 minutes) for complex Step 6 generation + response = requests.post(url, headers=headers, json=payload, timeout=600) + response.raise_for_status() + result = response.json() + if 'candidates' in result and result['candidates']: + candidate = result['candidates'][0] + if 'content' in candidate and 'parts' in candidate['content']: + return candidate['content']['parts'][0]['text'] + logging.warning(f"Unexpected API response structure: {result}") + return "" + except requests.exceptions.HTTPError as e: + # Retry on server errors (500, 502, 503, 504) + if e.response.status_code in [500, 502, 503, 504] and attempt < retries - 1: + wait_time = (attempt + 1) * 5 + logging.warning(f"API Error {e.response.status_code}. Retrying in {wait_time}s...") + time.sleep(wait_time) + continue + logging.error(f"Error calling Gemini API: {e}") + raise + except Exception as e: + # Retry on connection errors + if attempt < retries - 1: + wait_time = (attempt + 1) * 5 + logging.warning(f"API Connection Error: {e}. Retrying in {wait_time}s...") + time.sleep(wait_time) + continue + logging.error(f"Final Error calling Gemini API: {e}") + raise + +def get_text_from_url(url): + try: + headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'} + response = requests.get(url, headers=headers, timeout=10) + response.raise_for_status() + soup = BeautifulSoup(response.content, 'lxml') + + # 1. Remove specific noise tags (including header/footer to avoid navigation links) + for element in soup(['script', 'style', 'noscript', 'iframe', 'svg', 'header', 'footer', 'nav', 'aside', 'form', 'button', 'meta', 'link']): + element.decompose() + + # 2. Clean attributes but keep structure and HREF + for tag in soup.find_all(True): + # We want to keep the tag (e.g.

, ), but clean attributes + current_attrs = dict(tag.attrs) + for attr in current_attrs: + # Keep 'href' for links so the LLM can extract the source URL + if tag.name == 'a' and attr == 'href': + continue + # Remove everything else (class, id, style, onclick, etc.) + del tag[attr] + + # 3. Return the HTML structure (body only if possible) + body = soup.find('body') + if body: + html_content = str(body) + else: + html_content = str(soup) + + # 4. Minimize whitespace to save tokens (remove empty lines) + lines = [line.strip() for line in html_content.split('\n') if line.strip()] + return "\n".join(lines) + + except Exception as e: + logging.warning(f"Could not fetch or read URL {url}: {e}") + return "" + +def find_relevant_links(base_url): + try: + headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'} + response = requests.get(base_url, headers=headers, timeout=10) + response.raise_for_status() + soup = BeautifulSoup(response.content, 'lxml') + base_netloc = urlparse(base_url).netloc + relevant_links = set() + for a_tag in soup.find_all('a', href=True): + href = a_tag['href'] + link_text = a_tag.get_text(strip=True).lower() + if any(keyword in href.lower() or keyword in link_text for keyword in LINK_KEYWORDS): + abs_url = urljoin(base_url, href) + if urlparse(abs_url).netloc == base_netloc: + relevant_links.add(abs_url) + return list(relevant_links)[:10] + except Exception as e: + logging.warning(f"Could not scrape base URL {base_url} for links: {e}") + return [] + +def parse_markdown_table(markdown_text): + lines = [line.strip() for line in markdown_text.strip().split('\n')] + table_lines = [] + in_table_section = False + for line in lines: + if re.match(r'^\|.*\|$', line) and '---' not in line: + in_table_section = True + table_lines.append(line) + elif in_table_section and '---' in line and re.match(r'^\|(:?-+:?)\|', line.replace(' ', '')): + table_lines.append(line) + elif in_table_section: + break + if not table_lines: return {"headers": [], "rows": []} + separator_index = -1 + for i, line in enumerate(table_lines): + if '---' in line and re.match(r'^\|(:?-+:?)\|', line.replace(' ', '')): + separator_index = i + break + if separator_index == -1 or separator_index == 0: return {"headers": [], "rows": []} + header_line = table_lines[0] + headers = [re.sub(r'\*+([^*]+)\*+', r'\1', h.strip()).strip() for h in header_line.split('|') if h.strip()] + rows = [] + for line in table_lines[separator_index + 1:]: + raw_cells = line.split('|') + cells = [re.sub(r'\*+([^*]+)\*+', r'\1', c.strip()).strip() for c in raw_cells if c.strip()] + if len(cells) == len(headers): + rows.append(cells) + return {"headers": headers, "rows": rows} + +def format_context_for_prompt(analysis_data, language): + context = "" + current_prompts = PROMPTS[language] + step_titles = current_prompts['STEP_TITLES'] + step_keys = ['offer', 'targetGroups', 'personas', 'painPoints', 'gains', 'messages'] + for i, step_key in enumerate(step_keys): + step_data = analysis_data.get(step_key) + if step_data: + title = step_titles.get(step_key, f"Step {i+1}") + context += f"\n\n## {title}\n\n" + summary = step_data.get('summary') + if summary: + context += f"**{current_prompts['SUMMARY_TITLE']}**\n" + context += "\n".join([f"* {s}" for s in summary]) + "\n\n" + headers, rows = step_data.get('headers', []), step_data.get('rows', []) + if headers and rows: + context += f"| {' | '.join(headers)} |\n| {' | '.join(['---']*len(headers))} |\n" + for row in rows: + padded_row = row + [''] * (len(headers) - len(row)) + context += f"| {' | '.join(padded_row)} |\n" + context += "\n" + return context + +# --- CORE LOGIC --- + +def start_generation(url, language, regions, focus): + logging.info(f"Starting Step 1 for URL: {url} in language: {language}") + api_key = load_api_key() + if not api_key: raise ValueError("Gemini API key is missing.") + + # 1. Scraping Strategy: Main Page + Relevant Sub-pages + urls_to_scrape = sorted(list(set([url] + find_relevant_links(url)))) + grounding_text = "" + logging.info(f"Identified {len(urls_to_scrape)} pages to scrape.") + + for u in urls_to_scrape: + logging.info(f" - Scraping: {u}") + text_content = get_text_from_url(u) + if text_content: + # Inject SOURCE_URL marker for the LLM + grounding_text += f"SOURCE_URL: {u}\nCONTENT (Simplified HTML):\n{text_content}\n\n{'='*50}\n\n" + + if not grounding_text.strip(): raise RuntimeError(f"Failed to scrape content from {url}") + + current_prompts = PROMPTS[language] + system_instruction = current_prompts['SYSTEM_PROMPT'].replace('{{language}}', language) + + # Updated Prompt: Removed length limit and added instruction for SOURCE_URL + grounded_offer_prompt = f"{system_instruction}\n\n# TASK\nAnalyze the provided website content to understand the company's offerings. Your response MUST be a Markdown table.\n\n# CONTEXT\n- Website Content: The input provided is **Simplified HTML**. Use the structure (e.g.

-

headers,