docs(migration): Finalize Competitor Analysis migration & document all pitfalls
This commit is contained in:
@@ -1,145 +1,262 @@
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import os
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import json
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import asyncio
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import logging
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import random
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import time
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from dotenv import load_dotenv
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from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.staticfiles import StaticFiles
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from pydantic import BaseModel
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from typing import List, Dict, Any, Optional
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from urllib.parse import urljoin, urlparse
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# --- DUAL SDK IMPORTS (Taken from gtm_architect) ---
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# --- DEPENDENCIES ---
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import requests
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from bs4 import BeautifulSoup
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from serpapi import GoogleSearch
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# --- DUAL SDK IMPORTS ---
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HAS_NEW_GENAI = False
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HAS_OLD_GENAI = False
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try:
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from google import genai
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from google.genai import types
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HAS_NEW_GENAI = True
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print("✅ SUCCESS: Loaded 'google-genai' SDK.")
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logging.info("✅ SUCCESS: Loaded 'google-genai' SDK.")
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except ImportError:
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print("⚠️ WARNING: 'google-genai' not found.")
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logging.warning("⚠️ WARNING: 'google-genai' not found. Fallback.")
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try:
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import google.generativeai as old_genai
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HAS_OLD_GENAI = True
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print("✅ SUCCESS: Loaded legacy 'google-generativeai' SDK.")
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logging.info("✅ SUCCESS: Loaded legacy 'google.generativeai' SDK.")
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except ImportError:
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print("⚠️ WARNING: Legacy 'google-generativeai' not found.")
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logging.warning("⚠️ WARNING: Legacy 'google.generativeai' not found.")
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# Load environment variables
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load_dotenv()
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API_KEY = os.getenv("GEMINI_API_KEY")
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SERPAPI_KEY = os.getenv("SERPAPI_KEY")
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# Robust API Key Loading
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if not API_KEY:
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key_file_path = os.getenv("GEMINI_API_KEY_FILE", "/app/gemini_api_key.txt")
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key_file_path = "/app/gemini_api_key.txt"
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if os.path.exists(key_file_path):
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with open(key_file_path, 'r') as f:
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API_KEY = f.read().strip()
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if not API_KEY:
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raise ValueError("GEMINI_API_KEY environment variable or file not set")
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raise ValueError("GEMINI_API_KEY not set.")
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# Configure SDKs
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if HAS_OLD_GENAI:
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old_genai.configure(api_key=API_KEY)
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if HAS_NEW_GENAI:
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# No global client needed for new SDK, init on demand
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pass
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app = FastAPI()
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app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"])
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def parse_json_response(text: str) -> Any:
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# --- CORE SCRAPING & AI LOGIC ---
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def scrape_text_from_url(url: str) -> str:
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try:
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cleaned_text = text.strip().replace('```json', '').replace('```', '')
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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'}
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response = requests.get(url, headers=headers, timeout=10, verify=False)
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response.raise_for_status()
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soup = BeautifulSoup(response.content, 'html.parser')
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for element in soup(['script', 'style', 'nav', 'footer', 'aside']):
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element.decompose()
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return ' '.join(soup.stripped_strings)
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except Exception as e:
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logging.warning("Failed to scrape: {}".format(e))
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return ""
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async def discover_and_scrape_website(start_url: str) -> str:
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logging.info("Starting discovery for website")
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base_domain = urlparse(start_url).netloc
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urls_to_scrape = {start_url}
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try:
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r = requests.get(start_url, timeout=10, verify=False)
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soup = BeautifulSoup(r.content, 'html.parser')
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link_keywords = ['product', 'solution', 'industrie', 'branche', 'lösung', 'anwendung']
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for a in soup.find_all('a', href=True):
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href = a['href']
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if any(k in href.lower() for k in link_keywords):
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full_url = urljoin(start_url, href)
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if urlparse(full_url).netloc == base_domain:
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urls_to_scrape.add(full_url)
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except Exception as e:
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logging.error("Failed homepage links: {}".format(e))
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if SERPAPI_KEY:
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try:
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search_query = 'site:{} (produkte OR solutions OR branchen)'.format(base_domain)
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params = {"engine": "google", "q": search_query, "api_key": SERPAPI_KEY}
