Updated

app.py

@@ -1,44 +1,33 @@
 import os
 import logging
-from fastapi import FastAPI, Request, Header, HTTPException
-from fastapi.responses import JSONResponse
+from fastapi import FastAPI, Header, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
-from huggingface_hub.utils import HfHubHTTPError
+from pydantic import BaseModel
 from langchain.prompts import PromptTemplate
 from langchain_huggingface import HuggingFaceEndpoint
+from huggingface_hub.utils import HfHubHTTPError
+from langchain.schema import HumanMessage
 from vector import query_vector
 
-# ==============================
-# Setup Logging
-# ==============================
+# ----------------- CONFIG -----------------
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger("AgriCopilot")
 
-# ==============================
-# Config
-# ==============================
-PROJECT_API_KEY = os.getenv("PROJECT_API_KEY", "super-secret-123")  # 🔑 Change in prod
+PROJECT_API_KEY = os.getenv("PROJECT_API_KEY", "super-secret-123")
 
-# ==============================
-# App Init
-# ==============================
+# FastAPI app
 app = FastAPI(title="AgriCopilot")
 
-@app.get("/")
-async def root():
-    return {"status": "AgriCopilot AI Backend is working perfectly"}
-
-# ==============================
-# Global Exception Handler
-# ==============================
-@app.exception_handler(Exception)
-async def global_exception_handler(request: Request, exc: Exception):
-    logger.error(f"Unhandled error: {exc}")
-    return JSONResponse(status_code=500, content={"error": str(exc)})
-
-# ==============================
-# Auth Helper
-# ==============================
+# CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Change to frontend URL in prod
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ----------------- AUTH -----------------
 def check_auth(authorization: str | None):
     if not PROJECT_API_KEY:
         return
@@ -48,102 +37,108 @@ def check_auth(authorization: str | None):
     if token != PROJECT_API_KEY:
         raise HTTPException(status_code=403, detail="Invalid token")
 
-# ==============================
-# HuggingFace Model Config
-# ==============================
-default_model = dict(
-    temperature=0.3,
-    top_p=0.9,
-    do_sample=True,
-    repetition_penalty=1.1,
-    max_new_tokens=1024
-)
+# ----------------- REQUEST MODELS -----------------
+class CropDoctorRequest(BaseModel):
+    symptoms: str
 
-# ==============================
-# CHAINS
-# ==============================
+class ChatRequest(BaseModel):
+    query: str
 
-# 1. Crop Doctor
+class DisasterRequest(BaseModel):
+    report: str
+
+class MarketplaceRequest(BaseModel):
+    product: str
+
+class VectorRequest(BaseModel):
+    query: str
+
+# ----------------- PROMPT TEMPLATES -----------------
 crop_template = PromptTemplate(
     input_variables=["symptoms"],
-    template="You are AgriCopilot, a multilingual AI assistant for farmers. "
-             "A farmer reports: {symptoms}. Diagnose the likely disease and suggest "
-             "clear, farmer-friendly treatments."
+    template="You are AgriCopilot, a multilingual AI crop doctor. Farmer reports: {symptoms}. Diagnose the disease and suggest treatments in simple farmer-friendly language."
 )
-crop_llm = HuggingFaceEndpoint(repo_id="meta-llama/Llama-3.2-11B-Vision-Instruct", **default_model)
-crop_chain = crop_template | crop_llm
 
-# 2. Multilingual Chat
 chat_template = PromptTemplate(
     input_variables=["query"],
-    template="You are AgriCopilot, a supportive multilingual AI guide. "
-             "Respond in the SAME language as the user. Farmer says: {query}"
+    template="You are AgriCopilot, a supportive multilingual AI guide built for farmers. Farmer says: {query}"
 )
-chat_llm = HuggingFaceEndpoint(repo_id="meta-llama/Llama-3.1-8B-Instruct", **default_model)
-chat_chain = chat_template | chat_llm
 
-# 3. Disaster Summarizer
 disaster_template = PromptTemplate(
     input_variables=["report"],
-    template="You are AgriCopilot, an AI disaster assistant. "
-             "Summarize the following report into 3–5 short, actionable steps farmers can follow: {report}"
+    template="You are AgriCopilot, an AI disaster assistant. Summarize the following report for farmers in simple steps: {report}"
 )
-disaster_llm = HuggingFaceEndpoint(repo_id="meta-llama/Llama-3.1-8B-Instruct", **default_model)
-disaster_chain = disaster_template | disaster_llm
 
