Update app.py

app.py

@@ -1,11 +1,16 @@
+import os
 import gradio as gr
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 from PIL import Image
 import torch
 import numpy as np
-import logging
 import requests
-import os
+import logging
+from dotenv import load_dotenv  # Load .env file
+
+# Load environment variables
+load_dotenv()
+HUGGINGFACE_API_KEY = os.getenv("HUGGINGFACE_API_KEY")
 
 # Configure Logging
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
@@ -20,30 +25,19 @@ except Exception as e:
     logging.error(f"❌ Failed to load model: {str(e)}")
     raise RuntimeError("Failed to load the model. Please check the logs for details.")
 
-# Gemini API Key (Replace with your actual key)
-GEMINI_API_KEY = os.getenv("AIzaSyCiRL0ES-zsJGJYsY03xmpwqcggDGcL2Fk", "AIzaSyCiRL0ES-zsJGJYsY03xmpwqcggDGcL2Fk")
-
 # Function to Get AI-Powered Treatment Suggestions
 def get_treatment_suggestions(disease_name):
-    url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateText?key={GEMINI_API_KEY}"
-    headers = {"Content-Type": "application/json"}
-    data = {
-        "prompt": { "text": f"Provide detailed organic and chemical treatment options, including dosage and preventive care, for {disease_name} in crops." },
-        "temperature": 0.7,
-        "candidate_count": 1
-    }
+    url = "https://api-inference.huggingface.co/models/OpenAGI/agriculture-gpt"
+    headers = {"Authorization": f"Bearer {HUGGINGFACE_API_KEY}"}
+    data = {"inputs": f"What are the treatment options for {disease_name} in plants?"}
 
     try:
         response = requests.post(url, headers=headers, json=data)
         if response.status_code == 200:
-            result = response.json()
-            treatment = result.get("candidates", [{}])[0].get("content", "No treatment suggestions found.")
-            return treatment
+            return response.json()[0]["generated_text"]
         else:
-            logging.error(f"API Error: {response.status_code} - {response.text}")
             return f"API Error: {response.status_code}"
     except Exception as e:
-        logging.error(f"Error fetching treatment suggestions: {str(e)}")
         return "Error retrieving treatment details."
 
 # Define Prediction Function