Updated

.env

@@ -0,0 +1 @@
+AgriCopilot_OPENROUTER_MODEL_KEY=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

app.py

@@ -5,12 +5,12 @@
 import os
 import logging
 import io
-import torch
+import base64
 from fastapi import FastAPI, Request, Header, HTTPException, UploadFile, File
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel
-from transformers import pipeline
-from PIL import Image
+from openai import OpenAI
+from duckduckgo_search import DDGS
 from vector import query_vector
 
 # ==============================
@@ -67,102 +67,99 @@ class VectorRequest(BaseModel):
     query: str
 
 # ==============================
-# Hugging Face Config
+# OpenRouter Config
 # ==============================
-HF_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+OPENROUTER_API_KEY = os.getenv("AgriCopilot_OPENROUTER_MODEL_KEY")
+if not OPENROUTER_API_KEY:
+    logger.error("🛑 Missing AgriCopilot_OPENROUTER_MODEL_KEY")
 
-if not HF_TOKEN:
-    logger.warning("⚠️ No Hugging Face token found. Gated models may fail to load.")
-else:
-    logger.info("✅ Hugging Face token detected.")
-
-# Device setup (GPU if available)
-device = 0 if torch.cuda.is_available() else -1
-logger.info(f"🧠 Using device: {'GPU' if device == 0 else 'CPU'}")
-
-# ==============================
-# Pipelines
-# ==============================
-# Conversational + reasoning models (Meta LLaMA)
-chat_pipe = pipeline(
-    "text-generation",
-    model="meta-llama/Llama-3.1-8B-Instruct",
-    token=HF_TOKEN,
-    device=device,
-)
-
-disaster_pipe = pipeline(
-    "text-generation",
-    model="meta-llama/Llama-3.1-8B-Instruct",
-    token=HF_TOKEN,
-    device=device,
-)
-
-market_pipe = pipeline(
-    "text-generation",
-    model="meta-llama/Llama-3.1-8B-Instruct",
-    token=HF_TOKEN,
-    device=device,
-)
-
-# Lightweight Meta Vision backbone (ConvNeXt-Tiny)
-crop_vision = pipeline(
-    "image-classification",
-    model="facebook/convnext-tiny-224",
-    token=HF_TOKEN,
-    device=device,
+client = OpenAI(
+    base_url="https://openrouter.ai/api/v1",
+    api_key=OPENROUTER_API_KEY,
 )
+MODEL_NAME = "qwen/qwen3-vl-235b-a22b-thinking"
 
 # ==============================
 # Helper Functions
 # ==============================
-def run_conversational(pipe, prompt: str):
-    """Handles conversational tasks safely."""
+def format_ai_response(text: str) -> str:
+    return f"{text}\n\n---\n**AI: AgriCopilot | Provider: Team Astra**"
+
+def run_with_fallback(system_prompt: str, user_prompt: str) -> str:
     try:
-        output = pipe(prompt, max_new_tokens=200, temperature=0.7, do_sample=True)
-        if isinstance(output, list) and len(output) > 0:
-            return output[0].get("generated_text", str(output))
-        return str(output)
+        # First pass
+        response = client.chat.completions.create(
+            model=MODEL_NAME,
+            messages=[
+                {"role": "system", "content": system_prompt + " If you do not know the answer and lack sufficient knowledge, explicitly reply with only 'WEB_SEARCH_REQUIRED'."},
+                {"role": "user", "content": user_prompt}
+            ],
+            temperature=0.7,
+            max_tokens=800
+        )
+        reply = response.choices[0].message.content.strip()
+
+        if "WEB_SEARCH_REQUIRED" in reply.upper():
+            logger.info("🌍 Web search triggered!")
+            ddgs = DDGS()
+            search_results = list(ddgs.text(user_prompt, max_results=3))
+            context = " ".join([r['body'] for r in search_results]) if search_results else "No relevant search results found online."
+            
+            # Second pass with search context
+            second_prompt = f"Using this web search context: {context}\n\nAnswer the user: {user_prompt}"
+            response2 = client.chat.completions.create(
+                model=MODEL_NAME,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": second_prompt}
+                ],
+                temperature=0.7,
+                max_tokens=800
+            )
+            reply = response2.choices[0].message.content.strip()
+            reply += "\n\n*(Information augmented with real-time web search)*"
+            
+        return format_ai_response(reply)
     except Exception as e:
         logger.error(f"Conversational pipeline error: {e}")
         return f"⚠️ Model error: {str(e)}"
 
