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Update app.py
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app.py
CHANGED
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import gradio as gr
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from tensorflow.keras.utils import img_to_array,load_img
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from keras.models import load_model
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import numpy as np
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# Load the pre-trained model from the local path
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model_path = 'Mango.h5'
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model = load_model(model_path) # Load the model here
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try:
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# Load and preprocess the image
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img_array = img_to_array(img)
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img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
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img_array = img_array / 255.0 # Normalize the image
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# Predict the class
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predictions = model.predict(img_array)
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predicted_class = np.argmax(predictions[0])
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# Get the predicted class label
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predicted_label = all_labels[predicted_class]
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# Print the predicted label to the console
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font-size: 17px;
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margin-top: 15px;
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}
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h4:hover{
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cursor: pointer;
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}
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h3:hover{
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cursor: pointer;
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color: blue;
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transform: scale(1.3);
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}
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.note{
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text-align: center;
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font-size: 16px;
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}
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p{
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font-size: 13px;
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text-align: center;
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}
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</style>
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<h3><center><b>Mango Anthracrose</b></center></h3>
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<h4>PESTICIDES TO BE USED:</h4>
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<ul>
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<li>1. Mancozeb</li>
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<li>2. Azoxystrobin</li>
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<li>3.
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<li>4. Propiconazole</li>
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<li>5. Thiophanate-methyl</li>
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<li>6. Copper Sulfate</li>
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</ul><br>
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<p class="note"><b>* * * IMPORTANT NOTE * * *</b></p>
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<p>Be sure to follow local regulations and guidelines for application</p>
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"""
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elif predicted_label=='Mango Bacterial Canker':
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font-size: 15px;
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margin-left: 90px;
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margin-top: 15px;
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margin-bottom: 15px;
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}
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h4{
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font-size: 17px;
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margin-top: 15px;
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}
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h4:hover{
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cursor: pointer;
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}
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h3:hover{
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cursor: pointer;
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color: blue;
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transform: scale(1.3);
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}
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.note{
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text-align: center;
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font-size: 16px;
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}
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p{
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font-size: 13px;
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text-align: center;
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}
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</style>
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<h3><center><b>Mango Bacterial Canker</b></center></h3>
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<h4>PESTICIDES TO BE USED:</h4>
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<ul>
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<li>1. Copper Hydroxide</li>
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<li>2. Copper Oxychloride</li>
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<li>3. Streptomycin</li>
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<li>4. oxytetracycline</li>
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<li>5. Neem oil</li>
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<li>6. Garlic oil</li>
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</ul>
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<p class="note"><b>* * * IMPORTANT NOTE * * *</b></p>
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<p>Be sure to follow local regulations and guidelines for application</p>
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"""
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elif predicted_label=='Mango Cutting Weevil':
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font-size: 15px;
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margin-left: 90px;
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margin-top: 15px;
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margin-bottom: 15px;
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}
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h4{
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font-size: 17px;
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margin-top: 15px;
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}
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h4:hover{
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cursor: pointer;
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}
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h3:hover{
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cursor: pointer;
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color: blue;
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transform: scale(1.3);
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}
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.note{
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text-align: center;
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font-size: 16px;
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}
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p{
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font-size: 13px;
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text-align: center;
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}
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</style>
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<h3><center><b>Mango Cutting Weevil</b></center></h3>
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<h4>PESTICIDES TO BE USED:</h4>
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<ul>
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<li>1. Imidacloprid</li>
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<li>2. Thiamethoxam</li>
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<li>3. Chlorpyrifos</li>
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<li>4. Lambda-cyhalothrin</li>
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<li>5. Fipronil</li>
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<li>6. Neem oil</li>
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</ul>
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<p class="note"><b>* * * IMPORTANT NOTE * * *</b></p>
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<p>Be sure to follow local regulations and guidelines for application</p>
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"""
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li{
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font-size: 15px;
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margin-left: 90px;
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margin-top: 15px;
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margin-bottom: 15px;
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}
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h4{
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font-size: 17px;
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margin-top: 15px;
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}
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h4:hover{
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cursor: pointer;
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}
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h3:hover{
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cursor: pointer;
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color: blue;
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transform: scale(1.3);
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}
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.note{
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text-align: center;
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font-size: 16px;
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}
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p{
