import os
import json
import asyncio
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel
from typing import List, Dict, Any, Optional

# Modern SDK only
from google import genai
from google.genai import types

# Load environment variables
load_dotenv()
API_KEY = os.getenv("GEMINI_API_KEY")

if not API_KEY:
    key_file_path = os.getenv("GEMINI_API_KEY_FILE", "/app/gemini_api_key.txt")
    if os.path.exists(key_file_path):
        with open(key_file_path, 'r') as f:
            API_KEY = f.read().strip()

if not API_KEY:
    raise ValueError("GEMINI_API_KEY environment variable or file not set")

# Initialize the modern client
client = genai.Client(api_key=API_KEY)
MODEL_CANDIDATES = ['gemini-1.5-flash', 'gemini-1.5-pro'] # Directly set to a modern, fast model

print(f"DEBUG: Initialized with MODEL_NAME={MODEL_NAME}")

app = FastAPI()

# Configure CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

def parse_json_response(response) -> Any:
    """Parses JSON response from the modern SDK robustly."""
    try:
        text = response.text
        if not text:
            return {} # Return empty dict on empty response
            
        cleaned_text = text.strip()
        if cleaned_text.startswith("```"):
            lines = cleaned_text.splitlines()
            if lines[0].startswith("```"):
                lines = lines[1:]
            if lines[-1].startswith("```"):
                lines = lines[:-1]
            cleaned_text = "\n".join(lines).strip()
            
        result = json.loads(cleaned_text)
        if isinstance(result, list) and result:
            return result[0]
        return result
    except Exception as e:
        print(f"CRITICAL: Failed to parse JSON: {e}\nRaw text: {getattr(response, 'text', 'NO TEXT')}")
        return {} # Return empty dict to avoid frontend crash

# --- Schemas (Native Python Dictionaries) ---
evidence_schema = {
    "type": "object",
    "properties": {
        "url": {"type": "string"},
        "snippet": {"type": "string"},
    },
    "required": ['url', 'snippet']
}

product_schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "purpose": {"type": "string"},
        "evidence": {"type": "array", "items": evidence_schema},
    },
    "required": ['name', 'purpose', 'evidence']
}

industry_schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "evidence": {"type": "array", "items": evidence_schema},
    },
    "required": ['name', 'evidence']
}

# --- Request Models ---
class ProductDetailsRequest(BaseModel):
    name: str; url: str; language: str
class FetchStep1DataRequest(BaseModel):
    start_url: str; language: str
class ProductModel(BaseModel):
    name: str; purpose: str; evidence: List[Dict[str, str]]
class TargetIndustryModel(BaseModel):
    name: str; evidence: List[Dict[str, str]]
class FetchStep2DataRequest(BaseModel):
    products: List[ProductModel]; industries: List[TargetIndustryModel]; language: str
class KeywordModel(BaseModel):
    term: str; rationale: str
class FetchStep3DataRequest(BaseModel):
    keywords: List[KeywordModel]; market_scope: str; language: str
class CompanyModel(BaseModel):
    name: str; start_url: str
class CompetitorCandidateModel(BaseModel):
    name: str; url: str; confidence: float; why: str; evidence: List[Dict[str, str]]
class FetchStep4DataRequest(BaseModel):
    company: CompanyModel; competitors: List[CompetitorCandidateModel]; language: str
class AnalysisModel(BaseModel):
    competitor: Dict[str, str]; portfolio: List[Dict[str, str]]; target_industries: List[str]
    delivery_model: str; overlap_score: int; differentiators: List[str]; evidence: List[Dict[str, str]]
class FetchStep5DataSilverBulletsRequest(BaseModel):
    company: CompanyModel; analyses: List[AnalysisModel]; language: str
class SilverBulletModel(BaseModel):
    competitor_name: str; statement: str
class FetchStep6DataConclusionRequest(BaseModel):
    company: CompanyModel; products: List[ProductModel]; industries: List[TargetIndustryModel]
    analyses: List[AnalysisModel]; silver_bullets: List[SilverBulletModel]; language: str
class FetchStep7DataBattlecardsRequest(BaseModel):
    company: CompanyModel; analyses: List[AnalysisModel]; silver_bullets: List[SilverBulletModel]; language: str
class ShortlistedCompetitorModel(BaseModel):
    name: str; url: str
class FetchStep8DataReferenceAnalysisRequest(BaseModel):
    competitors: List[ShortlistedCompetitorModel]; language: str

# --- API Helper ---
async def call_gemini_json(prompt: str, schema: dict):
    """Calls Gemini with schema enforcement."""
    last_err = None
    for model_name in MODEL_CANDIDATES:
        try:
            config_args = {"response_mime_type": "application/json"}
            if schema:
                config_args["response_schema"] = schema

            response = client.models.generate_content(
                model=model_name,
                contents=prompt,
                config=types.GenerateContentConfig(**config_args)
            )
            return parse_json_response(response)
        except Exception as e:
            last_err = e
            print(f"DEBUG: Model {model_name} failed: {e}")
            if "404" in str(e) or "not supported" in str(e).lower():
                continue
            break
    raise HTTPException(status_code=500, detail=f"Gemini API Error: {str(last_err)}")

