import h5py import gradio as gr from tensorflow.keras.utils import img_to_array, load_img from keras.models import load_model import numpy as np from deep_translator import GoogleTranslator # Load the pre-trained model from the local path model_path = 'Mango.h5' # Check if the model is loading correctly try: with h5py.File(model_path, 'r+') as f: if 'groups' in f.attrs['model_config']: model_config_string = f.attrs['model_config'] model_config_string = model_config_string.replace('"groups": 1,', '') model_config_string = model_config_string.replace('"groups": 1}', '}') f.attrs['model_config'] = model_config_string.encode('utf-8') model = load_model(model_path) print("Model loaded successfully.") except Exception as e: print(f"Error loading model: {e}") def predict_disease(image_file, model, all_labels, target_language): try: # Load and preprocess the image print(f"Received image file: {image_file}") img = load_img(image_file, target_size=(224, 224)) # Ensure image size matches model input 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) predictions = model.predict(img_array) confidence_threshold = 0.7 # Require at least 98% confidence confidence_scores = predictions[0] max_confidence = np.max(confidence_scores) if max_confidence < confidence_threshold: print(f"Prediction confidence ({max_confidence:.2f}) is too low.") return f"""

Please upload a clearer image of the plant.

""" predicted_class = np.argmax(predictions[0]) # Get the predicted class label predicted_label = all_labels[predicted_class] # Translate the predicted label to the selected language translated_label = GoogleTranslator(source='en', target=target_language).translate(predicted_label) # Provide pesticide information based on the predicted label if predicted_label == 'Mango Anthracrose': pesticide_info = """

Mango Anthracrose

PESTICIDES TO BE USED:



* * * IMPORTANT NOTE * * *


Be sure to follow local regulations and guidelines for application

""" elif predicted_label == 'Mango Bacterial Canker': pesticide_info = """

Mango Bacterial Canker

PESTICIDES TO BE USED:



* * * IMPORTANT NOTE * * *


Be sure to follow local regulations and guidelines for application

""" elif predicted_label == 'Mango Cutting Weevil': pesticide_info = """

Mango Cutting Weevil

PESTICIDES TO BE USED:



* * * IMPORTANT NOTE * * *


Be sure to follow local regulations and guidelines for application

""" elif predicted_label == 'Mango Die Back': pesticide_info = """

Mango Die Back

PESTICIDES TO BE USED:



* * * IMPORTANT NOTE * * *


Be sure to follow local regulations and guidelines for application

""" elif predicted_label == 'Mango Gall Midge': pesticide_info = """

Mango Gall Midge

PESTICIDES TO BE USED:



* * * IMPORTANT NOTE * * *


Be sure to follow local regulations and guidelines for application

""" elif predicted_label == 'Mango Healthy': pesticide_info = """

Mango Healthy

No pesticides needed""" elif predicted_label == 'Mango Powdery Mildew': pesticide_info = """

Mango Powdery Mildew

PESTICIDES TO BE USED:



* * * IMPORTANT NOTE * * *


Be sure to follow local regulations and guidelines for application

""" elif predicted_label == 'Mango Sooty Mould': pesticide_info = """

Mango Sooty Mould

PESTICIDES TO BE USED:



* * * IMPORTANT NOTE * * *


Be sure to follow local regulations and guidelines for application

""" else: pesticide_info = 'No pesticide information available.' print(f"Pesticide Info (Before Translation): {pesticide_info}") # Translate the pesticide information to the selected language translated_pesticide_info = GoogleTranslator(source='en', target=target_language).translate(pesticide_info) print(f"Translated Pesticide Info: {translated_pesticide_info}") # Return translated label and pesticide information with associated styling predicted_label_html = f""" {translated_pesticide_info} """ return predicted_label_html except Exception as e: print(f"Error during prediction: {e}") return f"

Error: {e}

" # List of class labels all_labels = [ 'Mango Anthracrose', 'Mango Bacterial Canker', 'Mango Cutting Weevil', 'Mango Die Back', 'Mango Gall Midge', 'Mango Healthy', 'Mango Powdery Mildew', 'Mango Sooty Mould' ] # Language codes and their full names (display full names in dropdown) language_choices = { 'hi': 'Hindi', 'te': 'Telugu', 'en': 'English', 'ml': 'Malayalam', 'ta': 'Tamil', 'bn': 'Bengali', 'gu': 'Gujarati', 'kn': 'Kannada', 'mr': 'Marathi' } # Mapping full names back to their corresponding language code full_to_code = {value: key for key, value in language_choices.items()} # Create a dropdown of full language names, using the full name in the UI languages = list(language_choices.values()) # List of full language names # Define the Gradio interface def gradio_predict(image_file, target_language): # Map full name back to language code for translation language_code = full_to_code.get(target_language, 'en') return predict_disease(image_file, model, all_labels, language_code) # Create the Gradio interface gr_interface = gr.Interface( fn=gradio_predict, inputs=[ gr.Image(type="filepath"), # Image input for disease prediction gr.Dropdown(label="Select language", choices=languages, value='English') # Language selection dropdown with full names ], outputs="html", # Output will be in HTML (translated text) title="Plant Disease Predictor", description="Upload an image of a plant to predict the disease and get the translated label and pesticide information in the selected language." ) # Launch the Gradio app gr_interface.launch()