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font-size: 13px;
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text-align: center;
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}
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</style>
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<h3><center><b>Mango Die Back</b></center></h3>
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<h4>PESTICIDES TO BE USED:</h4>
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<ul>
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<li>1. Carbendazim</li>
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<li>2. Mancozeb</li>
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<li>3. Azoxystrobin</li>
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<li>5. Potassium bicarbonate</li>
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<li>6. Sodium bicarbonate</li>
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</ul>
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<p
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"""
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elif predicted_label=='Mango Gall Midge':
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font-size: 15px;
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margin-left: 90px;
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margin-top: 15px;
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margin-bottom: 15px;
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}
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h4{
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font-size: 17px;
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margin-top: 15px;
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}
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h4:hover{
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cursor: pointer;
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}
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h3:hover{
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cursor: pointer;
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color: blue;
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transform: scale(1.3);
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}
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.note{
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text-align: center;
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font-size: 16px;
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}
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p{
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font-size: 13px;
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text-align: center;
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}
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</style>
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<h3><center><b>Mango Gall Midge</b></center></h3>
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<h4>PESTICIDES TO BE USED:</h4>
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<ul>
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<li>1. Imidacloprid</li>
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<li>2. Thiamethoxam</li>
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<li>3. Chlorpyrifos</li>
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<li>5. Spinosad</li>
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<li>6. Pyrethrin</li>
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</ul>
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"""
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margin-bottom: 15px;
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}
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h4{
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font-size: 17px;
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margin-top: 15px;
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}
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h4:hover{
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cursor: pointer;
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}
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h3:hover{
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cursor: pointer;
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color: blue;
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transform: scale(1.3);
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}
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.note{
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text-align: center;
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font-size: 16px;
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}
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p{
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font-size: 13px;
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text-align: center;
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}
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</style>
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<h3><center><b>Mango Powdery Mildew</b></center></h3>
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<h4>PESTICIDES TO BE USED:</h4>
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<ul>
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<li>1. Sulfur</li>
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<li>2. Bicarbonates</li>
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<li>3. Myclobutanil</li>
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<li>5. Propiconazole</li>
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<li>6. Azoxystrobin</li>
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</ul>
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"""
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<style>
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li{
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font-size: 15px;
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margin-left: 90px;
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margin-top: 15px;
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margin-bottom: 15px;
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}
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h4{
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font-size: 17px;
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margin-top: 15px;
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}
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h4:hover{
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cursor: pointer;
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}
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h3:hover{
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cursor: pointer;
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color: blue;
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transform: scale(1.3);
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}
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.note{
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text-align: center;
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font-size: 16px;
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}
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p{
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font-size: 13px;
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text-align: center;
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}
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</style>
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<h3><center><b>Mango Sooty Mould</b></center></h3>
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<h4>PESTICIDES TO BE USED:</h4>
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<ul>
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<li>1. Imidacloprid (Neonicotinoid)</li>
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<li>2. Thiamethoxam (Neonicotinoid)</li>
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<li>3. Bifenthrin (Pyrethroid)</li>
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<li>5. Insecticidal soap</li>
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<li>6. Horticultural oil</li>
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</ul>
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"""
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else:
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except Exception as e:
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print(f"Error: {e}")
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return
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# List of class labels
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all_labels = [
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'Mango Sooty Mould'
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]
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# Define the Gradio interface
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def gradio_predict(image_file):
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# Create
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gr_interface = gr.Interface(
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fn=gradio_predict,
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inputs=
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)
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# Launch the Gradio app
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gr_interface.launch()
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import h5py
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import gradio as gr
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from tensorflow.keras.utils import img_to_array, load_img
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from keras.models import load_model
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import numpy as np
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from deep_translator import GoogleTranslator
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# Load the pre-trained model from the local path
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model_path = 'Mango.h5'
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# Check if the model is loading correctly
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try:
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with h5py.File(model_path, 'r+') as f:
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if 'groups' in f.attrs['model_config']:
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model_config_string = f.attrs['model_config']
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model_config_string = model_config_string.replace('"groups": 1,', '')
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model_config_string = model_config_string.replace('"groups": 1}', '}')
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f.attrs['model_config'] = model_config_string.encode('utf-8')
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model = load_model(model_path)
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print("Model loaded successfully.")
