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| import streamlit as st | |
| import tensorflow as tf | |
| import numpy as np | |
| from PIL import Image | |
| import json | |
| import plotly.graph_objects as go | |
| from datetime import datetime | |
| import pandas as pd | |
| # Load class indices | |
| with open("class_indices.json", "r") as f: | |
| class_indices = json.load(f) | |
| # Reverse the mapping for predictions | |
| class_names = {v: k for k, v in class_indices.items()} | |
| # Load the TFLite model | |
| def load_model(): | |
| interpreter = tf.lite.Interpreter(model_path="model.tflite") | |
| interpreter.allocate_tensors() | |
| return interpreter | |
| interpreter = load_model() | |
| input_details = interpreter.get_input_details() | |
| output_details = interpreter.get_output_details() | |
| # Define the image preprocessing function | |
| def preprocess_image(image, target_size=(224, 224)): | |
| image = image.resize(target_size) | |
| image = np.array(image) / 255.0 | |
| image = np.expand_dims(image, axis=0) | |
| return image.astype(np.float32) | |
| # Define prediction function with detailed output | |
| def predict(image): | |
| input_data = preprocess_image(image) | |
| interpreter.set_tensor(input_details[0]['index'], input_data) | |
| interpreter.invoke() | |
| output_data = interpreter.get_tensor(output_details[0]['index']) | |
| # Get top 3 predictions | |
| top_indices = np.argsort(output_data[0])[-3:][::-1] | |
| predictions = [] | |
| for idx in top_indices: | |
| predictions.append({ | |
| 'class': class_names[idx], | |
| 'confidence': float(output_data[0][idx]) | |
| }) | |
| return predictions | |
| # Custom CSS | |
| st.set_page_config( | |
| page_title="🌿 Smart Crop Disease Detective", | |
| page_icon="🔬", | |
| layout="wide", | |
| initial_sidebar_state="expanded", | |
| ) | |
| # Custom CSS | |
| st.markdown(""" | |
| <style> | |
| .main { | |
| background-color: #f5f7f9; | |
| } | |
| .stButton>button { | |
| background-color: #2d6a4f; | |
| color: white; | |
| border-radius: 10px; | |
| padding: 0.5rem 1rem; | |
| border: none; | |
| width: 100%; | |
| } | |
| .stButton>button:hover { | |
| background-color: #40916c; | |
| border: none; | |
| } | |
| .prediction-box { | |
| background-color: white; | |
| padding: 20px; | |
| border-radius: 10px; | |
| box-shadow: 0 2px 4px rgba(0,0,0,0.1); | |
| } | |
| .info-box { | |
| background-color: #e9ecef; | |
| padding: 15px; | |
| border-radius: 5px; | |
| margin: 10px 0; | |
| } | |
| .status-box { | |
| padding: 10px; | |
| border-radius: 5px; | |
| margin: 10px 0; | |
| text-align: center; | |
| } | |
| .header-container { | |
| display: flex; | |
| align-items: center; | |
| justify-content: space-between; | |
| padding: 1rem; | |
| background-color: white; | |
| border-radius: 10px; | |
| margin-bottom: 2rem; | |
| box-shadow: 0 2px 4px rgba(0,0,0,0.1); | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Session State initialization | |
| if 'prediction_history' not in st.session_state: | |
| st.session_state.prediction_history = [] | |
| # App Header | |
| col1, col2, col3 = st.columns([1,2,1]) | |
| with col2: | |
| st.markdown(""" | |
| <div style='text-align: center'> | |
| <h1 style='color: #2d6a4f'>🌿 Smart Crop Disease Detective</h1> | |
| <p style='color: #40916c; font-size: 1.2em;'> | |
| Your AI-Powered Assistant for Crop Health Monitoring | |
| </p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Sidebar | |
| with st.sidebar: | |
| st.image("https://via.placeholder.com/250x150?text=Smart+Crop+AI", use_column_width=True) | |
| st.markdown("### 📊 Dashboard") | |
| total_scans = len(st.session_state.prediction_history) | |
| st.metric("Total Scans", total_scans) | |
| st.markdown("### 🎯 Features") | |
| st.markdown(""" | |
