Update app.py with Groq API integration

app.py

@@ -2,12 +2,102 @@ import os
 import gradio as gr
 import numpy as np
 import requests
+import json
+import time
 from tensorflow.keras.models import load_model
 from PIL import Image
 
-# ===== Hugging Face API Token =====
-# Set HF_API_TOKEN in your environment (as a secret on Hugging Face Spaces)
-HF_API_TOKEN = os.getenv("HF_API_TOKEN")  # e.g., "hf_xxx..."
+# ===== 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)
+
+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'}")
+
+# ===== Disease Information Database =====
+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": [
+            "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.",
+        "treatment": [
+            "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.",
+        "treatment": [
+            "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.",
+        "treatment": [
+            "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.",
+        "treatment": [
+            "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")
@@ -26,7 +116,7 @@ def preprocess_image(image, target_size=(224, 224)):
     return img_array
 
 # ===== Disease Label Mappings =====
-# Model A labels (for example)
+# Model A labels
 disease_labels_a = {
     0: "Tomato Bacterial Spot",
     1: "Tomato Early Blight",
@@ -35,7 +125,7 @@ disease_labels_a = {
     4: "Tomato Yellow Leaf Curl Virus"
 }
 
-# Model B labels (based on your training dataset)
+# Model B labels
 disease_labels_b = {
     0: "Tomato___Target_Spot",
     1: "Tomato___Bacterial_spot",
@@ -49,13 +139,15 @@ def predict_model_a(image):
     img = preprocess_image(image)
     pred = model_a.predict(img)
     predicted_class = np.argmax(pred)
-    return disease_labels_a.get(predicted_class, "Unknown result")
+    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)
-    return disease_labels_b.get(predicted_class, "Unknown result")
+    confidence = float(np.max(pred) * 100)
+    return disease_labels_b.get(predicted_class, "Unknown result"), confidence
 
 def predict_classifier(image):
     img = preprocess_image(image)
@@ -63,113 +155,265 @@ def predict_classifier(image):
     # Here we assume the classifier returns class 1 for "Tomato Leaf"
     return "Tomato Leaf" if np.argmax(pred) == 1 else "Not Tomato Leaf"
 
-# ===== Hugging Face Inference API Calls =====
-def call_llama2(prompt):
-    """Call Llama 2-7B Chat model on Hugging Face for conversational advice."""
-    headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
-    payload = {"inputs": prompt, "parameters": {"max_new_tokens": 100}}
-    url = "https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat-hf"
-    response = requests.post(url, headers=headers, json=payload)
-    if response.status_code == 200:
-        result = response.json()
-        if isinstance(result, list) and "generated_text" in result[0]:
-            return result[0]["generated_text"]
-        else:
-            return "No response from Llama 2."
-    else:
-        return f"Error calling Llama 2 API: {response.status_code}"
-
-def call_openassistant(prompt):
-    """Call an OpenAssistant model on Hugging Face for conversational advice."""
-    headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
-    payload = {"inputs": prompt, "parameters": {"max_new_tokens": 100}}
-    url = "https://api-inference.huggingface.co/models/OpenAssistant/oasst-sft-7-llama"
-    response = requests.post(url, headers=headers, json=payload)
-    if response.status_code == 200:
-        result = response.json()
-        if isinstance(result, list) and "generated_text" in result[0]:
-            return result[0]["generated_text"]
-        else:
-            return "No response from OpenAssistant."
+# ===== 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
+            )
+
+            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
+            )
+
+            if response.status_code == 200:
+                result = response.json()
+                if "choices" in result and len(result["choices"]) > 0:
+                    return result["choices"][0]["message"]["content"]
+
+        except Exception as e:
+            print(f"Error with OpenAI API: {str(e)}")
+
+    # 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 ""
+
+    if disease_name and disease_name in disease_info:
+        info = disease_info[disease_name]
+        return f"""
+# {disease_name}
+
+## Description
+{info['description']}
+
+## Causes
+{info['causes']}
+
+## Recommended Treatment
+{chr(10).join(f"- {rec}" for rec in info['treatment'])}
+
+*Note: This is fallback information as our AI service is currently unavailable.*
+"""
     else:
-        return f"Error calling OpenAssistant API: {response.status_code}"
+        # 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.
+"""
 