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except Exception as e:
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print(f"Error loading model: {e}")
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def predict_disease(image_file, model, all_labels, target_language):
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try:
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# Load and preprocess the image
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print(f"Received image file: {image_file}")
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img = load_img(image_file, target_size=(224, 224)) # Ensure image size matches model input
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img_array = img_to_array(img)
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img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
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img_array = img_array / 255.0 # Normalize the image
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# Predict the class
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predictions = model.predict(img_array)
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predicted_class = np.argmax(predictions[0])
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# Get the predicted class label
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predicted_label = all_labels[predicted_class]
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# Translate the predicted label to the selected language
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translated_label = GoogleTranslator(source='en', target=target_language).translate(predicted_label)
|
| 43 |
+
|
| 44 |
+
# Provide pesticide information based on the predicted label
|
| 45 |
+
if predicted_label == 'Mango Anthracrose':
|
| 46 |
+
pesticide_info = """
|
| 47 |
+
<h2><center><b>Mango Anthracrose</b></center></h2>
|
| 48 |
+
<h4>PESTICIDES TO BE USED:</h4><br>
|
| 49 |
+
<ul style="font-size:17px;margin-left:40px;">
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| 50 |
<li>1. Mancozeb</li>
|
| 51 |
<li>2. Azoxystrobin</li>
|
| 52 |
+
<li>3. Carbendazim</li>
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| 53 |
<li>4. Propiconazole</li>
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| 54 |
<li>5. Thiophanate-methyl</li>
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| 55 |
<li>6. Copper Sulfate</li>
|
| 56 |
</ul><br>
|
| 57 |
+
<center><p class="note" style="font-size:15px;"><b>* * * IMPORTANT NOTE * * *</b></p></center><br>
|
| 58 |
+
<center><p style="font-size:13px;">Be sure to follow local regulations and guidelines for application</p></center>
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| 59 |
"""
|
| 60 |
+
elif predicted_label == 'Mango Bacterial Canker':
|
| 61 |
+
pesticide_info = """<h2><center><b>Mango Bacterial Canker</b></center></h2>
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| 62 |
+
<h4>PESTICIDES TO BE USED:</h4><br>
|
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+
<ul style="font-size:17px;margin-left:40px;">
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| 64 |
<li>1. Copper Hydroxide</li>
|
| 65 |
<li>2. Copper Oxychloride</li>
|
| 66 |
<li>3. Streptomycin</li>
|
| 67 |
<li>4. oxytetracycline</li>
|
| 68 |
<li>5. Neem oil</li>
|
| 69 |
<li>6. Garlic oil</li>
|
| 70 |
+
</ul><br>
|
| 71 |
+
<center><p class="note" style="font-size:15px;"><b>* * * IMPORTANT NOTE * * *</b></p></center><br>
|
| 72 |
+
<center><p style="font-size:13px;">Be sure to follow local regulations and guidelines for application</p></center>
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|
| 73 |
"""
|
| 74 |
+
elif predicted_label == 'Mango Cutting Weevil':
|
| 75 |
+
pesticide_info = """<h2><center><b>Mango Cutting Weevil</b></center></h2>
|
| 76 |
+
<h4>PESTICIDES TO BE USED:</h4><br>
|
| 77 |
+
<ul style="font-size:17px;margin-left:40px;">
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| 78 |
