Update app.py with real-time chat functionality

app.py

@@ -1,26 +1,42 @@
 import os
-import gradio as gr
 import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import gradio as gr
 import requests
 import json
-import time
-from tensorflow.keras.models import load_model
-from PIL import Image
 
-# ===== API Configuration =====
-# Try to get API tokens from environment variables
-HF_API_TOKEN = os.getenv("HUGGINGFACE_TOKEN")  # Hugging Face API token
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")  # Groq API key
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")  # OpenAI API key (fallback)
+# Suppress TensorFlow warnings
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+device = "cuda" if tf.test.is_gpu_available() else "cpu"
+print(f"Running on: {device.upper()}")
+
+# Groq API key for AI assistant
+GROQ_API_KEY = "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"
+GROQ_MODEL = "llama3-70b-8192"  # Using Llama 3 70B model
 
-print(f"API tokens available: HF={'Yes' if HF_API_TOKEN else 'No'}, Groq={'Yes' if GROQ_API_KEY else 'No'}, OpenAI={'Yes' if OPENAI_API_KEY else 'No'}")
+# Fallback to Hugging Face token if Groq fails
+HF_API_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
+print(f"API tokens available: Groq=Yes, HF={'Yes' if HF_API_TOKEN else 'No'}")
 
-# ===== Disease Information Database =====
+# Load the trained tomato disease detection model
+model = tf.keras.models.load_model("Tomato_Leaf_Disease_Model.h5")
+
+# Disease categories
+class_labels = [
+    "Tomato Bacterial Spot",
+    "Tomato Early Blight",
+    "Tomato Late Blight",
+    "Tomato Mosaic Virus",
+    "Tomato Yellow Leaf Curl Virus"
+]
+
+# Disease information database (fallback if API fails)
 disease_info = {
     "Tomato Bacterial Spot": {
         "description": "A bacterial disease that causes small, dark spots on leaves, stems, and fruits.",
         "causes": "Caused by Xanthomonas bacteria, spread by water splash, contaminated tools, and seeds.",
-        "treatment": [
+        "recommendations": [
             "Remove and destroy infected plants",
             "Rotate crops with non-solanaceous plants",
             "Use copper-based fungicides",
@@ -30,7 +46,7 @@ disease_info = {
     "Tomato Early Blight": {
         "description": "A fungal disease that causes dark spots with concentric rings on lower leaves first.",
         "causes": "Caused by Alternaria solani fungus, favored by warm, humid conditions.",
-        "treatment": [
+        "recommendations": [
             "Remove infected leaves promptly",
             "Improve air circulation around plants",
             "Apply fungicides preventatively",
@@ -40,7 +56,7 @@ disease_info = {
     "Tomato Late Blight": {
         "description": "A devastating fungal disease that causes dark, water-soaked lesions on leaves and fruits.",
         "causes": "Caused by Phytophthora infestans, favored by cool, wet conditions.",
-        "treatment": [
+        "recommendations": [
             "Remove and destroy infected plants immediately",
             "Apply fungicides preventatively in humid conditions",
             "Improve drainage and air circulation",
@@ -50,7 +66,7 @@ disease_info = {
     "Tomato Mosaic Virus": {
         "description": "A viral disease that causes mottled green/yellow patterns on leaves and stunted growth.",
         "causes": "Caused by tobacco mosaic virus (TMV), spread by handling, tools, and sometimes seeds.",
-        "treatment": [
+        "recommendations": [
             "Remove and destroy infected plants",
             "Wash hands and tools after handling infected plants",
             "Control insect vectors like aphids",
@@ -60,295 +76,188 @@ disease_info = {
     "Tomato Yellow Leaf Curl Virus": {
         "description": "A viral disease transmitted by whiteflies that causes yellowing and curling of leaves.",
         "causes": "Caused by a begomovirus, transmitted primarily by whiteflies.",
-        "treatment": [
+        "recommendations": [
             "Use whitefly control measures",
             "Remove and destroy infected plants",
             "Use reflective mulches to repel whiteflies",
             "Plant resistant varieties"
         ]
-    },
-    "Tomato___Target_Spot": {
-        "description": "A fungal disease causing circular lesions with concentric rings on leaves, stems, and fruits.",
-        "causes": "Caused by Corynespora cassiicola fungus, favored by warm, humid conditions.",
-        "treatment": [
-            "Remove infected plant parts",
-            "Improve air circulation",
-            "Apply fungicides at first sign of disease",
-            "Avoid overhead irrigation"
-        ]
-    },
-    "Tomato___Bacterial_spot": {
-        "description": "A bacterial disease causing small, dark, water-soaked spots on leaves, stems, and fruits.",
-        "causes": "Caused by Xanthomonas species, spread by water splash and contaminated tools.",
-        "treatment": [
-            "Remove infected plant debris",
-            "Use copper-based bactericides",
-            "Rotate crops",
-            "Use disease-free seeds and transplants"
-        ]
-    },
-    "Tomato___healthy": {
-        "description": "The plant shows no signs of disease and appears to be in good health.",
-        "causes": "Proper growing conditions, good management practices, and disease prevention.",
-        "treatment": [
-            "Continue regular watering and fertilization",
-            "Monitor for early signs of disease",
-            "Maintain good air circulation",
-            "Practice crop rotation"
-        ]
     }
 }
 