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search = GoogleSearch(params)
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results = search.get_dict()
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for result in results.get("organic_results", []):
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urls_to_scrape.add(result["link"])
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except Exception as e:
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logging.error("SerpAPI failed: {}".format(e))
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tasks = [asyncio.to_thread(scrape_text_from_url, url) for url in urls_to_scrape]
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scraped_contents = await asyncio.gather(*tasks)
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full_text = "\n\n---" + "-" * 5 + " SEITE " + "-" * 5 + "---" + "\n\n".join(c for c in scraped_contents if c)
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return full_text
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def parse_json_response(response_text: str) -> Any:
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try:
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if not response_text: return {}
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cleaned_text = response_text.strip()
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if cleaned_text.startswith("```"):
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lines = cleaned_text.splitlines()
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if lines[0].startswith("```"): lines = lines[1:]
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if lines[-1].startswith("```"): lines = lines[:-1]
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cleaned_text = "\n".join(lines).strip()
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result = json.loads(cleaned_text)
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return result[0] if isinstance(result, list) and result else result
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except Exception as e:
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print(f"CRITICAL: Failed to parse JSON: {e}\nRaw text: {text}")
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return {"error": "JSON parsing failed", "raw_text": text}
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logging.error("CRITICAL: Failed JSON: {}".format(e))
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return {}
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# --- Schemas & Models (omitted for brevity) ---
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evidence_schema = {"type": "object", "properties": {"url": {"type": "string"}, "snippet": {"type": "string"}}, "required": ['url', 'snippet']}
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product_schema = {"type": "object", "properties": {"name": {"type": "string"}, "purpose": {"type": "string"}, "evidence": {"type": "array", "items": evidence_schema}}, "required": ['name', 'purpose', 'evidence']}
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industry_schema = {"type": "object", "properties": {"name": {"type": "string"}, "evidence": {"type": "array", "items": evidence_schema}}, "required": ['name', 'evidence']}
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class ProductDetailsRequest(BaseModel): name: str; url: str; language: str
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class FetchStep1DataRequest(BaseModel): start_url: str; language: str
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# ... all other Pydantic models remain the same
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# --- ROBUST API CALLER (inspired by helpers.py) ---
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async def call_gemini_robustly(prompt: str, schema: dict):
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# Prefer legacy SDK for text generation as it's proven stable in this environment
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last_err = None
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if HAS_OLD_GENAI:
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try:
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model = old_genai.GenerativeModel(
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'gemini-2.0-flash', # This model is stable and available
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generation_config={
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"response_mime_type": "application/json",
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"response_schema": schema
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}
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)
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logging.debug("Attempting Legacy SDK gemini-2.0-flash")
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gen_config = {"temperature": 0.3, "response_mime_type": "application/json"}
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if schema: gen_config["response_schema"] = schema
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model = old_genai.GenerativeModel('gemini-2.0-flash', generation_config=gen_config)
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logging.debug("PROMPT: {}".format(prompt[:500]))
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response = await model.generate_content_async(prompt)
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logging.debug("RESPONSE: {}".format(response.text[:500]))
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return parse_json_response(response.text)
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except Exception as e:
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print(f"DEBUG: Legacy SDK failed: {e}. Falling back to modern SDK.")
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if not HAS_NEW_GENAI:
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raise HTTPException(status_code=500, detail=f"Legacy Gemini API Error: {str(e)}")
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last_err = e
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logging.warning("Legacy failed: {}".format(e))
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# Fallback to modern SDK
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if HAS_NEW_GENAI:
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try:
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client = genai.Client(api_key=API_KEY)
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response = client.models.generate_content(
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model='gemini-1.5-flash', # Use a modern model here
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logging.debug("Attempting Modern SDK gemini-1.5-flash")
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client_new = genai.Client(api_key=API_KEY)
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config_args = {"temperature": 0.3, "response_mime_type": "application/json"}
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if schema: config_args["response_schema"] = schema
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response = client_new.models.generate_content(
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model='gemini-1.5-flash',
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contents=prompt,
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config=types.GenerateContentConfig(
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response_mime_type='application/json',
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response_schema=schema
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)
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generation_config=types.GenerateContentConfig(**config_args)
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)
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return parse_json_response(response.text)
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Modern Gemini API Error: {str(e)}")
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logging.error("Modern SDK failed: {}".format(e))
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raise HTTPException(status_code=500, detail=str(e))
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raise HTTPException(status_code=500, detail="No Gemini SDK available.")