<li>1. Imidacloprid</li>
|
| 79 |
<li>2. Thiamethoxam</li>
|
| 80 |
<li>3. Chlorpyrifos</li>
|
| 81 |
<li>4. Lambda-cyhalothrin</li>
|
| 82 |
<li>5. Fipronil</li>
|
| 83 |
<li>6. Neem oil</li>
|
| 84 |
+
</ul><br>
|
| 85 |
+
<center><p class="note" style="font-size:15px;"><b>* * * IMPORTANT NOTE * * *</b></p></center><br>
|
| 86 |
+
<center><p style="font-size:13px;">Be sure to follow local regulations and guidelines for application</p></center>
|
| 87 |
|
| 88 |
|
| 89 |
"""
|
| 90 |
+
elif predicted_label == 'Mango Die Back':
|
| 91 |
+
pesticide_info = """<h2><center><b>Mango Die Back</b></center></h2>
|
| 92 |
+
<h4>PESTICIDES TO BE USED:</h4><br>
|
| 93 |
+
<ul style="font-size:17px;margin-left:40px;">
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|
| 94 |
<li>1. Carbendazim</li>
|
| 95 |
<li>2. Mancozeb</li>
|
| 96 |
<li>3. Azoxystrobin</li>
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|
| 98 |
<li>5. Potassium bicarbonate</li>
|
| 99 |
<li>6. Sodium bicarbonate</li>
|
| 100 |
</ul>
|
| 101 |
+
<br>
|
| 102 |
+
<center><p class="note" style="font-size:15px;"><b>* * * IMPORTANT NOTE * * *</b></p></center><br>
|
| 103 |
+
<center><p style="font-size:13px;">Be sure to follow local regulations and guidelines for application</p></center>
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|
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|
| 104 |
"""
|
| 105 |
+
elif predicted_label == 'Mango Gall Midge':
|
| 106 |
+
pesticide_info = """<h2><center><b>Mango Gall Midge</b></center></h2>
|
| 107 |
+
<h4>PESTICIDES TO BE USED:</h4><br>
|
| 108 |
+
<ul style="font-size:17px;margin-left:40px;">
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|
| 109 |
<li>1. Imidacloprid</li>
|
| 110 |
<li>2. Thiamethoxam</li>
|
| 111 |
<li>3. Chlorpyrifos</li>
|
|
|
|
| 113 |
<li>5. Spinosad</li>
|
| 114 |
<li>6. Pyrethrin</li>
|
| 115 |
</ul>
|
| 116 |
+
<br>
|
| 117 |
+
<center><p class="note" style="font-size:15px;"><b>* * * IMPORTANT NOTE * * *</b></p></center><br>
|
| 118 |
+
<center><p style="font-size:13px;">Be sure to follow local regulations and guidelines for application</p></center>
|
| 119 |
|
| 120 |
|
| 121 |
"""
|
| 122 |
+
|
| 123 |
+
elif predicted_label == 'Mango Healthy':
|
| 124 |
+
pesticide_info = 'No pesticides needed'
|
| 125 |
+
elif predicted_label == 'Mango Powdery Mildew':
|
| 126 |
+
pesticide_info = """<h2><center><b>Mango Powdery Mildew</b></center></h2>
|
| 127 |
+
<h4>PESTICIDES TO BE USED:</h4><br>
|
| 128 |
+
<ul style="font-size:17px;margin-left:40px;">
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
| 129 |
<li>1. Sulfur</li>
|
| 130 |
<li>2. Bicarbonates</li>
|
| 131 |
<li>3. Myclobutanil</li>
|
|
|
|
| 133 |
<li>5. Propiconazole</li>
|
| 134 |
<li>6. Azoxystrobin</li>
|
| 135 |
</ul>
|
| 136 |
+
<br>
|
| 137 |
+
<center><p class="note" style="font-size:15px;"><b>* * * IMPORTANT NOTE * * *</b></p></center><br>
|
| 138 |
+
<center><p style="font-size:13px;">Be sure to follow local regulations and guidelines for application</p></center>
|
|
|
|
| 139 |
"""
|
| 140 |
+
elif predicted_label == 'Mango Sooty Mould':
|
| 141 |
+
pesticide_info = """<h2><center><b>Mango Sooty Mould</b></center></h2>
|
| 142 |
+
<h4>PESTICIDES TO BE USED:</h4><br>
|
| 143 |
+
<ul style="font-size:17px;margin-left:40px;">
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 144 |
<li>1. Imidacloprid (Neonicotinoid)</li>
|
| 145 |
<li>2. Thiamethoxam (Neonicotinoid)</li>
|
| 146 |
<li>3. Bifenthrin (Pyrethroid)</li>
|
|
|
|
| 148 |
<li>5. Insecticidal soap</li>
|
| 149 |
<li>6. Horticultural oil</li>
|
| 150 |
</ul>
|
| 151 |
+
<br>
|
| 152 |
+
<center><p class="note" style="font-size:15px;"><b>* * * IMPORTANT NOTE * * *</b></p></center><br>
|
| 153 |
+
<center><p style="font-size:13px;">Be sure to follow local regulations and guidelines for application</p></center>"""
|
|
|
|
|
|
|
| 154 |
else:
|
| 155 |
+
pesticide_info = 'No pesticide information available.'