-# ===== Load Trained Models =====
-model_a = load_model("Tomato_Leaf_Disease_Model.h5")
-model_b = load_model("tomato_leaf_model_final(77%).h5")
-classifier_model = load_model("tomato_leaf_classifier_optimized.h5")
-
-# ===== Preprocessing Function =====
-def preprocess_image(image, target_size=(224, 224)):
-    # Ensure the image is resized and normalized.
-    if isinstance(image, Image.Image):
-        img = image.resize(target_size)
-    else:
-        img = Image.fromarray(image).resize(target_size)
-    img_array = np.array(img) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
-    return img_array
-
-# ===== Disease Label Mappings =====
-# Model A labels
-disease_labels_a = {
-    0: "Tomato Bacterial Spot",
-    1: "Tomato Early Blight",
-    2: "Tomato Late Blight",
-    3: "Tomato Mosaic Virus",
-    4: "Tomato Yellow Leaf Curl Virus"
-}
-
-# Model B labels
-disease_labels_b = {
-    0: "Tomato___Target_Spot",
-    1: "Tomato___Bacterial_spot",
-    2: "Tomato___Early_blight",
-    3: "Tomato___healthy",
-    4: "Tomato___Late_blight"
-}
+# Image preprocessing function
+def preprocess_image(img):
+    img = img.resize((224, 224))  # Resize for model input
+    img = image.img_to_array(img) / 255.0  # Normalize
+    return np.expand_dims(img, axis=0)  # Add batch dimension
+
+# Temperature Scaling: Adjusts predictions using a temperature parameter.
+def apply_temperature_scaling(prediction, temperature):
+    # Avoid log(0) by adding a small epsilon
+    eps = 1e-8
+    scaled_logits = np.log(np.maximum(prediction, eps)) / temperature
+    exp_logits = np.exp(scaled_logits)
+    scaled_probs = exp_logits / np.sum(exp_logits)
+    return scaled_probs
+
+# Min-Max Normalization: Scales the raw confidence based on provided min and max values.
+def apply_min_max_scaling(confidence, min_conf, max_conf):
+    norm = (confidence - min_conf) / (max_conf - min_conf) * 100
+    norm = np.clip(norm, 0, 100)
+    return norm
+
+# Call Groq API for AI assistant
+def call_groq_api(prompt):
+    """Call Groq API for detailed disease analysis and advice"""
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
 
