Update app.py

app.py

@@ -3,10 +3,8 @@ from transformers import AutoImageProcessor, AutoModelForImageClassification
 from PIL import Image
 import torch
 import numpy as np
-import json
 import logging
 import requests
-import os
 
 # Configure Logging
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
@@ -21,22 +19,33 @@ 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
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "AIzaSyDaWXCixp-KcO63-lCtCsFbjyXEIFsNV6k")
+# Gemini API Key (Replace 'xxxxxxx' with your actual API key)
+GEMINI_API_KEY = "xxxxxxx"
 
 # Function to Get AI-Powered Treatment Suggestions
 def get_treatment_suggestions(disease_name):
     prompt = f"Provide detailed organic and chemical treatment options, including dosage and preventive care, for {disease_name} in crops."
-    url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateText"
+    url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateText?key={GEMINI_API_KEY}"
+    
     headers = {"Content-Type": "application/json"}
-    data = {"prompt": prompt, "temperature": 0.7, "max_tokens": 250}
-    params = {"key": GEMINI_API_KEY}
+    data = {
+        "contents": [{"parts": [{"text": prompt}]}],  # Correct API request format
+        "temperature": 0.7,
+        "maxOutputTokens": 250  # Correct parameter name
+    }
+
     try:
-        response = requests.post(url, headers=headers, json=data, params=params)
+        response = requests.post(url, headers=headers, json=data)
         if response.status_code == 200:
-            return response.json().get("candidates", [{}])[0].get("output", "No treatment suggestions found.")
+            json_response = response.json()
+            candidates = json_response.get("candidates", [])
+            if candidates:
+                return candidates[0].get("output", "No treatment suggestions found.")
+            else:
+                return "No treatment suggestions found."
         else:
-            return "Failed to fetch treatment suggestions."
+            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."
@@ -65,7 +74,7 @@ iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="numpy", label="Upload or capture plant image"),
     outputs=gr.Textbox(label="Result"),
-    title="AI-Powered Plant Disease Detector",
+    title="🌿 AI-Powered Plant Disease Detector",
     description="Upload a plant leaf image to detect diseases and get AI-powered treatment suggestions.",
     allow_flagging="never",
 )