Update app.py

app.py

@@ -3,8 +3,10 @@ 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")
@@ -19,33 +21,100 @@ 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 'xxxxxxx' with your actual API key)
-GEMINI_API_KEY = "AIzaSyDaWXCixp-KcO63-lCtCsFbjyXEIFsNV6k"
+# Gemini API Key
+GEMINI_API_KEY = "import gradio as gr
+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")
+
+# Load Model & Processor
+model_name = "linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification"
+try:
+    processor = AutoImageProcessor.from_pretrained(model_name, use_fast=True)
+    model = AutoModelForImageClassification.from_pretrained(model_name)
+    logging.info("✅ Model and processor loaded successfully.")
+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 = "xxxxxxxx"  # Replace this with your actual API key
+
+# 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 = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateText?key={GEMINI_API_KEY}"
+    headers = {"Content-Type": "application/json"}
+    data = {"contents": [{"parts": [{"text": prompt}]}]}  # Correct request format
+
+    try:
+        response = requests.post(url, headers=headers, json=data)
+        if response.status_code == 200:
+            response_json = response.json()
+            return response_json["candidates"][0]["content"]["parts"][0]["text"]
+        else:
+            logging.error(f"API Error {response.status_code}: {response.text}")
+            return f"API Error: {response.status_code} - {response.text}"
+    except Exception as e:
+        logging.error(f"Error fetching treatment suggestions: {str(e)}")
+        return "Error retrieving treatment details."
+
+# Define Prediction Function
+def predict(image):
+    try:
+        image = Image.fromarray(np.uint8(image)).convert("RGB")
+        inputs = processor(images=image, return_tensors="pt")
+        with torch.no_grad():
+            outputs = model(**inputs)
+        logits = outputs.logits
+        predicted_class_idx = logits.argmax(-1).item()
+        predicted_label = model.config.id2label[predicted_class_idx]
+        
+        # Get AI-generated treatment suggestions
+        treatment = get_treatment_suggestions(predicted_label)
+        
+        return f"Predicted Disease: {predicted_label}\nTreatment: {treatment}"
+    except Exception as e:
+        logging.error(f"Prediction failed: {str(e)}")
+        return f"❌ Prediction failed: {str(e)}"
+
+# Gradio Interface
+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",
+    description="Upload a plant leaf image to detect diseases and get AI-powered treatment suggestions.",
+    allow_flagging="never",
+)
+
+# Launch Gradio App
+iface.launch()
+"  # Replace this with your actual API key
 
 # 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 = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateText?key={GEMINI_API_KEY}"
-    
     headers = {"Content-Type": "application/json"}
-    data = {
-        "contents": [{"parts": [{"text": prompt}]}],  # Correct API request format
-        "temperature": 0.7,
-        "maxOutputTokens": 250  # Correct parameter name
-    }
+    data = {"contents": [{"parts": [{"text": prompt}]}]}  # Correct request format
 
     try:
         response = requests.post(url, headers=headers, json=data)
         if response.status_code == 200:
-            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."
+            response_json = response.json()
+            return response_json["candidates"][0]["content"]["parts"][0]["text"]
         else:
-            logging.error(f"API Error: {response.status_code} - {response.text}")
-            return f"API Error: {response.status_code}"
+            logging.error(f"API Error {response.status_code}: {response.text}")
+            return f"API Error: {response.status_code} - {response.text}"
     except Exception as e:
         logging.error(f"Error fetching treatment suggestions: {str(e)}")
         return "Error retrieving treatment details."
@@ -74,7 +143,7 @@ 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",
 )