Initial commit: Add app.py with Groq API integration

app.py

@@ -0,0 +1,371 @@
+import os
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import gradio as gr
+import requests
+import json
+
+# 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
+
+# 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'}")
+
+# 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.",
+        "recommendations": [
+            "Remove and destroy infected plants",
+            "Rotate crops with non-solanaceous plants",
+            "Use copper-based fungicides",
+            "Avoid overhead irrigation"
+        ]
+    },
+    "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.",
+        "recommendations": [
+            "Remove infected leaves promptly",
+            "Improve air circulation around plants",
+            "Apply fungicides preventatively",
+            "Mulch around plants to prevent soil splash"
+        ]
+    },
+    "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.",
+        "recommendations": [
+            "Remove and destroy infected plants immediately",
+            "Apply fungicides preventatively in humid conditions",
+            "Improve drainage and air circulation",
+            "Plant resistant varieties when available"
+        ]
+    },
+    "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.",
+        "recommendations": [
+            "Remove and destroy infected plants",
+            "Wash hands and tools after handling infected plants",
+            "Control insect vectors like aphids",
+            "Plant resistant varieties"
+        ]
+    },
+    "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.",
+        "recommendations": [
+            "Use whitefly control measures",
+            "Remove and destroy infected plants",
+            "Use reflective mulches to repel whiteflies",
+            "Plant resistant varieties"
+        ]
+    }
+}
+
+# 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"
+    }
+
+    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
+    }
+
+    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"]
+
+        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
+        }
+    }
+
+    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."
+    )
+
+    # Call AI model with fallback mechanisms
+    ai_response = call_ai_model(prompt)
+
+    # 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.get('causes', 'Information not available.')}
+
+## Recommended Treatment
+{chr(10).join(f"- {rec}" for rec in info['recommendations'])}
+
+*Note: This is fallback information. For more detailed advice, please try again later when the AI service is available.*
+"""
+
+    return ai_response
+
+# 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 - 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 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. "
+        f"Previous conversation:\n{context}\n\n"
+        f"Farmer's new question: {message}\n\n"
+        f"Provide a helpful, informative response about farming, focusing on tomatoes if relevant."
+    )
+
+    # Call AI model with fallback mechanisms
+    response = call_ai_model(prompt)
+
+    # 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."
+
+    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:
+        adjusted_confidence = raw_confidence
+
+    # Generate AI response
+    ai_response = generate_ai_response(disease_name, adjusted_confidence)
+
+    # Return results
+    result = f"{disease_name} (Confidence: {adjusted_confidence:.2f}%)"
+    raw_text = f"Raw Confidence: {raw_confidence:.2f}%"
+    return result, raw_text, ai_response
+
+# Simplified Gradio UI for better compatibility
+with gr.Blocks() as demo:
+    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")
+
+                scaling_method = gr.Radio(
+                    ["Temperature Scaling", "Min-Max Normalization"],
+                    label="Confidence Scaling Method",
+                    value="Temperature Scaling"
+                )
+                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_expert,
+        inputs=[chat_input, chatbot],
+        outputs=[chat_input, chatbot]
+    )
+
+    # Also allow pressing Enter to send chat
+    chat_input.submit(
+        fn=chat_with_expert,
+        inputs=[chat_input, chatbot],
+        outputs=[chat_input, chatbot]
+    )
+
+demo.launch()