-# ===== Prediction Functions =====
-def predict_model_a(image):
-    img = preprocess_image(image)
-    pred = model_a.predict(img)
-    predicted_class = np.argmax(pred)
-    confidence = float(np.max(pred) * 100)
-    return disease_labels_a.get(predicted_class, "Unknown result"), confidence
-
-def predict_model_b(image):
-    img = preprocess_image(image)
-    pred = model_b.predict(img)
-    predicted_class = np.argmax(pred)
-    confidence = float(np.max(pred) * 100)
-    return disease_labels_b.get(predicted_class, "Unknown result"), confidence
-
-def predict_classifier(image):
-    img = preprocess_image(image)
-    pred = classifier_model.predict(img)
-    # Here we assume the classifier returns class 1 for "Tomato Leaf"
-    return "Tomato Leaf" if np.argmax(pred) == 1 else "Not Tomato Leaf"
-
-# ===== AI Model API Calls =====
-def get_ai_advice(prompt, retries=2):
-    """Try multiple AI models with fallback mechanisms"""
-
-    # Try Groq API first (if key available)
-    if GROQ_API_KEY:
-        try:
-            headers = {
-                "Authorization": f"Bearer {GROQ_API_KEY}",
-                "Content-Type": "application/json"
-            }
-
-            payload = {
-                "model": "llama3-8b-8192",  # Using Llama 3 8B model
-                "messages": [
-                    {"role": "system", "content": "You are an expert agricultural advisor specializing in tomato farming."},
-                    {"role": "user", "content": prompt}
-                ],
-                "max_tokens": 800,
-                "temperature": 0.7
-            }
-
-            response = requests.post(
-                "https://api.groq.com/openai/v1/chat/completions",
-                headers=headers,
-                json=payload,
-                timeout=30
-            )
+    payload = {
+        "model": GROQ_MODEL,
+        "messages": [
+            {"role": "system", "content": "You are an expert agricultural advisor specializing in tomato farming and plant diseases."},
+            {"role": "user", "content": prompt}
+        ],
+        "max_tokens": 800,
+        "temperature": 0.7
+    }
 
-            if response.status_code == 200:
-                result = response.json()
-                if "choices" in result and len(result["choices"]) > 0:
-                    return result["choices"][0]["message"]["content"]
-
-            print(f"Groq API error: {response.status_code} - {response.text}")
-
-        except Exception as e:
-            print(f"Error with Groq API: {str(e)}")
-
-    # Try Hugging Face Inference API as first fallback (if token available)
-    if HF_API_TOKEN:
-        try:
-            headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
-
-            # Format prompt for instruction-tuned models
-            formatted_prompt = f"""<s>[INST] {prompt} [/INST]"""
-
-            payload = {
-                "inputs": formatted_prompt,
-                "parameters": {
-                    "max_new_tokens": 800,
-                    "temperature": 0.7,
-                    "top_p": 0.95,
-                    "do_sample": True
-                }
-            }
-
-            # Try Mistral model first
-            url = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2"
-
-            response = requests.post(url, headers=headers, json=payload, timeout=30)
-
-            if response.status_code == 200:
-                result = response.json()
-                if isinstance(result, list) and len(result) > 0:
-                    if "generated_text" in result[0]:
-                        # Extract just the response part (after the prompt)
-                        generated_text = result[0]["generated_text"]
-                        # Remove the prompt from the response
-                        response_text = generated_text.split("[/INST]")[-1].strip()
-                        return response_text
-
-            # If Mistral fails, try Llama 3
-            url = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
-            response = requests.post(url, headers=headers, json=payload, timeout=30)
-
-            if response.status_code == 200:
-                result = response.json()
-                if isinstance(result, list) and len(result) > 0:
-                    if "generated_text" in result[0]:
-                        generated_text = result[0]["generated_text"]
-                        response_text = generated_text.split("[/INST]")[-1].strip()
-                        return response_text
-
-        except Exception as e:
-            print(f"Error with Hugging Face API: {str(e)}")
-
-    # Try OpenAI API as final fallback (if key available)
-    if OPENAI_API_KEY:
-        try:
-            headers = {
-                "Authorization": f"Bearer {OPENAI_API_KEY}",
-                "Content-Type": "application/json"
-            }
-
-            payload = {
-                "model": "gpt-3.5-turbo",
-                "messages": [
-                    {"role": "system", "content": "You are an expert agricultural advisor specializing in tomato farming."},
-                    {"role": "user", "content": prompt}
-                ],
-                "max_tokens": 800,
-                "temperature": 0.7
-            }
-
-            response = requests.post(
-                "https://api.openai.com/v1/chat/completions",
-                headers=headers,
-                json=payload,
-                timeout=30
-            )
+    try:
+        response = requests.post(
+            "https://api.groq.com/openai/v1/chat/completions",
+            headers=headers,
+            json=payload,
+            timeout=30
+        )
 
