252 lines
14 KiB
Python
252 lines
14 KiB
Python
import os
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import json
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import asyncio
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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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# Modern SDK only
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from google import genai
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from google.genai import types
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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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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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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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# Initialize the modern client
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client = genai.Client(api_key=API_KEY)
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MODEL_CANDIDATES = ['gemini-1.5-flash', 'gemini-1.5-pro'] # Directly set to a modern, fast model
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print(f"DEBUG: Initialized with MODEL_NAME={MODEL_NAME}")
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app = FastAPI()
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# Configure CORS
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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def parse_json_response(response) -> Any:
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"""Parses JSON response from the modern SDK robustly."""
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try:
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text = response.text
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if not text:
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return {} # Return empty dict on empty response
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cleaned_text = 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("```"):
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lines = lines[1:]
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if lines[-1].startswith("```"):
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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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if isinstance(result, list) and result:
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return result[0]
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return result
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except Exception as e:
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print(f"CRITICAL: Failed to parse JSON: {e}\nRaw text: {getattr(response, 'text', 'NO TEXT')}")
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return {} # Return empty dict to avoid frontend crash
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# --- Schemas (Native Python Dictionaries) ---
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evidence_schema = {
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"type": "object",
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"properties": {
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"url": {"type": "string"},
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"snippet": {"type": "string"},
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},
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"required": ['url', 'snippet']
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}
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product_schema = {
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"purpose": {"type": "string"},
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"evidence": {"type": "array", "items": evidence_schema},
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},
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"required": ['name', 'purpose', 'evidence']
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}
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industry_schema = {
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"evidence": {"type": "array", "items": evidence_schema},
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},
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"required": ['name', 'evidence']
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}
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# --- Request Models ---
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class ProductDetailsRequest(BaseModel):
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name: str; url: str; language: str
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class FetchStep1DataRequest(BaseModel):
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start_url: str; language: str
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class ProductModel(BaseModel):
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name: str; purpose: str; evidence: List[Dict[str, str]]
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class TargetIndustryModel(BaseModel):
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name: str; evidence: List[Dict[str, str]]
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class FetchStep2DataRequest(BaseModel):
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products: List[ProductModel]; industries: List[TargetIndustryModel]; language: str
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class KeywordModel(BaseModel):
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term: str; rationale: str
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class FetchStep3DataRequest(BaseModel):
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keywords: List[KeywordModel]; market_scope: str; language: str
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class CompanyModel(BaseModel):
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name: str; start_url: str
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class CompetitorCandidateModel(BaseModel):
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name: str; url: str; confidence: float; why: str; evidence: List[Dict[str, str]]
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class FetchStep4DataRequest(BaseModel):
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company: CompanyModel; competitors: List[CompetitorCandidateModel]; language: str
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class AnalysisModel(BaseModel):
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competitor: Dict[str, str]; portfolio: List[Dict[str, str]]; target_industries: List[str]
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delivery_model: str; overlap_score: int; differentiators: List[str]; evidence: List[Dict[str, str]]
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class FetchStep5DataSilverBulletsRequest(BaseModel):
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company: CompanyModel; analyses: List[AnalysisModel]; language: str
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class SilverBulletModel(BaseModel):
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competitor_name: str; statement: str
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class FetchStep6DataConclusionRequest(BaseModel):
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company: CompanyModel; products: List[ProductModel]; industries: List[TargetIndustryModel]
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analyses: List[AnalysisModel]; silver_bullets: List[SilverBulletModel]; language: str
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class FetchStep7DataBattlecardsRequest(BaseModel):
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company: CompanyModel; analyses: List[AnalysisModel]; silver_bullets: List[SilverBulletModel]; language: str
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class ShortlistedCompetitorModel(BaseModel):
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name: str; url: str
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class FetchStep8DataReferenceAnalysisRequest(BaseModel):
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competitors: List[ShortlistedCompetitorModel]; language: str
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# --- API Helper ---
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async def call_gemini_json(prompt: str, schema: dict):
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"""Calls Gemini with schema enforcement."""
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last_err = None
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for model_name in MODEL_CANDIDATES:
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try:
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config_args = {"response_mime_type": "application/json"}
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if schema:
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config_args["response_schema"] = schema
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response = client.models.generate_content(
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model=model_name,
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contents=prompt,
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config=types.GenerateContentConfig(**config_args)
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)
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return parse_json_response(response)
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except Exception as e:
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last_err = e
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print(f"DEBUG: Model {model_name} failed: {e}")
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if "404" in str(e) or "not supported" in str(e).lower():
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continue
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break
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raise HTTPException(status_code=500, detail=f"Gemini API Error: {str(last_err)}")
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# --- Endpoints ---
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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 {url} und beschreibe den Zweck von "{name}" in 1-2 Sätzen. Antworte ausschließlich im JSON-Format."""