-# 4. Marketplace Recommender
 market_template = PromptTemplate(
     input_variables=["product"],
-    template="You are AgriCopilot, an agricultural marketplace advisor. "
-             "Farmer wants to sell or buy: {product}. Suggest options, advice, and safe trade tips."
+    template="You are AgriCopilot, an agricultural marketplace recommender. Farmer wants: {product}. Suggest buyers/sellers and short advice."
 )
-market_llm = HuggingFaceEndpoint(repo_id="meta-llama/Llama-3.1-8B-Instruct", **default_model)
-market_chain = market_template | market_llm
 
-# ==============================
-# ENDPOINTS (with auth)
-# ==============================
+# ----------------- LLM MODELS -----------------
+crop_llm = HuggingFaceEndpoint(repo_id="meta-llama/Llama-3.2-11B-Vision-Instruct")
+chat_llm = HuggingFaceEndpoint(repo_id="meta-llama/Llama-3.1-8B-Instruct")
+disaster_llm = HuggingFaceEndpoint(repo_id="meta-llama/Llama-3.1-8B-Instruct")
+market_llm = HuggingFaceEndpoint(repo_id="meta-llama/Llama-3.1-8B-Instruct")
+
+# ----------------- ROOT -----------------
+@app.get("/")
+async def root():
+    return {"status": "✅ AgriCopilot AI Backend running"}
+
+# ----------------- ENDPOINTS -----------------
 @app.post("/crop-doctor")
-async def crop_doctor(symptoms: str, authorization: str | None = Header(None)):
+async def crop_doctor(req: CropDoctorRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
     try:
-        response = crop_chain.invoke({"symptoms": symptoms})
-        return {"diagnosis": str(response)}
+        prompt = crop_template.format(symptoms=req.symptoms)
+        response = crop_llm.invoke([HumanMessage(content=prompt)])
+        return {"success": True, "diagnosis": str(response)}
     except HfHubHTTPError as e:
-        return {"error": f"HuggingFace error: {str(e)}"}
+        if "quota" in str(e).lower():
+            return {"success": False, "error": "⚠️ Model quota exceeded. Try again later."}
+        raise e
 
 @app.post("/multilingual-chat")
-async def multilingual_chat(query: str, authorization: str | None = Header(None)):
+async def multilingual_chat(req: ChatRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
     try:
-        response = chat_chain.invoke({"query": query})
-        return {"reply": str(response)}
+        prompt = chat_template.format(query=req.query)
+        response = chat_llm.invoke([HumanMessage(content=prompt)])
+        return {"success": True, "reply": str(response)}
     except HfHubHTTPError as e:
-        return {"error": f"HuggingFace error: {str(e)}"}
+        if "quota" in str(e).lower():
+            return {"success": False, "error": "⚠️ Model quota exceeded. Try again later."}
+        raise e
 
 @app.post("/disaster-summarizer")
-async def disaster_summarizer(report: str, authorization: str | None = Header(None)):
+async def disaster_summarizer(req: DisasterRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
     try:
-        response = disaster_chain.invoke({"report": report})
-        return {"summary": str(response)}
+        prompt = disaster_template.format(report=req.report)
+        response = disaster_llm.invoke([HumanMessage(content=prompt)])
+        return {"success": True, "summary": str(response)}
     except HfHubHTTPError as e:
-        return {"error": f"HuggingFace error: {str(e)}"}
+        if "quota" in str(e).lower():
+            return {"success": False, "error": "⚠️ Model quota exceeded. Try again later."}
+        raise e
 
 @app.post("/marketplace")
-async def marketplace(product: str, authorization: str | None = Header(None)):
+async def marketplace(req: MarketplaceRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
     try:
-        response = market_chain.invoke({"product": product})
-        return {"recommendation": str(response)}
+        prompt = market_template.format(product=req.product)
+        response = market_llm.invoke([HumanMessage(content=prompt)])
+        return {"success": True, "recommendation": str(response)}
     except HfHubHTTPError as e:
-        return {"error": f"HuggingFace error: {str(e)}"}
+        if "quota" in str(e).lower():
+            return {"success": False, "error": "⚠️ Model quota exceeded. Try again later."}
+        raise e
 
 @app.post("/vector-search")
-async def vector_search(query: str, authorization: str | None = Header(None)):
+async def vector_search(req: VectorRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
     try:
-        results = query_vector(query)
-        return {"results": results}
+        results = query_vector(req.query)
+        return {"success": True, "results": results}
     except Exception as e:
-        return {"error": f"Vector search error: {str(e)}"}
+        return {"success": False, "error": str(e)}