-            if response.status_code == 200:
-                result = response.json()
-                if "choices" in result and len(result["choices"]) > 0:
-                    return result["choices"][0]["message"]["content"]
+        if response.status_code == 200:
+            result = response.json()
+            if "choices" in result and len(result["choices"]) > 0:
+                return result["choices"][0]["message"]["content"]
+
+        print(f"Groq API error: {response.status_code} - {response.text}")
+        return None
+
+    except Exception as e:
+        print(f"Error with Groq API: {str(e)}")
+        return None
+
+# Fallback to Hugging Face if Groq fails
+def call_hf_model(prompt, model_id="mistralai/Mistral-7B-Instruct-v0.2"):
+    """Call an AI model on Hugging Face for detailed disease analysis."""
+    if not HF_API_TOKEN:
+        return None
+
+    headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
+
+    # Format prompt for instruction-tuned models
+    formatted_prompt = f"""<s>[INST] {prompt} [/INST]"""
+
+    payload = {
+        "inputs": formatted_prompt,
+        "parameters": {
+            "max_new_tokens": 500,
+            "temperature": 0.7,
+            "top_p": 0.95,
+            "do_sample": True
+        }
+    }
 
-        except Exception as e:
-            print(f"Error with OpenAI API: {str(e)}")
+    url = f"https://api-inference.huggingface.co/models/{model_id}"
+
+    try:
+        response = requests.post(url, headers=headers, json=payload, timeout=30)
+
+        if response.status_code == 200:
+            result = response.json()
+            if isinstance(result, list) and len(result) > 0:
+                if "generated_text" in result[0]:
+                    # Extract just the response part (after the prompt)
+                    generated_text = result[0]["generated_text"]
+                    # Remove the prompt from the response
+                    response_text = generated_text.split("[/INST]")[-1].strip()
+                    return response_text
+
+        return None
+
+    except Exception as e:
+        print(f"Exception when calling HF model: {str(e)}")
+        return None
+
+# Combined AI model call with fallback
+def call_ai_model(prompt):
+    """Call AI models with fallback mechanisms"""
+    # Try Groq first
+    response = call_groq_api(prompt)
+    if response:
+        return response
+
+    # If Groq fails, try Hugging Face
+    response = call_hf_model(prompt)
+    if response:
+        return response
+
+    # If both fail, return fallback message
+    return "Sorry, I'm having trouble connecting to the AI service. Using fallback information instead."
+
+# Generate AI response for disease analysis
+def generate_ai_response(disease_name, confidence):
+    """Generate a detailed AI response about the detected disease."""
+    # Get fallback information in case AI call fails
+    info = disease_info.get(disease_name, {
+        "description": "Information not available for this disease.",
+        "causes": "Unknown causes.",
+        "recommendations": ["Consult with a local agricultural extension service."]
+    })
+
+    # Create prompt for AI model
+    prompt = (
+        f"You are an agricultural expert advisor. A tomato plant disease has been detected: {disease_name} "
+        f"with {confidence:.2f}% confidence. "
+        f"Provide a detailed analysis including: "
+        f"1) A brief description of the disease "
+        f"2) What causes it and how it spreads "
+        f"3) The impact on tomato plants and yield "
+        f"4) Detailed treatment options (both organic and chemical) "
+        f"5) Prevention strategies for future crops "
+        f"Format your response in clear sections with bullet points where appropriate."
+    )
 
-    # If all API calls fail, use the fallback information from our database
-    disease_name = prompt.split("disease has been detected: ")[-1].split(" with")[0] if "disease has been detected:" in prompt else ""
+    # Call AI model with fallback mechanisms
+    ai_response = call_ai_model(prompt)
 
