Updated

app.py

@@ -1,6 +1,7 @@
 import os
 import logging
-from fastapi import FastAPI, Header, HTTPException
+from fastapi import FastAPI, Request, Header, HTTPException
+from fastapi.responses import JSONResponse
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 from langchain.prompts import PromptTemplate
@@ -9,36 +10,49 @@ from huggingface_hub.utils import HfHubHTTPError
 from langchain.schema import HumanMessage
 from vector import query_vector
 
-# ----------------- CONFIG -----------------
+# ==============================
+# Setup Logging
+# ==============================
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger("AgriCopilot")
 
-PROJECT_API_KEY = os.getenv("PROJECT_API_KEY", "super-secret-123")
-
-# FastAPI app
+# ==============================
+# App Init
+# ==============================
 app = FastAPI(title="AgriCopilot")
 
-# CORS
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  # Change to frontend URL in prod
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
+@app.get("/")
+async def root():
+    return {"status": "AgriCopilot AI Backend is working perfectly"}
+
+# ==============================
+# AUTH CONFIG
+# ==============================
+PROJECT_API_KEY = "agricopilot404"  # 🔑 Fixed bearer token for hackathon
 
-# ----------------- AUTH -----------------
 def check_auth(authorization: str | None):
-    if not PROJECT_API_KEY:
-        return
+    """Validate Bearer token against PROJECT_API_KEY"""
     if not authorization or not authorization.startswith("Bearer "):
         raise HTTPException(status_code=401, detail="Missing bearer token")
     token = authorization.split(" ", 1)[1]
     if token != PROJECT_API_KEY:
         raise HTTPException(status_code=403, detail="Invalid token")
 
-# ----------------- REQUEST MODELS -----------------
-class CropDoctorRequest(BaseModel):
+# ==============================
+# 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)},
+    )
+
+# ==============================
+# Request Models
+# ==============================
+class CropRequest(BaseModel):
     symptoms: str
 
 class ChatRequest(BaseModel):
@@ -47,98 +61,88 @@ class ChatRequest(BaseModel):
 class DisasterRequest(BaseModel):
     report: str
 
-class MarketplaceRequest(BaseModel):
+class MarketRequest(BaseModel):
     product: str
 
 class VectorRequest(BaseModel):
     query: str
 
-# ----------------- PROMPT TEMPLATES -----------------
+# ==============================
+# MODELS PER ENDPOINT
+# ==============================
 crop_template = PromptTemplate(
     input_variables=["symptoms"],
-    template="You are AgriCopilot, a multilingual AI crop doctor. Farmer reports: {symptoms}. Diagnose the disease and suggest treatments in simple farmer-friendly language."
+    template="You are AgriCopilot, a multilingual AI assistant created to support farmers. Farmer reports: {symptoms}. Diagnose the most likely disease and suggest treatments in simple farmer-friendly language."
+)
+crop_llm = HuggingFaceEndpoint(
+    repo_id="meta-llama/Llama-3.2-11B-Vision-Instruct",
+    temperature=0.3, top_p=0.9, do_sample=True,
+    repetition_penalty=1.1, max_new_tokens=1024
 )
 
 chat_template = PromptTemplate(
     input_variables=["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",
+    temperature=0.3, top_p=0.9, do_sample=True,
+    repetition_penalty=1.1, max_new_tokens=1024
+)
 
 disaster_template = PromptTemplate(
     input_variables=["report"],
-    template="You are AgriCopilot, an AI disaster assistant. Summarize the following report for farmers in simple steps: {report}"
+    template="You are AgriCopilot, an AI disaster-response assistant. Summarize in simple steps: {report}"
+)
+disaster_llm = HuggingFaceEndpoint(
+    repo_id="meta-llama/Llama-3.1-8B-Instruct",
+    temperature=0.3, top_p=0.9, do_sample=True,
+    repetition_penalty=1.1, max_new_tokens=1024
 )
 
 market_template = PromptTemplate(
     input_variables=["product"],
-    template="You are AgriCopilot, an agricultural marketplace recommender. Farmer wants: {product}. Suggest buyers/sellers and short advice."
+    template="You are AgriCopilot, an AI agricultural marketplace advisor. Farmer wants to sell or buy: {product}. Suggest best options and advice."
+)
+market_llm = HuggingFaceEndpoint(
+    repo_id="meta-llama/Llama-3.1-8B-Instruct",
+    temperature=0.3, top_p=0.9, do_sample=True,
+    repetition_penalty=1.1, max_new_tokens=1024
 )
 
-# ----------------- 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 -----------------
+# ==============================
+# ENDPOINTS
+# ==============================
 @app.post("/crop-doctor")
-async def crop_doctor(req: CropDoctorRequest, authorization: str | None = Header(None)):
+async def crop_doctor(req: CropRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
-    try:
-        prompt = crop_template.format(symptoms=req.symptoms)
-        response = crop_llm.invoke([HumanMessage(content=prompt)])
-        return {"success": True, "diagnosis": str(response)}
-    except HfHubHTTPError as e:
-        if "quota" in str(e).lower():
-            return {"success": False, "error": "⚠️ Model quota exceeded. Try again later."}
-        raise e
+    prompt = crop_template.format(symptoms=req.symptoms)
+    response = crop_llm.invoke([HumanMessage(content=prompt)])
+    return {"diagnosis": str(response)}
 
 @app.post("/multilingual-chat")
 async def multilingual_chat(req: ChatRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
-    try:
-        prompt = chat_template.format(query=req.query)
-        response = chat_llm.invoke([HumanMessage(content=prompt)])
-        return {"success": True, "reply": str(response)}
-    except HfHubHTTPError as e:
-        if "quota" in str(e).lower():
-            return {"success": False, "error": "⚠️ Model quota exceeded. Try again later."}
-        raise e
+    prompt = chat_template.format(query=req.query)
+    response = chat_llm.invoke([HumanMessage(content=prompt)])
+    return {"reply": str(response)}
 
 @app.post("/disaster-summarizer")
 async def disaster_summarizer(req: DisasterRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
-    try:
-        prompt = disaster_template.format(report=req.report)
-        response = disaster_llm.invoke([HumanMessage(content=prompt)])
-        return {"success": True, "summary": str(response)}
-    except HfHubHTTPError as e:
-        if "quota" in str(e).lower():
-            return {"success": False, "error": "⚠️ Model quota exceeded. Try again later."}
-        raise e
+    prompt = disaster_template.format(report=req.report)
+    response = disaster_llm.invoke([HumanMessage(content=prompt)])
+    return {"summary": str(response)}
 
 @app.post("/marketplace")
-async def marketplace(req: MarketplaceRequest, authorization: str | None = Header(None)):
+async def marketplace(req: MarketRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
-    try:
-        prompt = market_template.format(product=req.product)
-        response = market_llm.invoke([HumanMessage(content=prompt)])
-        return {"success": True, "recommendation": str(response)}
-    except HfHubHTTPError as e:
-        if "quota" in str(e).lower():
-            return {"success": False, "error": "⚠️ Model quota exceeded. Try again later."}
-        raise e
+    prompt = market_template.format(product=req.product)
+    response = market_llm.invoke([HumanMessage(content=prompt)])
+    return {"recommendation": str(response)}
 
 @app.post("/vector-search")
 async def vector_search(req: VectorRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
-    try:
-        results = query_vector(req.query)
-        return {"success": True, "results": results}
-    except Exception as e:
-        return {"success": False, "error": str(e)}
+    results = query_vector(req.query)
+    return {"results": results}