Updated

app.py

@@ -7,7 +7,6 @@ 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
 
 # ==============================
@@ -78,7 +77,7 @@ crop_template = PromptTemplate(
 )
 crop_llm = HuggingFaceEndpoint(
     repo_id="meta-llama/Llama-3.2-11B-Vision-Instruct",
-    task="conversational",   # ✅ FIXED
+    task="conversational",
     temperature=0.3,
     top_p=0.9,
     do_sample=True,
@@ -93,7 +92,7 @@ chat_template = PromptTemplate(
 )
 chat_llm = HuggingFaceEndpoint(
     repo_id="meta-llama/Llama-3.1-8B-Instruct",
-    task="conversational",   # ✅ FIXED
+    task="conversational",
     temperature=0.3,
     top_p=0.9,
     do_sample=True,
@@ -108,7 +107,7 @@ disaster_template = PromptTemplate(
 )
 disaster_llm = HuggingFaceEndpoint(
     repo_id="meta-llama/Llama-3.1-8B-Instruct",
-    task="conversational",   # ✅ FIXED
+    task="conversational",
     temperature=0.3,
     top_p=0.9,
     do_sample=True,
@@ -123,7 +122,7 @@ market_template = PromptTemplate(
 )
 market_llm = HuggingFaceEndpoint(
     repo_id="meta-llama/Llama-3.1-8B-Instruct",
-    task="conversational",   # ✅ FIXED
+    task="conversational",
     temperature=0.3,
     top_p=0.9,
     do_sample=True,
@@ -131,6 +130,19 @@ market_llm = HuggingFaceEndpoint(
     max_new_tokens=1024
 )
 
+# ==============================
+# ENDPOINT HELPERS
+# ==============================
+def run_conversational_model(model, prompt: str):
+    """Wraps prompt into HF conversational format"""
+    return model.invoke({
+        "inputs": {
+            "past_user_inputs": [],
+            "generated_responses": [],
+            "text": prompt
+        }
+    })
+
 # ==============================
 # ENDPOINTS
 # ==============================
@@ -138,32 +150,47 @@ market_llm = HuggingFaceEndpoint(
 async def crop_doctor(req: CropRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
     prompt = crop_template.format(symptoms=req.symptoms)
-    response = crop_llm.invoke([HumanMessage(content=prompt)])
-    return {"diagnosis": str(response)}
+    try:
+        response = run_conversational_model(crop_llm, prompt)
+        return {"diagnosis": str(response)}
+    except HfHubHTTPError as e:
+        return {"error": f"HuggingFace error: {str(e)}"}
 
 @app.post("/multilingual-chat")
 async def multilingual_chat(req: ChatRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
     prompt = chat_template.format(query=req.query)
-    response = chat_llm.invoke([HumanMessage(content=prompt)])
-    return {"reply": str(response)}
+    try:
+        response = run_conversational_model(chat_llm, prompt)
+        return {"reply": str(response)}
+    except HfHubHTTPError as e:
+        return {"error": f"HuggingFace error: {str(e)}"}
 
 @app.post("/disaster-summarizer")
 async def disaster_summarizer(req: DisasterRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
     prompt = disaster_template.format(report=req.report)
-    response = disaster_llm.invoke([HumanMessage(content=prompt)])
-    return {"summary": str(response)}
+    try:
+        response = run_conversational_model(disaster_llm, prompt)
+        return {"summary": str(response)}
+    except HfHubHTTPError as e:
+        return {"error": f"HuggingFace error: {str(e)}"}
 
 @app.post("/marketplace")
 async def marketplace(req: MarketRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
     prompt = market_template.format(product=req.product)
-    response = market_llm.invoke([HumanMessage(content=prompt)])
-    return {"recommendation": str(response)}
+    try:
+        response = run_conversational_model(market_llm, prompt)
+        return {"recommendation": str(response)}
+    except HfHubHTTPError as e:
+        return {"error": f"HuggingFace error: {str(e)}"}
 
 @app.post("/vector-search")
 async def vector_search(req: VectorRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
-    results = query_vector(req.query)
-    return {"results": results}
+    try:
+        results = query_vector(req.query)
+        return {"results": results}
+    except Exception as e:
+        return {"error": f"Vector search error: {str(e)}"}