-    if disease_name and disease_name in disease_info:
-        info = disease_info[disease_name]
-        return f"""
-# {disease_name}
+    # If AI response contains error message, use fallback information
+    if "Sorry, I'm having trouble" in ai_response:
+        ai_response = f"""
+# Disease: {disease_name}
 
 ## Description
 {info['description']}
 
 ## Causes
-{info['causes']}
+{info.get('causes', 'Information not available.')}
 
 ## Recommended Treatment
-{chr(10).join(f"- {rec}" for rec in info['treatment'])}
+{chr(10).join(f"- {rec}" for rec in info['recommendations'])}
 
-*Note: This is fallback information as our AI service is currently unavailable.*
-"""
-    else:
-        # Generic fallback response
-        return """
-# Agricultural Advice
-
-I apologize, but I'm currently unable to connect to our AI service. Here are some general tips for tomato plant care:
-
-## General Tomato Care Tips
-- Water consistently, aiming for 1-2 inches per week
-- Provide support with stakes or cages
-- Fertilize regularly with balanced fertilizer
-- Remove suckers for indeterminate varieties
-- Monitor for pests and diseases regularly
-- Ensure good air circulation between plants
-- Mulch to retain moisture and prevent soil-borne diseases
-
-Please try again later for more specific advice.
+*Note: This is fallback information. For more detailed advice, please try again later when the AI service is available.*
 """
 
-# ===== AI Assistant Functions =====
-def generate_disease_advice(disease_name, confidence):
-    """Generate advice for a specific disease with confidence level."""
-    if "healthy" in disease_name.lower():
-        prompt = (
-            "You are an agricultural advisor speaking to a farmer. "
-            "The tomato crop appears healthy. "
-            "Provide detailed preventive tips and best practices for maintaining tomato crop health. "
-            "Include information about watering, fertilization, pest prevention, and optimal growing conditions. "
-            "Format your response in clear sections with bullet points where appropriate."
-        )
-    else:
-        prompt = (
-            f"You are an agricultural advisor speaking to a farmer. "
-            f"A disease has been detected in their tomato crop: {disease_name} with {confidence:.1f}% confidence. "
-            f"Provide detailed advice on how to identify, manage and treat this disease. "
-            f"Include information about: "
-            f"1) What causes this disease "
-            f"2) How it spreads "
-            f"3) Specific treatments (both organic and chemical options) "
-            f"4) Preventive measures for the future "
-            f"Format your response in clear sections with bullet points where appropriate."
-        )
-
-    return get_ai_advice(prompt)
+    return ai_response
 
-def chat_with_farmer(message, chat_history):
+# Chat with agricultural expert
+def chat_with_expert(message, chat_history):
     """Handle chat interactions with farmers about agricultural topics."""
     if not message.strip():
         return "", chat_history
 
-    # Prepare context from chat history
-    context = "\n".join([f"Farmer: {q}\nAdvisor: {a}" for q, a in chat_history[-3:]])  # Use last 3 exchanges for context
+    # Prepare context from chat history - use last 3 exchanges for context to avoid token limits
+    context = "\n".join([f"Farmer: {q}\nExpert: {a}" for q, a in chat_history[-3:]])
 
     prompt = (
-        f"You are FarmAssist, an expert agricultural advisor specializing in tomato farming and plant diseases. "
+        f"You are an expert agricultural advisor specializing in tomato farming and plant diseases. "
         f"You provide helpful, accurate, and practical advice to farmers. "
         f"Always be respectful and considerate of farmers' knowledge while providing expert guidance. "
         f"If you're unsure about something, acknowledge it and provide the best information you can. "
@@ -357,191 +266,106 @@ def chat_with_farmer(message, chat_history):
         f"Provide a helpful, informative response about farming, focusing on tomatoes if relevant."
     )
 
-    response = get_ai_advice(prompt)
-    chat_history.append((message, response))
-    return "", chat_history
+    # Call AI model with fallback mechanisms
+    response = call_ai_model(prompt)
 
