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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 = 'bell pepper.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)
# Validate predictions
confidence_threshold = 0.98 # 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"""
<h3 style="color:red; text-align:center;">
Please upload a clearer image of the plant.
</h3>
"""
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 == 'Bell pepper Bacterial Spot':
pesticide_info = """
<h2><center><b>Bell pepper Bacterial Spot</b></center></h2>
<h4>PESTICIDES TO BE USED:</h4><br>
<ul style="font-size:17px;margin-left:40px;">
<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><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 == 'Bell pepper Healthy':
pesticide_info = """<h2><center><b>Bell pepper 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 = [
'Bell pepper Healthy',
'Bell pepper Bacterial Spot'
]
# 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="Bell pepper 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()
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