🍅 Tomato Disease Detective 🕵️♂️
', unsafe_allow_html=True) # Sidebar for additional information with st.sidebar: st.image("https://upload.wikimedia.org/wikipedia/commons/8/89/Tomato_je.jpg", use_column_width=True) st.markdown("## About") st.info("This app uses AI to detect diseases in tomato plants. Simply upload an image of a tomato leaf, and let the detective do its work!") st.markdown("## How to use") st.write("1. Upload a clear image of a tomato leaf") st.write("2. Wait for the AI to analyze") st.write("3. Review the diagnosis and recommendations") st.markdown("## 🧑🌾 Happy Gardening! 🌱") # Main content col1, col2 = st.columns([1, 1]) with col1: st.markdown('Upload Your Tomato Leaf Image
', unsafe_allow_html=True) uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) # Load model and other setup (as in your original code) model_path = 'tomato_model' try: VIT = load_model(model_path) except Exception as e: st.error(f"Error loading model: {e}") # Define the class names class_names = [ 'Tomato_Bacterial_spot', 'Tomato_Early_blight', 'Tomato_Late_blight', 'Tomato_Leaf_Mold', 'Tomato_Septoria_leaf_spot', 'Tomato_Spider_mites_Two_spotted_spider_mite', 'Tomato_Target_Spot', 'Tomato_Tomato_Yellow_Leaf_Curl_Virus', 'Tomato_Tomato_mosaic_virus', 'Tomato_healthy' ] # Define cure and prevention suggestions for each disease disease_info = { 'Tomato_Bacterial_spot': { 'cure': [ "Remove infected plants", "Apply copper-based fungicides", "Prune to improve air circulation" ], 'prevention': [ "Use disease-free seeds", "Practice crop rotation", "Avoid overhead irrigation", "Disinfect gardening tools regularly" ] }, 'Tomato_Early_blight': { 'cure': [ "Remove infected leaves", "Apply fungicides (chlorothalonil, mancozeb, or copper)", "Improve air circulation around plants" ], 'prevention': [ "Mulch around plants", "Ensure good air circulation", "Water at the base of plants", "Rotate crops every 2-3 years" ] }, 'Tomato_Late_blight': { 'cure': [ "Remove and destroy infected plants", "Apply fungicides (chlorothalonil or copper-based)", "Harvest remaining healthy fruit" ], 'prevention': [ "Plant resistant varieties", "Avoid overhead watering", "Space plants properly", "Remove volunteers and nightshade weeds" ] }, 'Tomato_Leaf_Mold': { 'cure': [ "Improve air circulation", "Apply fungicides (chlorothalonil or mancozeb)", "Remove severely infected leaves" ], 'prevention': [ "Reduce humidity in greenhouses", "Avoid leaf wetness", "Use resistant varieties", "Prune and stake plants for better air flow" ] }, 'Tomato_Septoria_leaf_spot': { 'cure': [ "Remove infected leaves", "Apply fungicides (chlorothalonil or copper-based)", "Improve air circulation" ], 'prevention': [ "Mulch around plants", "Practice crop rotation", "Avoid overhead watering", "Remove plant debris after harvest" ] }, 'Tomato_Spider_mites_Two_spotted_spider_mite': { 'cure': [ "Use insecticidal soaps or neem oil", "Introduce predatory mites", "Prune heavily infested leaves" ], 'prevention': [ "Keep plants well-watered", "Increase humidity", "Use reflective mulches", "Avoid excessive nitrogen fertilization" ] }, 'Tomato_Target_Spot': { 'cure': [ "Remove infected leaves", "Apply fungicides (chlorothalonil or copper-based)", "Improve air circulation" ], 'prevention': [ "Improve air circulation", "Avoid overhead watering", "Practice crop rotation", "Remove plant debris after harvest" ] }, 'Tomato_Tomato_Yellow_Leaf_Curl_Virus': { 'cure': [ "No cure available", "Remove and destroy infected plants", "Control whitefly population" ], 'prevention': [ "Use resistant varieties", "Control whiteflies with insecticides or traps", "Use reflective mulches", "Plant early in the season" ] }, 'Tomato_Tomato_mosaic_virus': { 'cure': [ "No cure available", "Remove and destroy infected plants", "Control aphid population" ], 'prevention': [ "Use disease-free seeds", "Disinfect tools and hands", "Control aphids", "Avoid working with plants when wet" ] }, 'Tomato_healthy': { 'cure': [ "No treatment needed" ], 'prevention': [ "Maintain good soil health", "Water consistently", "Provide adequate sunlight", "Monitor for early signs of pests or diseases" ] } } # Function to load and preprocess the image def load_and_prep_image(image): try: img = image.resize((224, 224)) # Assuming your model expects 224x224 images img = np.array(img) / 255.0 # Normalize the image img = np.expand_dims(img, axis=0) # Add batch dimension return img except Exception as e: st.error(f"Error preprocessing image: {e}") return None # Prediction and results display if uploaded_file is not None: with col1: image = Image.open(uploaded_file) st.image(image, caption='Uploaded Image', use_column_width=True) with col2: st.markdown('Analysis Results
', unsafe_allow_html=True) with st.spinner('Analyzing image... 🔍'): # Preprocess the image prepped_image = load_and_prep_image(image) # Ensure the model is loaded and image is preprocessed before making a prediction if VIT is not None and prepped_image is not None: # Make prediction prediction = VIT.predict(prepped_image) predicted_class = class_names[np.argmax(prediction)] confidence = np.max(prediction) * 100 # Display the prediction with a progress bar st.write(f"Detected Disease: **{predicted_class}**") st.progress(confidence / 100) st.write(f"Confidence: {confidence:.2f}%") # Create a pie chart for top 3 predictions top_3_idx = np.argsort(prediction[0])[-3:][::-1] top_3_values = prediction[0][top_3_idx] * 100 top_3_labels = [class_names[i] for i in top_3_idx] fig = go.Figure(data=[go.Pie(labels=top_3_labels, values=top_3_values, hole=.3)]) fig.update_layout(title_text="Top 3 Predictions") st.plotly_chart(fig) # Display cure and prevention suggestions if predicted_class in disease_info: st.markdown("### 🏥 Cure:") for cure_point in disease_info[predicted_class]['cure']: st.markdown(f"- {cure_point}") st.markdown("### 🛡️ Prevention:") for prevention_point in disease_info[predicted_class]['prevention']: st.markdown(f"- {prevention_point}") else: st.write("No specific cure or prevention information available for this condition.") # Add a "Get Expert Help" button if st.button("Get Expert Help"): st.markdown("### 👩🌾 Expert Consultation") st.write("Our team of agricultural experts is ready to assist you. Please provide the following information:") with st.form("expert_help_form"): name = st.text_input("Your Name") email = st.text_input("Your Email") description = st.text_area("Describe your problem in detail") submitted = st.form_submit_button("Submit") if submitted: st.success("Thank you! Our experts will contact you within 24 hours.") # Add a fun fact about tomatoes st.markdown("---") st.markdown("### 🍅 Fun Tomato Fact") fun_facts = [ "Tomatoes are actually fruits, not vegetables!", "The fear of tomatoes is called Lycopersicophobia.", "The world's largest tomato tree produces more than 32,000 tomatoes a year!", "There are over 10,000 varieties of tomatoes worldwide.", "The first tomatoes discovered by Europeans were yellow, hence the Italian name 'pomodoro' (golden apple)." ] st.write(np.random.choice(fun_facts)) else: st.write("Please upload an image to get started!") # Footer st.markdown("---")