|
| 156 |
+
|
| 157 |
+
print(f"Pesticide Info (Before Translation): {pesticide_info}")
|
| 158 |
|
| 159 |
+
# Translate the pesticide information to the selected language
|
| 160 |
+
translated_pesticide_info = GoogleTranslator(source='en', target=target_language).translate(pesticide_info)
|
| 161 |
+
print(f"Translated Pesticide Info: {translated_pesticide_info}")
|
| 162 |
|
| 163 |
+
# Return translated label and pesticide information with associated styling
|
| 164 |
+
predicted_label_html = f"""
|
| 165 |
+
|
| 166 |
+
{translated_pesticide_info}
|
| 167 |
+
"""
|
| 168 |
+
return predicted_label_html
|
| 169 |
|
| 170 |
except Exception as e:
|
| 171 |
+
print(f"Error during prediction: {e}")
|
| 172 |
+
return f"<h3>Error: {e}</h3>"
|
| 173 |
|
| 174 |
# List of class labels
|
| 175 |
all_labels = [
|
|
|
|
| 183 |
'Mango Sooty Mould'
|
| 184 |
]
|
| 185 |
|
| 186 |
+
# Language codes and their full names (display full names in dropdown)
|
| 187 |
+
language_choices = {
|
| 188 |
+
'hi': 'Hindi',
|
| 189 |
+
'te': 'Telugu',
|
| 190 |
+
'en': 'English',
|
| 191 |
+
'ml': 'Malayalam',
|
| 192 |
+
'ta': 'Tamil',
|
| 193 |
+
'or': 'Odia/Odisi',
|
| 194 |
+
'bn': 'Bengali',
|
| 195 |
+
'gu': 'Gujarati',
|
| 196 |
+
'kn': 'Kannada',
|
| 197 |
+
'mr': 'Marathi'
|
| 198 |
+
}
|
| 199 |
+
|
| 200 |
+
# Mapping full names back to their corresponding language code
|
| 201 |
+
full_to_code = {value: key for key, value in language_choices.items()}
|
| 202 |
+
|
| 203 |
+
# Create a dropdown of full language names, using the full name in the UI
|
| 204 |
+
languages = list(language_choices.values()) # List of full language names
|
| 205 |
+
|
| 206 |
# Define the Gradio interface
|
| 207 |
+
def gradio_predict(image_file, target_language):
|
| 208 |
+
# Map full name back to language code for translation
|
| 209 |
+
language_code = full_to_code.get(target_language, 'en')
|
| 210 |
+
return predict_disease(image_file, model, all_labels, language_code)
|
| 211 |
|
| 212 |
+
# Create the Gradio interface
|
| 213 |
gr_interface = gr.Interface(
|
| 214 |
+
fn=gradio_predict,
|
| 215 |
+
inputs=[
|
| 216 |
+
gr.Image(type="filepath"), # Image input for disease prediction
|
| 217 |
+
gr.Dropdown(label="Select language", choices=languages, value='Choose language') # Language selection dropdown with full names
|
| 218 |
+
],
|
| 219 |
+
outputs="html", # Output will be in HTML (translated text)
|
| 220 |
+
title="Plant Disease Predictor",
|
| 221 |
+
description="Upload an image of a plant to predict the disease and get the translated label and pesticide information in the selected language."
|
| 222 |
)
|
| 223 |
|
| 224 |
# Launch the Gradio app
|
| 225 |
+
gr_interface.launch()
|