Update app.py

app.py

@@ -6,7 +6,6 @@ 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")
@@ -21,99 +20,26 @@ 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 = "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
+# Gemini API Key (Replace with your actual key)
+GEMINI_API_KEY = "AIzaSyCiRL0ES-zsJGJYsY03xmpwqcggDGcL2Fk"  # <-- Replace 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
-
+    data = {
+        "prompt": f"Provide detailed organic and chemical treatment options, including dosage and preventive care, for {disease_name} in crops.",
+        "temperature": 0.7,
+        "max_tokens": 250
+    }
+    
     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"]
+            result = response.json()
+            treatment = result.get("candidates", [{}])[0].get("output", "No treatment suggestions found.")
+            return treatment
         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)}")