| - 🔍 Real-time disease detection | |
| - 📊 Confidence scoring | |
| - 📈 Multiple disease possibilities | |
| - 💾 Scan history tracking | |
| - 🌱 Treatment recommendations | |
| """) | |
| st.markdown("### 💡 Tips for Best Results") | |
| st.info(""" | |
| 1. Ensure good lighting | |
| 2. Focus on affected areas | |
| 3. Avoid blurry images | |
| 4. Include multiple angles | |
| 5. Clean lens before capture | |
| """) | |
| if st.button("Clear History"): | |
| st.session_state.prediction_history = [] | |
| st.success("History cleared!") | |
| # Main Content | |
| main_col1, main_col2 = st.columns([2,3]) | |
| with main_col1: | |
| st.markdown("### 📸 Upload Image") | |
| uploaded_file = st.file_uploader( | |
| "Choose a leaf image (JPG/PNG)", | |
| type=["jpg", "png", "jpeg"], | |
| help="Upload a clear image of the affected crop leaf" | |
| ) | |
| if uploaded_file: | |
| image = Image.open(uploaded_file) | |
| st.image(image, caption="Uploaded Image", use_column_width=True) | |
| analyze_btn = st.button("🔍 Analyze Image") | |
| if analyze_btn: | |
| with st.spinner("🔄 Analyzing image..."): | |
| predictions = predict(image) | |
| # Store prediction in history | |
| timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") | |
| st.session_state.prediction_history.append({ | |
| 'timestamp': timestamp, | |
| 'predictions': predictions, | |
| 'filename': uploaded_file.name | |
| }) | |
| with main_col2: | |
| if uploaded_file and analyze_btn: | |
| st.markdown("### 🔍 Analysis Results") | |
| # Display confidence gauge for top prediction | |
| fig = go.Figure(go.Indicator( | |
| mode = "gauge+number", | |
| value = predictions[0]['confidence'] * 100, | |
| domain = {'x': [0, 1], 'y': [0, 1]}, | |
| title = {'text': "Confidence Level"}, | |
| gauge = { | |
| 'axis': {'range': [None, 100]}, | |
| 'bar': {'color': "#2d6a4f"}, | |
| 'steps': [ | |
| {'range': [0, 50], 'color': "#ff9999"}, | |
| {'range': [50, 75], 'color': "#ffff99"}, | |
| {'range': [75, 100], 'color': "#99ff99"} | |
| ] | |
| } | |
| )) | |
| st.plotly_chart(fig) | |
| # Display predictions | |
| for i, pred in enumerate(predictions, 1): | |
| confidence_color = ( | |
| "#ff0000" if pred['confidence'] < 0.5 | |
| else "#ffa500" if pred['confidence'] < 0.7 | |
| else "#008000" | |
| ) | |
| st.markdown(f""" | |
| <div class="prediction-box"> | |
| <h4>Prediction {i}: {pred['class']}</h4> | |
| <p style='color: {confidence_color}'> | |
| Confidence: {pred['confidence']*100:.2f}% | |
| </p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Treatment Recommendations (example) | |
| st.markdown("### 💊 Treatment Recommendations") | |
| st.markdown(f""" | |
| <div class="info-box"> | |
| <h4>For {predictions[0]['class']}:</h4> | |
| <ul> | |
| <li>Isolate affected plants</li> | |
| <li>Apply appropriate fungicide/pesticide</li> | |
| <li>Improve air circulation</li> | |
| <li>Monitor moisture levels</li> | |
| </ul> | |
| <p><em>Consult with a local agricultural expert for specific treatment plans.</em></p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # History Section | |
| if st.session_state.prediction_history: | |
| st.markdown("### 📜 Scan History") | |
| history_df = pd.DataFrame([ | |
| { | |
| 'Timestamp': h['timestamp'], | |
| 'Filename': h['filename'], | |
| 'Primary Prediction': h['predictions'][0]['class'], | |
| 'Confidence': f"{h['predictions'][0]['confidence']*100:.2f}%" | |
| } | |
| for h in st.session_state.prediction_history | |
| ]) | |
| st.dataframe(history_df, use_container_width=True) | |
| # Footer | |
| st.markdown(""" | |
| <div style='text-align: center; color: gray; padding: 20px;'> | |
| <p>Developed with ❤️ for Final Yr Project</p> | |
| <p>Version 2.0 | Last Updated: 2024</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |