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| import gradio as gr | |
| from tensorflow.keras.utils import img_to_array,load_img | |
| from keras.models import load_model | |
| import numpy as np | |
| # Load the pre-trained model from the local path | |
| model_path = 'citrus.h5' | |
| model = load_model(model_path) # Load the model here | |
| def predict_disease(image_file, model, all_labels): | |
| try: | |
| # Load and preprocess the image | |
| img = load_img(image_file, target_size=(224, 224)) # Use load_img from tensorflow.keras.utils | |
| img_array = img_to_array(img) | |
| img_array = np.expand_dims(img_array, axis=0) # Add batch dimension | |
| img_array = img_array / 255.0 # Normalize the image | |
| # Predict the class | |
| predictions = model.predict(img_array) # Use the loaded model here | |
| predicted_class = np.argmax(predictions[0]) | |
| # Get the predicted class label | |
| predicted_label = all_labels[predicted_class] | |
| # Print the predicted label to the console | |
| if predicted_label=='Citrus Healthy': | |
| predicted_label = predicted_label = """<h3 align="center">Citrus Healthy</h3><br><br> | |
| <center>No need use Pesticides</center>""" | |
| elif predicted_label=='Citrus Greening': | |
| predicted_label = """ | |
| <style> | |
| li{ | |
| font-size: 15px; | |
| margin-left: 90px; | |
| margin-top: 15px; | |
| margin-bottom: 15px; | |
| } | |
| h4{ | |
| font-size: 17px; | |
| margin-top: 15px; | |
| } | |
| h4:hover{ | |
| cursor: pointer; | |
| } | |
| h3:hover{ | |
| cursor: pointer; | |
| color: blue; | |
| transform: scale(1.3); | |
| } | |
| .note{ | |
| text-align: center; | |
| font-size: 16px; | |
| } | |
| p{ | |
| font-size: 13px; | |
| text-align: center; | |
| } | |
| </style> | |
| <h3><center><b>Citrus Greening</b></center></h3> | |
| <h4>PESTICIDES TO BE USED:</h4> | |
| <ul> | |
| <li>1. Oxytetracycline (Terramycin)</li> | |
| <li>2. Streptomycin (Streptomycin sulfate)</li> | |
| <li>3. Mancozeb (Dithane)</li> | |
| <li>4. Copper oxychloride (Kocide)</li> | |
| </ul> | |
| <p class="note"><b>* * * IMPORTANT NOTE * * *</b></p> | |
| <p>Be sure to follow local regulations and guidelines for application</p> | |
| """ | |
| elif predicted_label=='Citrus Canker': | |
| predicted_label = """ | |
| <style> | |
| li{ | |
| font-size: 15px; | |
| margin-left: 90px; | |
| margin-top: 15px; | |
| margin-bottom: 15px; | |
| } | |
| h4{ | |
| font-size: 17px; | |
| margin-top: 15px; | |
| } | |
| h4:hover{ | |
| cursor: pointer; | |
| } | |
| h3:hover{ | |
| cursor: pointer; | |
| color: blue; | |
| transform: scale(1.3); | |
| } | |
| .note{ | |
| text-align: center; | |
| font-size: 16px; | |
| } | |
| p{ | |
| font-size: 13px; | |
| text-align: center; | |
| } | |
| </style> | |
| <h3><center><b>Citrus Canker</b></center></h3> | |
| <h4>PESTICIDES TO BE USED:</h4> | |
| <ul> | |
| <li>1. Oxytetracycline (Terramycin)</li> | |
| <li>2. Streptomycin (Streptomycin sulfate)</li> | |
| <li>3. Mancozeb (Dithane)</li> | |
| <li>4. Copper oxychloride (Kocide)</li> | |
| <li>5. Azoxystrobin (Heritage)</li> | |
| </ul> | |
| <p class="note"><b>* * * IMPORTANT NOTE * * *</b></p> | |
| <p>Be sure to follow local regulations and guidelines for application</p> | |
| """ | |
| elif predicted_label=='Citrus Black Spot': | |
| predicted_label = """ | |
| <style> | |
| li{ | |
| font-size: 15px; | |
| margin-left: 90px; | |
| margin-top: 15px; | |
| margin-bottom: 15px; | |
| } | |
| h4{ | |
| font-size: 17px; | |
| margin-top: 15px; | |
| } | |
| h4:hover{ | |
| cursor: pointer; | |
| } | |
| h3:hover{ | |
| cursor: pointer; | |
| color: blue; | |
| transform: scale(1.3); | |
| } | |
| .note{ | |
| text-align: center; | |
| font-size: 16px; | |
| } | |
| p{ | |
| font-size: 13px; | |
| text-align: center; | |
| } | |
| </style> | |
| <h3><center><b>Citrus Black Spot</b></center></h3> | |
| <h4>PESTICIDES TO BE USED:</h4> | |
| <ul> | |
| <li>1. Propiconazole (Tilt)</li> | |
| <li>2. Chlorothalonil (Daconil)</li> | |
| <li>3. Mancozeb (Dithane)</li> | |
| <li>4. Azoxystrobin (Heritage)</li> | |
| <li>5. Pyraclostrobin (Cabrio)</li> | |
| </ul> | |
| <p class="note"><b>* * * IMPORTANT NOTE * * *</b></p> | |
| <p>Be sure to follow local regulations and guidelines for application</p> | |
| """ | |
| else: | |
| predicted_label = """<h3 align="center">Choose Correct image</h3><br><br> | |
| """ | |
| return predicted_label | |
| except Exception as e: | |
| print(f"Error: {e}") | |
| return None | |
| # List of class labels | |
| all_labels = [ | |
| 'Citrus Greening', | |
| 'Citrus Canker', | |
| 'Citrus Healthy','Citrus Black Spot' | |
| ] | |
| # Define the Gradio interface | |
| def gradio_predict(image_file): | |
| return predict_disease(image_file, model, all_labels) # Pass the model to the function | |
| # Create a Gradio interface | |
| gr_interface = gr.Interface( | |
| fn=gradio_predict, # Function to call for predictions | |
| inputs=gr.Image(type="filepath"), # Upload image as file path | |
| outputs="html", # Output will be the class label as text | |
| title="Citrus Disease Predictor", | |
| description="Upload an image of a plant to predict the disease.", | |
| ) | |
| # Launch the Gradio app | |
| gr_interface.launch(share=True) |