-# ===== Process Function Based on Version =====
-def process_version(image, version):
-    if image is None:
-        return "No image provided."
-
-    # --- Version 1.x (Model A) ---
-    if version == "1.1":
-        result, confidence = predict_model_a(image)
-        return f"Model A Prediction: {result} (Confidence: {confidence:.1f}%)\n\nView Model A Training Notebook: https://colab.research.google.com/drive/1FMjs7JmdO6WVoXbzLA-ymwnIKq-GaV6w?usp=sharing"
-
-    elif version == "1.2":
-        result, confidence = predict_model_a(image)
-        advice = generate_disease_advice(result, confidence)
-        return f"Model A Prediction: {result} (Confidence: {confidence:.1f}%)\n\nExpert Advice:\n{advice}"
-
-    elif version == "1.3":
-        cls_result = predict_classifier(image)
-        if cls_result != "Tomato Leaf":
-            return "Classifier: The image is not a tomato leaf. Please try again with a tomato leaf image."
-
-        result, confidence = predict_model_a(image)
-        advice = generate_disease_advice(result, confidence)
-        return (
-            f"Classifier: {cls_result}\n"
-            f"Model A Prediction: {result} (Confidence: {confidence:.1f}%)\n\n"
-            f"Expert Advice:\n{advice}\n\n"
-            f"[View Model A & Classifier Training Notebook](https://colab.research.google.com/drive/1FMjs7JmdO6WVoXbzLA-ymwnIKq-GaV6w?usp=sharing)"
-        )
+    # If AI response contains error message, use fallback response
+    if "Sorry, I'm having trouble" in response:
+        response = "I apologize, but I'm having trouble connecting to my knowledge base at the moment. Please try again later, or ask a different question about tomato farming or plant diseases."
 
-    # --- Version 2.x (Model B) ---
-    elif version == "2.1":
-        result, confidence = predict_model_b(image)
-        return f"Model B Prediction: {result} (Confidence: {confidence:.1f}%)\n\n[View Model B Training Notebook](https://colab.research.google.com/drive/1CvoQY40gK2YsMgt4wq9kM2ZSO2c4lzFU?usp=sharing)"
-
-    elif version == "2.2":
-        result, confidence = predict_model_b(image)
-        advice = generate_disease_advice(result, confidence)
-        return f"Model B Prediction: {result} (Confidence: {confidence:.1f}%)\n\nExpert Advice:\n{advice}"
-
-    elif version == "2.3":
-        cls_result = predict_classifier(image)
-        if cls_result != "Tomato Leaf":
-            return "Classifier: The image is not a tomato leaf. Please try again with a tomato leaf image."
-
-        result, confidence = predict_model_b(image)
-        advice = generate_disease_advice(result, confidence)
-        return (
-            f"Classifier: {cls_result}\n"
-            f"Model B Prediction: {result} (Confidence: {confidence:.1f}%)\n\n"
-            f"Expert Advice:\n{advice}\n\n"
-            f"[View Model B & Classifier Training Notebook](https://colab.research.google.com/drive/1CvoQY40gK2YsMgt4wq9kM2ZSO2c4lzFU?usp=sharing)"
-        )
+    chat_history.append((message, response))
+    return "", chat_history
 
+# Main detection function with adjustable confidence scaling
+def detect_disease_scaled(img, scaling_method, temperature, min_conf, max_conf):
+    processed_img = preprocess_image(img)
+    prediction = model.predict(processed_img)[0]  # Get prediction for single image
+    raw_confidence = np.max(prediction) * 100
+    class_idx = np.argmax(prediction)
+    disease_name = class_labels[class_idx]
+
+    if scaling_method == "Temperature Scaling":
+        scaled_probs = apply_temperature_scaling(prediction, temperature)
+        adjusted_confidence = np.max(scaled_probs) * 100
+    elif scaling_method == "Min-Max Normalization":
+        adjusted_confidence = apply_min_max_scaling(raw_confidence, min_conf, max_conf)
     else:
-        return "Invalid version selected."
-
-# ===== Helper Function to Choose Between Uploaded & Camera Image =====
-def combine_images(uploaded, camera):
-    return camera if camera is not None else uploaded
-
-# ===== CSS for Theme Switching =====
-light_css = """
-<style>
-body { background-color: white; color: black; }
-.gr-button { background-color: #4CAF50; color: white; }
-.gr-input, .gr-textbox, .gr-dropdown, .gr-radio, .gr-markdown, .gr-container { background-color: white; color: black; }
-</style>
-"""
+        adjusted_confidence = raw_confidence
 
