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import os
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import List
from enhanced_prompt_builder import EnhancedPromptBuilder
from feedback_analyzer import FeedbackAnalyzer
from google import generativeai as genai
from datetime import datetime
import json
from dotenv import load_dotenv
load_dotenv()
# Read Gemini API key from Hugging Face secret
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
if not GEMINI_API_KEY:
raise RuntimeError("GEMINI_API_KEY not found in environment.")
model = genai.GenerativeModel("gemini-2.5-flash")
def call_gemini(prompt: str) -> str:
"""Use Gemini via REST API instead of gRPC-based SDK"""
url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent?key={GEMINI_API_KEY}"
payload = {
"contents": [{"parts": [{"text": prompt}]}]
}
response = requests.post(url, json=payload)
try:
return response.json()["candidates"][0]["content"]["parts"][0]["text"]
except Exception:
raise HTTPException(status_code=500, detail="Error in Gemini response format.")
app = FastAPI()
# CORS
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Initialize enhanced components
enhanced_builder = EnhancedPromptBuilder()
feedback_analyzer = FeedbackAnalyzer()
class AdRequest(BaseModel):
ad_text: str
tone: str
platforms: List[str]
class Feedback(BaseModel):
ad_text: str
tone: str
platforms: List[str]
rewritten_output: str
rating: int # 1 to 5
@app.post("/run-enhanced-agent")
def run_enhanced_agent(request: AdRequest):
"""Run the agent with enhanced RAG, KG traversal, and adaptive learning"""
try:
# Use enhanced prompt builder
prompt = enhanced_builder.build_adaptive_prompt(
request.ad_text,
request.tone,
request.platforms
)
# Generate response
response = model.generate_content(prompt)
# Get improvement suggestions
suggestions = enhanced_builder.get_improvement_suggestions()
return {
"rewritten_ads": response.text,
"metadata": {
"used_enhanced_features": True,
"improvement_suggestions": suggestions[:3] # Top 3 suggestions
}
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/feedback")
def submit_feedback(feedback: Feedback):
entry = {
"timestamp": datetime.now().isoformat(),
"ad_text": feedback.ad_text,
"tone": feedback.tone,
"platforms": feedback.platforms,
"rewritten_output": feedback.rewritten_output,
"rating": feedback.rating
}
try:
with open("feedback_store.json", "r+", encoding="utf-8") as f:
data = json.load(f)
data.append(entry)
f.seek(0)
json.dump(data, f, indent=2)
return {"message": "Feedback submitted successfully"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"Error storing feedback: {str(e)}")
@app.get("/insights")
def get_insights():
"""Get insights from feedback analysis"""
try:
analysis = feedback_analyzer.analyze_patterns()
trends = feedback_analyzer.get_time_based_trends()
weights = feedback_analyzer.get_adaptive_weights()
return {
"analysis_summary": {
"total_feedback": analysis.get("total_feedback", 0),
"average_rating": round(analysis.get("average_rating", 0), 2),
"recommendations": analysis.get("recommendations", [])[:5]
},
"performance_by_tone": analysis.get("tone_stats", {}),
"performance_by_platform": analysis.get("platform_stats", {}),
"winning_combinations": analysis.get("high_performing_patterns", []),
"needs_improvement": analysis.get("low_performing_patterns", []),
"adaptive_weights": weights,
"recent_trends": trends
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/graph-insights/{tone}/{platform}")
def get_graph_insights(tone: str, platform: str):
"""Get knowledge graph insights for a specific tone-platform combination"""
try:
from enhanced_knowledge_graph import EnhancedKnowledgeGraph
kg = EnhancedKnowledgeGraph()
recommendations = kg.get_recommendations(tone, platform)
relationship = kg.explain_relationship(tone, platform)
# Find related nodes
tone_related = kg.traverse_bfs(tone, max_depth=2)
platform_related = kg.traverse_bfs(platform, max_depth=2)
return {
"tone_platform_analysis": {
"tone": tone,
"platform": platform,
"compatibility_score": recommendations["compatibility_score"],
"relationship_explanation": relationship,
"suggestions": recommendations["suggested_elements"],
"warnings": recommendations["warnings"],
"recommended_creative_types": recommendations["creative_types"]
},
"graph_connections": {
"tone_connections": list(tone_related.keys()),
"platform_connections": list(platform_related.keys())
}
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
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