Update app.py

app.py

@@ -1,158 +1 @@
-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 = 'apple.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)
-        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 == 'Cedar Apple Rust':
-            pesticide_info = """
-                <h2><center><b>Cedar Apple Rust</b></center></h2>
-                <h4>PESTICIDES TO BE USED:</h4><br>
-                
-                <ul style="font-size:17px;margin-left:40px;">
-                    <li>1. Chlorothalonil (Daconil)</li>
-                    <li>2. Mancozeb (Dithane)</li>
-                    <li>3. Propiconazole</li>
-                    <li>4. Azoxystrobin (Heritage)</li>
-                    <li>5. Pyraclostrobin (Cabrio)</li>
-                </ul><br>
-                <center><p class="note" style="font-size:15px;"><b>* * * IMPORTANT NOTE * * *</b></p></center><br>
-                <center><p style="font-size:13px;">Be sure to follow local regulations and guidelines for application</p></center>
-            """
-        elif predicted_label == 'Apple Scrab':
-            pesticide_info = """<h2><center><b>Apple Scrab</b></center></h2>
-                <h4>PESTICIDES TO BE USED:</h4><br>
-                <ul style="font-size:17px;margin-left:40px;">
-                    <li>1. Chlorothalonil (Daconil)</li>
-                    <li>2. Mancozeb (Dithane)</li>
-                    <li>3. Propiconazole</li>
-                    <li>4. Azoxystrobin (Heritage)</li>
-                    <li>5. Pyraclostrobin (Cabrio)</li>
-                </ul><br>
-                <center><p class="note" style="font-size:15px;"><b>* * * IMPORTANT NOTE * * *</b></p></center><br>
-                <center><p style="font-size:13px;">Be sure to follow local regulations and guidelines for application</p></center>
-            """
-        elif predicted_label == 'Apple Black Rot':
-            pesticide_info = """<h2><center><b>Apple Black Rot</b></center></h2>
-                <h4>PESTICIDES TO BE USED:</h4><br>
-                <ul style="font-size:17px;margin-left:40px;">
-                    <li>1. Chlorothalonil (Daconil)</li>
-                    <li>2. Mancozeb (Dithane)</li>
-                    <li>3. Propiconazole</li>
-                    <li>4. Azoxystrobin (Heritage)</li>
-                    <li>5. Pyraclostrobin (Cabrio)</li>
-                </ul><br>
-                <center><p class="note" style="font-size:15px;"><b>* * * IMPORTANT NOTE * * *</b></p></center><br>
-                <center><p style="font-size:13px;">Be sure to follow local regulations and guidelines for application</p></center>
-            """
-        
-            
-        elif predicted_label == 'Apple Healthy':
-            pesticide_info = """<h2><center><b>Apple Healthy</b></center></h2>
-            <h5> No pesticides needed"""
-        
-        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"<h3>Error: {e}</h3>"
-
-# List of class labels
-all_labels = [
-    'Cedar Apple Rust',
-    'Apple Scrab',
-    'Apple Healthy',
-    'Apple Black Rot'
-]
-
-# 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="Apple 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()
+print("hello")