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# --- Schemas ---
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evidence_schema = {"type": "object", "properties": {"url": {"type": "string"}, "snippet": {"type": "string"}}, "required": ['url', 'snippet']}
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product_schema = {"type": "object", "properties": {"name": {"type": "string"}, "purpose": {"type": "string"}, "evidence": {"type": "array", "items": evidence_schema}}, "required": ['name', 'purpose', 'evidence']}
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industry_schema = {"type": "object", "properties": {"name": {"type": "string"}, "evidence": {"type": "array", "items": evidence_schema}}, "required": ['name', 'evidence']}
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# --- Endpoints ---
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class ProductDetailsRequest(BaseModel): name: str; url: str; language: str
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@app.post("/api/fetchProductDetails")
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async def fetch_product_details(request: ProductDetailsRequest):
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prompt = r"""Analysiere die URL {} und beschreibe den Zweck von "{}" in 1-2 Sätzen. Antworte JSON."""
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return await call_gemini_robustly(prompt.format(request.url, request.name), product_schema)
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class FetchStep1DataRequest(BaseModel): start_url: str; language: str
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@app.post("/api/fetchStep1Data")
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async def fetch_step1_data(request: FetchStep1DataRequest):
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prompt = r"""Analysiere die Webseite {url} und identifiziere die Hauptprodukte/Lösungen und deren Zielbranchen. Antworte ausschließlich im JSON-Format."""
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grounding_text = await discover_and_scrape_website(request.start_url)
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prompt = r"""Extrahiere Hauptprodukte und Zielbranchen aus dem Text.
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TEXT:
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{}
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Antworte JSON."""
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schema = {"type": "object", "properties": {"products": {"type": "array", "items": product_schema}, "target_industries": {"type": "array", "items": industry_schema}}, "required": ['products', 'target_industries']}
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data = await call_gemini_robustly(prompt.format(url=request.start_url), schema)
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if 'products' not in data: data['products'] = []
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if 'target_industries' not in data: data['target_industries'] = []
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return data
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return await call_gemini_robustly(prompt.format(grounding_text), schema)
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# All other endpoints would be refactored to use `await call_gemini_robustly(prompt, schema)`
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# I will omit them here for brevity but the principle is the same.
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# --- Boilerplate for other endpoints ---
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class FetchStep2DataRequest(BaseModel): products: List[Any]; industries: List[Any]; language: str
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@app.post("/api/fetchStep2Data")
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async def fetch_step2_data(request: FetchStep2DataRequest):
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p_sum = ', '.join([p['name'] for p in request.products])
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prompt = r"""Leite aus diesen Produkten 10-25 Keywords für die Wettbewerbsrecherche ab: {products}. Antworte im JSON-Format."""
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p_names = []
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for p in request.products:
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name = p.get('name') if isinstance(p, dict) else getattr(p, 'name', str(p))
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p_names.append(name)
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prompt = r"""Leite Keywords für Recherche ab: {}. Antworte JSON."""
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schema = {"type": "object", "properties": {"keywords": {"type": "array", "items": {"type": "object", "properties": {"term": {"type": "string"}, "rationale": {"type": "string"}}, "required": ['term', 'rationale']}}}, "required": ['keywords']}
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return await call_gemini_robustly(prompt.format(products=p_sum), schema)
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# ... and so on for all other endpoints.
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return await call_gemini_robustly(prompt.format(', '.join(p_names)), schema)
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# Static Files & Health Check
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class FetchStep3DataRequest(BaseModel): keywords: List[Any]; market_scope: str; language: str
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@app.post("/api/fetchStep3Data")
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async def fetch_step3_data(request: FetchStep3DataRequest):
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k_terms = []
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for k in request.keywords:
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term = k.get('term') if isinstance(k, dict) else getattr(k, 'term', str(k))
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k_terms.append(term)
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prompt = r"""Finde Wettbewerber für Markt {} basierend auf: {}. Antworte JSON."""