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return await call_gemini_json(prompt.format(url=request.url, name=request.name), product_schema)
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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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schema = {
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"type": "object",
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"properties": {
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"products": {"type": "array", "items": product_schema},
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"target_industries": {"type": "array", "items": industry_schema},
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},
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"required": ['products', 'target_industries']
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}
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data = await call_gemini_json(prompt.format(url=request.start_url), schema)
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# Double check keys for frontend compatibility
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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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@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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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_json(prompt.format(products=p_sum), schema)
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@app.post("/api/fetchStep3Data")
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async def fetch_step3_data(request: FetchStep3DataRequest):
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k_sum = ', '.join([k.term for k in request.keywords])
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prompt = r"""Finde Wettbewerber für Markt {scope} basierend auf: {keywords}. 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_json(prompt.format(scope=request.market_scope, keywords=k_sum), schema)
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@app.post("/api/fetchStep4Data")
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async def fetch_step4_data(request: FetchStep4DataRequest):
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c_sum = '\n'.join([f'- {c.name}: {c.url}' for c in request.competitors])
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prompt = r"""Analysiere Portfolio & Positionierung für:\n{comps}\nVergleiche mit {me}. 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_json(prompt.format(comps=c_sum, me=request.company.name), schema)
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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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c_sum = '\n'.join([f"- {a.competitor['name']}: {'; '.join(a.differentiators)}" for a in request.analyses])
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prompt = r"""Erstelle prägnante Silver Bullets für {me} gegen diese Wettbewerber:\n{comps}\nAntworte 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_json(prompt.format(me=request.company.name, comps=c_sum), schema)
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@app.post("/api/fetchStep6Data_Conclusion")
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async def fetch_step6_data_conclusion(request: FetchStep6DataConclusionRequest):
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prompt = r"""Erstelle ein abschließendes Fazit der Wettbewerbsanalyse für {me}. Antworte JSON."""
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schema = {"type": "object", "properties": {"conclusion": {"type": "object", "properties": {"product_matrix": {"type": "array", "items": {"type": "object", "properties": {"product": {"type": "string"}, "availability": {"type": "array", "items": {"type": "object", "properties": {"competitor": {"type": "string"}, "has_offering": {"type": "boolean"}}, "required": ['competitor', 'has_offering']}}}, "required": ['product', 'availability']}}, "industry_matrix": {"type": "array", "items": {"type": "object", "properties": {"industry": {"type": "string"}, "availability": {"type": "array", "items": {"type": "object", "properties": {"competitor": {"type": "string"}, "has_offering": {"type": "boolean"}}, "required": ['competitor', 'has_offering']}}}, "required": ['industry', 'availability']}}, "overlap_scores": {"type": "array", "items": {"type": "object", "properties": {"competitor": {"type": "string"}, "score": {"type": "number"}}}}, "summary": {"type": "string"}, "opportunities": {"type": "string"}, "next_questions": {"type": "array", "items": {"type": "string"}}}, "required": ['product_matrix', 'industry_matrix', 'overlap_scores', 'summary', 'opportunities', 'next_questions']}}, "required": ['conclusion']}
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return await call_gemini_json(prompt.format(me=request.company.name), schema)
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@app.post("/api/fetchStep7Data_Battlecards")
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async def fetch_step7_data_battlecards(request: FetchStep7DataBattlecardsRequest):
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prompt = r"""Erstelle Sales Battlecards für {me} gegen seine Wettbewerber. Antworte JSON."""
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schema = {"type": "object", "properties": {"battlecards": {"type": "array", "items": {"type": "object", "properties": {"competitor_name": {"type": "string"}, "competitor_profile": {"type": "object", "properties": {"focus": {"type": "string"}, "positioning": {"type": "string"}}, "required": ['focus', 'positioning']}, "strengths_vs_weaknesses": {"type": "array", "items": {"type": "string"}}, "landmine_questions": {"type": "array", "items": {"type": "string"}}, "silver_bullet": {"type": "string"}}, "required": ['competitor_name', 'competitor_profile', 'strengths_vs_weaknesses', 'landmine_questions', 'silver_bullet']}}}, "required": ['battlecards']}
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return await call_gemini_json(prompt.format(me=request.company.name), schema)
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@app.post("/api/fetchStep8Data_ReferenceAnalysis")
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async def fetch_step8_data_reference_analysis(request: FetchStep8DataReferenceAnalysisRequest):
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c_sum = '\n'.join([f'- {c.name}: {c.url}' for c in request.competitors])
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prompt = r"""Finde offizielle Referenzkunden für diese Wettbewerber:\n{comps}\nAntworte JSON."""
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schema = {"type": "object", "properties": {"reference_analysis": {"type": "array", "items": {"type": "object", "properties": {"competitor_name": {"type": "string"}, "references": {"type": "array", "items": {"type": "object", "properties": {"name": {"type": "string"}, "industry": {"type": "string"}, "testimonial_snippet": {"type": "string"}, "case_study_url": {"type": "string"}}, "required": ["name", "industry", "testimonial_snippet", "case_study_url"]}}}, "required": ["competitor_name", "references"]}}}, "required": ["reference_analysis"]}
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# IMPORTANT: The new SDK supports tools via a list in config, not directly as args to generate_content.
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response = client.models.generate_content(
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model=MODEL_NAME,
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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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tools=[types.Tool(google_search_retrieval={})]
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)
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)
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return parse_json_response(response)
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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": "modern-genai", "model": MODEL_NAME}
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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) |