# --- Endpoints ---

@app.post("/api/fetchProductDetails")
async def fetch_product_details(request: ProductDetailsRequest):
    prompt = r"""Analysiere {url} und beschreibe den Zweck von "{name}" in 1-2 Sätzen. Antworte ausschließlich im JSON-Format."""
    return await call_gemini_json(prompt.format(url=request.url, name=request.name), product_schema)

@app.post("/api/fetchStep1Data")
async def fetch_step1_data(request: FetchStep1DataRequest):
    prompt = r"""Analysiere die Webseite {url} und identifiziere die Hauptprodukte/Lösungen und deren Zielbranchen. Antworte ausschließlich im JSON-Format."""
    schema = {
        "type": "object",
        "properties": {
            "products": {"type": "array", "items": product_schema},
            "target_industries": {"type": "array", "items": industry_schema},
        },
        "required": ['products', 'target_industries']
    }
    data = await call_gemini_json(prompt.format(url=request.start_url), schema)
    # Double check keys for frontend compatibility
    if 'products' not in data: data['products'] = []
    if 'target_industries' not in data: data['target_industries'] = []
    return data

@app.post("/api/fetchStep2Data")
async def fetch_step2_data(request: FetchStep2DataRequest):
    p_sum = ', '.join([p.name for p in request.products])
    prompt = r"""Leite aus diesen Produkten 10-25 Keywords für die Wettbewerbsrecherche ab: {products}. Antworte im JSON-Format."""
    schema = {"type": "object", "properties": {"keywords": {"type": "array", "items": {"type": "object", "properties": {"term": {"type": "string"}, "rationale": {"type": "string"}}, "required": ['term', 'rationale']}}}, "required": ['keywords']}
    return await call_gemini_json(prompt.format(products=p_sum), schema)

@app.post("/api/fetchStep3Data")
async def fetch_step3_data(request: FetchStep3DataRequest):
    k_sum = ', '.join([k.term for k in request.keywords])
    prompt = r"""Finde Wettbewerber für Markt {scope} basierend auf: {keywords}. Antworte JSON."""
    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']}
    return await call_gemini_json(prompt.format(scope=request.market_scope, keywords=k_sum), schema)

@app.post("/api/fetchStep4Data")
async def fetch_step4_data(request: FetchStep4DataRequest):
    c_sum = '\n'.join([f'- {c.name}: {c.url}' for c in request.competitors])
    prompt = r"""Analysiere Portfolio & Positionierung für:\n{comps}\nVergleiche mit {me}. Antworte JSON."""
    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']}
    return await call_gemini_json(prompt.format(comps=c_sum, me=request.company.name), schema)

@app.post("/api/fetchStep5Data_SilverBullets")
async def fetch_step5_data_silver_bullets(request: FetchStep5DataSilverBulletsRequest):
    c_sum = '\n'.join([f"- {a.competitor['name']}: {'; '.join(a.differentiators)}" for a in request.analyses])
    prompt = r"""Erstelle prägnante Silver Bullets für {me} gegen diese Wettbewerber:\n{comps}\nAntworte JSON."""
    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']}
    return await call_gemini_json(prompt.format(me=request.company.name, comps=c_sum), schema)

@app.post("/api/fetchStep6Data_Conclusion")
async def fetch_step6_data_conclusion(request: FetchStep6DataConclusionRequest):
    prompt = r"""Erstelle ein abschließendes Fazit der Wettbewerbsanalyse für {me}. Antworte JSON."""
    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']}
    return await call_gemini_json(prompt.format(me=request.company.name), schema)

@app.post("/api/fetchStep7Data_Battlecards")
async def fetch_step7_data_battlecards(request: FetchStep7DataBattlecardsRequest):
    prompt = r"""Erstelle Sales Battlecards für {me} gegen seine Wettbewerber. Antworte JSON."""
    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']}
    return await call_gemini_json(prompt.format(me=request.company.name), schema)

@app.post("/api/fetchStep8Data_ReferenceAnalysis")
async def fetch_step8_data_reference_analysis(request: FetchStep8DataReferenceAnalysisRequest):
    c_sum = '\n'.join([f'- {c.name}: {c.url}' for c in request.competitors])
    prompt = r"""Finde offizielle Referenzkunden für diese Wettbewerber:\n{comps}\nAntworte JSON."""
    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"]}
    # IMPORTANT: The new SDK supports tools via a list in config, not directly as args to generate_content.
    response = client.models.generate_content(
        model=MODEL_NAME,
        contents=prompt,
        config=types.GenerateContentConfig(
            response_mime_type='application/json',
            tools=[types.Tool(google_search_retrieval={})]
        )
    )
    return parse_json_response(response)

# Static Files
dist_path = os.path.join(os.getcwd(), "dist")
if os.path.exists(dist_path):
    print(f"DEBUG: Mounting static files from {dist_path}")
    app.mount("/", StaticFiles(directory=dist_path, html=True), name="static")

@app.get("/api/health")
async def health_check():
    return {"status": "ok", "sdk": "modern-genai", "model": MODEL_NAME}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)