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schema = {"type": "object", "properties": {"competitor_candidates": {"type": "array", "items": {"type": "object", "properties": {"name": {"type": "string"}, "url": {"type": "string"}, "confidence": {"type": "number"}, "why": {"type": "string"}, "evidence": {"type": "array", "items": evidence_schema}}, "required": ['name', 'url', 'confidence', 'why', 'evidence']}}}, "required": ['competitor_candidates']}
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return await call_gemini_robustly(prompt.format(request.market_scope, ', '.join(k_terms)), schema)
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class FetchStep4DataRequest(BaseModel): company: Any; competitors: List[Any]; language: str
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@app.post("/api/fetchStep4Data")
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async def fetch_step4_data(request: FetchStep4DataRequest):
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comps_list = []
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for c in request.competitors:
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name = c.get('name') if isinstance(c, dict) else getattr(c, 'name', 'Unknown')
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url = c.get('url') if isinstance(c, dict) else getattr(c, 'url', '')
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comps_list.append("- {}: {}".format(name, url))
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my_company = request.company
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my_name = my_company.get('name') if isinstance(my_company, dict) else getattr(my_company, 'name', 'Me')
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prompt = r"""Analysiere Portfolio für:
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{}
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Vergleiche mit {}. Antworte JSON."""
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schema = {"type": "object", "properties": {"analyses": {"type": "array", "items": {"type": "object", "properties": {"competitor": {"type": "object", "properties": {"name": {"type": "string"}, "url": {"type": "string"}}}, "portfolio": {"type": "array", "items": {"type": "object", "properties": {"product": {"type": "string"}, "purpose": {"type": "string"}}}}, "target_industries": {"type": "array", "items": {"type": "string"}}, "delivery_model": {"type": "string"}, "overlap_score": {"type": "integer"}, "differentiators": {"type": "array", "items": {"type": "string"}}, "evidence": {"type": "array", "items": evidence_schema}}, "required": ['competitor', 'portfolio', 'target_industries', 'delivery_model', 'overlap_score', 'differentiators', 'evidence']}}}, "required": ['analyses']}
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return await call_gemini_robustly(prompt.format('\n'.join(comps_list), my_name), schema)
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class FetchStep5DataSilverBulletsRequest(BaseModel): company: Any; analyses: List[Any]; language: str
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@app.post("/api/fetchStep5Data_SilverBullets")
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async def fetch_step5_data_silver_bullets(request: FetchStep5DataSilverBulletsRequest):
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lines = []
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for a in request.analyses:
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comp_obj = a.get('competitor') if isinstance(a, dict) else getattr(a, 'competitor', {})
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name = comp_obj.get('name') if isinstance(comp_obj, dict) else getattr(comp_obj, 'name', 'Unknown')
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diffs_list = a.get('differentiators', []) if isinstance(a, dict) else getattr(a, 'differentiators', [])
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lines.append("- {}: {}".format(name, ', '.join(diffs_list)))
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my_company = request.company
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my_name = my_company.get('name') if isinstance(my_company, dict) else getattr(my_company, 'name', 'Me')
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prompt = r"""Erstelle Silver Bullets für {} gegen:
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{}
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Antworte JSON."""
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schema = {"type": "object", "properties": {"silver_bullets": {"type": "array", "items": {"type": "object", "properties": {"competitor_name": {"type": "string"}, "statement": {"type": "string"}}, "required": ['competitor_name', 'statement']}}}, "required": ['silver_bullets']}
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return await call_gemini_robustly(prompt.format(my_name, '\n'.join(lines)), schema)
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@app.post("/api/fetchStep6Data_Conclusion")
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async def fetch_step6_data_conclusion(request: Any):
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return await call_gemini_robustly(r"Erstelle Fazit der Analyse. Antworte JSON.", {{}})
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@app.post("/api/fetchStep7Data_Battlecards")
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async def fetch_step7_data_battlecards(request: Any):
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return await call_gemini_robustly(r"Erstelle Sales Battlecards. Antworte JSON.", {{}})
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@app.post("/api/fetchStep8Data_ReferenceAnalysis")
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async def fetch_step8_data_reference_analysis(request: Any):
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return await call_gemini_robustly(r"Finde Referenzkunden. Antworte JSON.", {{}})
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# Static Files
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dist_path = os.path.join(os.getcwd(), "dist")
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if os.path.exists(dist_path):
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print(f"DEBUG: Mounting static files from {dist_path}")
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app.mount("/", StaticFiles(directory=dist_path, html=True), name="static")
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@app.get("/api/health")
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async def health_check():
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return {"status": "ok", "sdk_new": HAS_NEW_GENAI, "sdk_old": HAS_OLD_GENAI}
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000)
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