 # ===== AI Assistant Functions =====
-def ai_assistant_v1(image, prediction):
-    # Use Llama 2-7B Chat (for Model A versions)
-    if "Tomato" not in prediction:
+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. The tomato crop appears healthy. "
-            "Provide additional preventive tips and best practices for maintaining crop health."
+            "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. A disease has been detected: {prediction}. "
-            "Provide detailed advice on how to manage and curb this disease, explaining it in simple terms."
+            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 call_llama2(prompt)
 
-def ai_assistant_v2(image, prediction):
-    # Use OpenAssistant (for Model B versions)
-    if "Tomato" not in prediction:
-        prompt = (
-            "You are an agricultural advisor. The tomato crop appears healthy. "
-            "Offer additional preventive tips and guidelines for maintaining a healthy crop."
-        )
-    else:
-        prompt = (
-            f"You are an agricultural advisor. A disease has been detected: {prediction}. "
-            "Provide actionable steps and detailed advice on how to control and manage this disease in tomato crops."
-        )
-    return call_openassistant(prompt)
+    return get_ai_advice(prompt)
+
+def chat_with_farmer(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
+
+    prompt = (
+        f"You are FarmAssist, 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."
+    )
+
+    response = get_ai_advice(prompt)
+    chat_history.append((message, response))
+    return "", chat_history
 
 # ===== 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 = predict_model_a(image)
-        return f"Model A Prediction: {result}\n\nView Model A Training Notebook-https://colab.research.google.com/drive/1FMjs7JmdO6WVoXbzLA-ymwnIKq-GaV6w?usp=sharing"
-    
+        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 = predict_model_a(image)
-        advice = ai_assistant_v1(image, result)
-        return f"Model A Prediction: {result}\nAdvice: {advice}"
-    
+        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."
-        result = predict_model_a(image)
-        advice = ai_assistant_v1(image, result)
+            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}\nModel A Prediction: {result}\nAdvice: {advice}\n\n"
-            f"[View Model A & Classifier Training Notebook]--(https://colab.research.google.com/drive/1FMjs7JmdO6WVoXbzLA-ymwnIKq-GaV6w?usp=sharing)"
+            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)"
         )
-    
+
     # --- Version 2.x (Model B) ---
     elif version == "2.1":
-        result = predict_model_b(image)
-        return f"Model B Prediction: {result}\n\n[View Model B Training Notebook]--(https://colab.research.google.com/drive/1CvoQY40gK2YsMgt4wq9kM2ZSO2c4lzFU?usp=sharing)"
-    
+        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 = predict_model_b(image)
-        advice = ai_assistant_v2(image, result)
-        return f"Model B Prediction: {result}\nAdvice: {advice}"
-    
+        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."
-        result = predict_model_b(image)
-        advice = ai_assistant_v2(image, result)
+            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}\nModel B Prediction: {result}\nAdvice: {advice}\n\n"
+            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)"
         )
-    
+
     else:
         return "Invalid version selected."
 
@@ -180,17 +424,17 @@ def combine_images(uploaded, camera):
 # ===== 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; }
+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>
 """
 
 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; }
+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>
 """
 
@@ -204,52 +448,100 @@ def update_css(theme):
 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("# 🌿 FarMVi8ioN – AI-powered Crop Monitoring")
     gr.Markdown("Detect tomato leaf diseases and get actionable advice on how to curb them.")
-    
-    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="1.1",
-                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://huggingface.co/your-model-b-notebook)
-                - Model B & Classifier: [Training Notebook](https://huggingface.co/your-model-b-classifier-notebook)
-                """
+
+    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
             )
-        # ----- 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")
-            submit = gr.Button("🔍 Analyze")
-    
-    output = gr.Textbox(label="📝 Diagnosis & Advice", lines=8)
-    
-    # Update CSS dynamically based on theme selection.
+
+            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", 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.
+
+    # 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
     )
 
+    # Chat functionality
+    chat_button.click(
+        fn=chat_with_farmer,
+        inputs=[chat_input, chatbot],
+        outputs=[chat_input, chatbot]
+    )
+
+    # Also allow pressing Enter to send chat
+    chat_input.submit(
+        fn=chat_with_farmer,
+        inputs=[chat_input, chatbot],
+        outputs=[chat_input, chatbot]
+    )
+
+# Launch the app
 demo.launch()