 def run_crop_doctor(image_bytes: bytes, symptoms: str):
-    """
-    Hybrid Crop Doctor System:
-    1. Uses ConvNeXt to classify plant visuals.
-    2. Pulls related info from vector dataset.
-    3. LLaMA 3.1 generates a short diagnosis and treatment guide.
-    """
     try:
-        # --- Step 1: Vision Classification ---
-        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
-        vision_results = crop_vision(image)
-        if not vision_results or "label" not in vision_results[0]:
-            raise ValueError("No vision classification result received.")
-        top_label = vision_results[0]["label"]
-
-        # --- Step 2: Vector Knowledge Recall ---
+        # Extract base64 encoded image
+        base64_image = base64.b64encode(image_bytes).decode('utf-8')
+        image_url = f"data:image/jpeg;base64,{base64_image}"
+        
         vector_matches = query_vector(symptoms)
         related_knowledge = " ".join(vector_matches[:3]) if isinstance(vector_matches, list) else str(vector_matches)
 
-        # --- Step 3: Reasoning via LLaMA ---
         prompt = (
-            f"A farmer uploaded a maize image showing signs of '{top_label}'. "
-            f"Reported symptoms: {symptoms}. "
+            "Analyze this crop image. "
+            f"The farmer reported these symptoms: {symptoms}. "
             f"Knowledge base reference: {related_knowledge}. "
             "Generate a structured diagnostic report with:\n"
             "1. Disease Name\n2. Cause\n3. Treatment\n4. Prevention Tips\n"
             "Keep the explanation short and easy for farmers to understand."
         )
 
-        response = chat_pipe(prompt, max_new_tokens=250, temperature=0.6, do_sample=False, truncation=True)
+        response = client.chat.completions.create(
+            model=MODEL_NAME,
+            messages=[
+                {"role": "system", "content": "You are AgriCopilot, an expert agricultural AI assistant."},
+                {"role": "user", "content": [
+                    {"type": "text", "text": prompt},
+                    {"type": "image_url", "image_url": {"url": image_url}}
+                ]}
+            ],
+            temperature=0.6,
+            max_tokens=800
+        )
 
-        # Extract text output
-        if isinstance(response, list) and len(response) > 0:
-            text = response[0].get("generated_text", "").strip()
-            return text if text else "⚠️ No response generated. Try again with clearer image or symptoms."
-        return "⚠️ Unexpected response format from reasoning model."
+        reply = response.choices[0].message.content.strip()
+        if not reply:
+            return "⚠️ No response generated. Try again with clearer image or symptoms."
+            
+        return format_ai_response(reply)
 
     except Exception as e:
         logger.error(f"Crop Doctor error: {e}")
@@ -186,19 +183,22 @@ async def crop_doctor(
 @app.post("/multilingual-chat")
 async def multilingual_chat(req: ChatRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
-    reply = run_conversational(chat_pipe, req.query)
+    sys_prompt = "You are AgriCopilot, an expert agricultural AI assistant. Answer the farmer's question helpfully."
+    reply = run_with_fallback(sys_prompt, req.query)
     return {"reply": reply}
 
 @app.post("/disaster-summarizer")
 async def disaster_summarizer(req: DisasterRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
-    summary = run_conversational(disaster_pipe, req.report)
+    sys_prompt = "You are AgriCopilot. Summarize the following disaster report for a farmer and provide actionable immediate next steps."
+    summary = run_with_fallback(sys_prompt, req.report)
     return {"summary": summary}
 
 @app.post("/marketplace")
 async def marketplace(req: MarketRequest, authorization: str | None = Header(None)):
     check_auth(authorization)
-    recommendation = run_conversational(market_pipe, req.product)
+    sys_prompt = "You are AgriCopilot. Provide marketplace recommendations, pricing strategies, and selling tips for the specified agricultural product."
+    recommendation = run_with_fallback(sys_prompt, req.product)
     return {"recommendation": recommendation}
 
 @app.post("/vector-search")

requirements.txt

@@ -2,14 +2,9 @@ fastapi
 uvicorn[standard]
 langchain-community
 faiss-cpu
-huggingface-hub
-sentence-transformers
 pandas
 datasets
-transformers
-accelerate
-torch
-sentencepiece
 kagglehub
-langdetect
-python-multipart
+python-multipart
+duckduckgo-search
+openai