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import streamlit as st |
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import matplotlib.pyplot as plt |
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import tensorflow as tf |
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from tensorflow.keras.models import load_model |
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@st.cache_resource |
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def loadu(): |
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reso = load_model("model.h5") |
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return reso |
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loaded_model = loadu() |
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class_names = ['Apple___Apple_scab', 'Apple___Black_rot', 'Apple___Cedar_apple_rust', 'Apple___healthy', |
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'Blueberry___healthy', 'Cherry_(including_sour)___Powdery_mildew', |
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'Cherry_(including_sour)___healthy', 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot', |
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'Corn_(maize)___Common_rust_', 'Corn_(maize)___Northern_Leaf_Blight', 'Corn_(maize)___healthy', |
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'Grape___Black_rot', 'Grape___Esca_(Black_Measles)', 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)', |
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'Grape___healthy', 'Orange___Haunglongbing_(Citrus_greening)', 'Peach___Bacterial_spot', |
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'Peach___healthy', 'Pepper,_bell___Bacterial_spot', 'Pepper,_bell___healthy', 'Potato___Early_blight', |
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'Potato___Late_blight', 'Potato___healthy', 'Raspberry___healthy', 'Soybean___healthy', |
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'Squash___Powdery_mildew', 'Strawberry___Leaf_scorch', 'Strawberry___healthy', |
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'Tomato___Bacterial_spot', 'Tomato___Early_blight', 'Tomato___Late_blight', 'Tomato___Leaf_Mold', |
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'Tomato___Septoria_leaf_spot', 'Tomato___Spider_mites Two-spotted_spider_mite', 'Tomato___Target_Spot', |
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'Tomato___Tomato_Yellow_Leaf_Curl_Virus', 'Tomato___Tomato_mosaic_virus', 'Tomato___healthy'] |
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crop_medicines = { |
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'Apple___Apple_scab': ['Fungicides (e.g., sulfur or copper-based products)', 'Sanitation practices'], |
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'Apple___Black_rot': ['Fungicides', 'Proper sanitation practices'], |
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'Apple___Cedar_apple_rust': ['Fungicides', 'Removal of infected juniper plants'], |
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'Apple___healthy': ['No specific treatment'], |
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'Blueberry___healthy': ['Well-drained soil', 'Proper irrigation'], |
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'Cherry_(including_sour)___Powdery_mildew': ['Fungicides', 'Pruning for air circulation', 'Proper sanitation'], |
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'Cherry_(including_sour)___healthy': ['No specific treatment'], |
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'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot': ['Fungicides', 'Crop rotation'], |
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'Corn_(maize)___Common_rust_': ['Fungicides', 'Resistant varieties'], |
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'Corn_(maize)___Northern_Leaf_Blight': ['Fungicides', 'Crop rotation'], |
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'Corn_(maize)___healthy': ['No specific treatment'], |
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'Grape___Black_rot': ['Fungicides', 'Proper pruning'], |
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'Grape___Esca_(Black_Measles)': ['Pruning', 'Cultural practices'], |
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'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)': ['Fungicides', 'Proper canopy management'], |
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'Grape___healthy': ['No specific treatment'], |
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'Orange___Haunglongbing_(Citrus_greening)': ['Vector control', 'Removing infected trees'], |
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'Peach___Bacterial_spot': ['Copper-based fungicides', 'Proper sanitation'], |
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'Peach___healthy': ['No specific treatment'], |
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'Pepper,_bell___Bacterial_spot': ['Copper-based fungicides', 'Resistant varieties'], |
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'Pepper,_bell___healthy': ['No specific treatment'], |
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'Potato___Early_blight': ['Fungicides', 'Crop rotation', 'Proper field hygiene'], |
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'Potato___Late_blight': ['Fungicides', 'Resistant varieties', 'Proper plant spacing'], |
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'Potato___healthy': ['No specific treatment'], |
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'Raspberry___healthy': ['Well-drained soil', 'Proper pruning'], |
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'Soybean___healthy': ['Proper crop rotation'], |
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'Squash___Powdery_mildew': ['Fungicides', 'Proper spacing for air circulation'], |
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'Strawberry___Leaf_scorch': ['Fungicides', 'Proper irrigation'], |
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'Strawberry___healthy': ['No specific treatment'], |
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'Tomato___Bacterial_spot': ['Copper-based fungicides', 'Resistant varieties'], |
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'Tomato___Early_blight': ['Fungicides', 'Resistant varieties', 'Proper plant spacing'], |
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'Tomato___Late_blight': ['Fungicides', 'Resistant varieties', 'Proper plant spacing'], |
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'Tomato___Leaf_Mold': ['Fungicides', 'Proper ventilation'], |
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'Tomato___Septoria_leaf_spot': ['Fungicides', 'Proper plant spacing'], |
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'Tomato___Spider_mites Two-spotted_spider_mite': ['Miticides', 'Biological control'], |
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'Tomato___Target_Spot': ['Fungicides', 'Proper plant hygiene'], |
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'Tomato___Tomato_Yellow_Leaf_Curl_Virus': ['Vector control', 'Resistant varieties'], |
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'Tomato___Tomato_mosaic_virus': ['Resistant varieties', 'Vector control'], |
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'Tomato___healthy': ['No specific treatment'] |
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} |
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IMAGE_SIZE = (224, 224) |
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def load_and_preprocess_image(image_path): |
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try: |
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img = tf.io.read_file(image_path) |
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img = tf.image.decode_image(img) |
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img = tf.image.resize(img, size=IMAGE_SIZE) |
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return img |
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except Exception as e: |
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st.error(f"Error loading or preprocessing image: {e}") |
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return None |
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def disease_predict(image_path): |
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image = load_and_preprocess_image(image_path) |
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if image is not None: |
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try: |
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pred = loaded_model.predict(tf.expand_dims(image, axis=0)) |
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if max(pred[0]) >= 0.90: |
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predicted_value = class_names[pred.argmax()] |
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display_prediction(predicted_value, image) |
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else: |
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st.error("Invalid Image") |
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except Exception as e: |
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st.error(f"Error predicting disease: {e}") |
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def display_prediction(predicted_value, image): |
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st.image(image.numpy() / 255., caption=f"Predicted Disease: {predicted_value}", use_column_width=True) |
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st.success(f"Disease: **{predicted_value }**") |
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treatment(predicted_value) |
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def treatment(predicted_value): |
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medicines = crop_medicines.get(predicted_value, []) |
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st.subheader("Solution for this Disease:") |
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st.write("Medicines:") |
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for medicine in medicines: |
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st.write(f"- {medicine}") |
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def disease_app(): |
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st.title('Agricultural Disease Detector 🦠') |
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uploaded_file = st.file_uploader("Upload an Image for Disease Analysis", type="jpg") |
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if uploaded_file is None: |
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uploaded_file = st.camera_input("Capture a photo") |
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if uploaded_file is not None: |
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img_path = f"uploaded_image.jpg" |
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with open(img_path, "wb") as f: |
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f.write(uploaded_file.read()) |
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disease_predict(img_path) |
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