-dark_css = """
-<style>
-body { background-color: #121212 !important; color: #e0e0e0 !important; }
-.gr-button { background-color: #555 !important; color: white !important; }
-.gr-input, .gr-textbox, .gr-dropdown, .gr-radio, .gr-markdown, .gr-container { background-color: #333 !important; color: #e0e0e0 !important; }
-</style>
-"""
+    # Generate AI response
+    ai_response = generate_ai_response(disease_name, adjusted_confidence)
 
-def update_css(theme):
-    if theme == "Dark":
-        return dark_css
-    else:
-        return light_css
+    # Return results
+    result = f"{disease_name} (Confidence: {adjusted_confidence:.2f}%)"
+    raw_text = f"Raw Confidence: {raw_confidence:.2f}%"
+    return result, raw_text, ai_response
 
-# ===== Gradio Interface =====
+# Simplified Gradio UI for better compatibility
 with gr.Blocks() as demo:
-    # Hidden element for CSS injection (initially Light theme)
-    css_injector = gr.HTML(update_css("Light"))
-
-    gr.Markdown("# 🌿 FarMVi8ioN – AI-powered Crop Monitoring")
-    gr.Markdown("Detect tomato leaf diseases and get actionable advice on how to curb them.")
-
-    with gr.Tabs():
-        # === Disease Detection Tab ===
-        with gr.TabItem("Disease Detection"):
-            with gr.Row():
-                # ----- Left Column (≈30%) -----
-                with gr.Column(scale=1):
-                    version = gr.Dropdown(
-                        choices=["1.1", "1.2", "1.3", "2.1", "2.2", "2.3"],
-                        label="Select Version",
-                        value="1.3",
-                        info="Versions 1.x use Model A; Versions 2.x use Model B."
-                    )
-
-                    theme_choice = gr.Radio(
-                        choices=["Light", "Dark"],
-                        label="Select Theme",
-                        value="Light"
-                    )
-
-                    gr.Markdown("### Notebook Links")
-                    gr.Markdown(
-                        """
-                        **For Model A:**
-                        - Model A Only: [Training Notebook](https://colab.research.google.com/drive/1FMjs7JmdO6WVoXbzLA-ymwnIKq-GaV6w?usp=sharing)
-                        - Model A & Classifier: [Training Notebook](https://colab.research.google.com/drive/1CvoQY40gK2YsMgt4wq9kM2ZSO2c4lzFU?usp=sharing)
-
-                        **For Model B:**
-                        - Model B Only: [Training Notebook](https://colab.research.google.com/drive/1CvoQY40gK2YsMgt4wq9kM2ZSO2c4lzFU?usp=sharing)
-                        - Model B & Classifier: [Training Notebook](https://colab.research.google.com/drive/1CvoQY40gK2YsMgt4wq9kM2ZSO2c4lzFU?usp=sharing)
-                        """
-                    )
-
-                # ----- Right Column (≈70%) -----
-                with gr.Column(scale=2):
-                    image_input = gr.Image(label="📂 Upload Tomato Leaf Image", type="pil")
-                    camera_input = gr.Image(label="📸 Use Camera (Live Preview)", type="pil", sources=["webcam"])
-                    submit = gr.Button("🔍 Analyze", variant="primary")
-                    output = gr.Markdown(label="📝 Diagnosis & Advice")
-
-        # === Farmer Chat Tab ===
-        with gr.TabItem("Chat with Farm Assistant"):
-            gr.Markdown("# 💬 Chat with Farm Assistant")
-            gr.Markdown("Ask any questions about farming, crop diseases, or agricultural practices.")
-
-            chatbot = gr.Chatbot(
-                label="Chat History",
-                height=400,
-                bubble_full_width=False,
-                show_copy_button=True
-            )
+    gr.Markdown("# 🍅 EvSentry8: Tomato Disease Detection with AI Assistant")
+
+    with gr.Tab("Disease Detection"):
+        with gr.Row():
+            with gr.Column():
+                image_input = gr.Image(type="pil", label="Upload a Tomato Leaf Image", sources=["upload", "webcam", "clipboard"])
 
-            with gr.Row():
-                chat_input = gr.Textbox(
-                    label="Your Question",
-                    placeholder="Ask about tomato farming, diseases, or agricultural practices...",
-                    lines=2
+                scaling_method = gr.Radio(
+                    ["Temperature Scaling", "Min-Max Normalization"],
+                    label="Confidence Scaling Method",
+                    value="Temperature Scaling"
                 )
-                chat_button = gr.Button("Send", variant="primary")
-
-            gr.Markdown("""
-            ### Example Questions:
-            - How often should I water my tomato plants?
-            - What's the best fertilizer for tomatoes?
-            - How do I prevent early blight?
-            - What are the signs of nutrient deficiency in tomatoes?
-            """)
-
-    # Update CSS dynamically based on theme selection
-    theme_choice.change(fn=update_css, inputs=theme_choice, outputs=css_injector)
-
-    # When submit is clicked, combine image inputs and process the selected version
-    submit.click(
-        fn=lambda uploaded, camera, ver: process_version(combine_images(uploaded, camera), ver),
-        inputs=[image_input, camera_input, version],
-        outputs=output
+                temperature_slider = gr.Slider(0.5, 2.0, step=0.1, label="Temperature", value=1.0)
+                min_conf_slider = gr.Slider(0, 100, step=1, label="Min Confidence", value=20)
+                max_conf_slider = gr.Slider(0, 100, step=1, label="Max Confidence", value=90)
+
+                detect_button = gr.Button("Detect Disease")
+
+            with gr.Column():
+                disease_output = gr.Textbox(label="Detected Disease & Adjusted Confidence")
+                raw_confidence_output = gr.Textbox(label="Raw Confidence")
+                ai_response_output = gr.Markdown(label="AI Assistant's Analysis & Recommendations")
+
+    with gr.Tab("Chat with Expert"):
+        gr.Markdown("# 💬 Chat with Agricultural Expert")
+        gr.Markdown("Ask any questions about tomato farming, diseases, or agricultural practices.")
+
+        chatbot = gr.Chatbot(height=400)
+
+        with gr.Row():
+            chat_input = gr.Textbox(
+                label="Your Question",
+                placeholder="Ask about tomato farming, diseases, or agricultural practices...",
+                lines=2
+            )
+            chat_button = gr.Button("Send")
+
+        gr.Markdown("""
+        ### Example Questions:
+        - How do I identify tomato bacterial spot?
+        - What's the best way to prevent late blight?
+        - How often should I water my tomato plants?
+        - What are the signs of nutrient deficiency in tomatoes?
+        """)
+
+    # Set up event handlers
+    detect_button.click(
+        detect_disease_scaled,
+        inputs=[image_input, scaling_method, temperature_slider, min_conf_slider, max_conf_slider],
+        outputs=[disease_output, raw_confidence_output, ai_response_output]
     )
 
     # Chat functionality
     chat_button.click(
-        fn=chat_with_farmer,
+        fn=chat_with_expert,
         inputs=[chat_input, chatbot],
         outputs=[chat_input, chatbot]
     )
 
     # Also allow pressing Enter to send chat
     chat_input.submit(
-        fn=chat_with_farmer,
+        fn=chat_with_expert,
         inputs=[chat_input, chatbot],
         outputs=[chat_input, chatbot]
     )
 
-# Launch the app
 demo.launch()