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app.py

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+import streamlit as st
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+model = load_model('cnn_model.h5')
+
+def process_image(img):
+    img = img.convert('RGB')  
+    img = img.resize((64, 64)) 
+    img = np.array(img)  
+    img = img / 255.0  
+    img = np.expand_dims(img, axis=0) 
+    return img
+
+st.title('Fruits and Vegetables Image Recognition :grapes: 	:carrot: :kiwifruit: :broccoli:')
+st.write('Upload a vegetable or fruit image and the model will recognize it.')
+
+file = st.file_uploader('Select an image', type=['jpg', 'jpeg', 'png'])
+
+if file is not None:
+    # Display the uploaded image
+    img = Image.open(file)
+    st.image(img, caption='Uploaded Image')
+
+    # Preprocess the image
+    image = process_image(img)
+    
+    # Model prediction
+    with st.spinner('Classifying the image...'):
+        predictions = model.predict(image)
+        predicted_class = np.argmax(predictions)  
+        predicted_prob = predictions[0][predicted_class]  
+
+    # Class names for prediction
+    class_names = ['capsicum',
+                'sweetcorn',
+                'orange',
+                'tomato',
+                'turnip',
+                'ginger',
+                'raddish',
+                'pomegranate',
+                'pineapple',
+                'jalepeno',
+                'apple',
+                'carrot',
+                'lettuce',
+                'bell pepper',
+                'eggplant',
+                'beetroot',
+                'kiwi',
+                'pear',
+                'cabbage',
+                'cauliflower',
+                'paprika',
+                'lemon',
+                'sweetpotato',
+                'grapes',
+                'cucumber',
+                'corn',
+                'banana',
+                'garlic',
+                'chilli pepper',
+                'watermelon',
+                'mango',
+                'peas',
+                'onion',
+                'potato',
+                'spinach',
+                'soy beans']
+    
+    # Display the prediction
+    st.subheader(f"Prediction: {class_names[predicted_class]}")
+    st.write(f"Confidence: {predicted_prob * 100:.2f}%")
+
+    # Display prediction probabilities
+    st.write("Prediction Probabilities for Each Class:")
+    probabilities = predictions[0]
+    prob_dict = {class_names[i]: probabilities[i] for i in range(len(class_names))}
+    
+    # Plot settings
+    sns.set(style="white")  # Clean style with no grid background
+
+    # Create the figure
+    fig, ax = plt.subplots(figsize=(12, 8))  # Adjust figure size for better readability
+
+    # Plot the bar chart
+    ax.bar(list(prob_dict.keys()), list(prob_dict.values()), color='#197339', edgecolor='black')
+    ax.set_ylabel('Probability', fontsize=14)
+    ax.set_title('Prediction Probabilities for Each Class', fontsize=16)
+
+    # Rotate x-axis labels for better readability
+    plt.xticks(rotation=90, ha='right', fontsize=10)
+
+    # Annotate bars with percentage values
+    for index, value in enumerate(prob_dict.values()):
+        ax.text(index, value, f'{value * 100:.2f}%', va='bottom', ha='center', fontsize=10)
+
+    # Remove background grid and spines
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+    ax.spines['left'].set_visible(False)
+    ax.spines['bottom'].set_visible(False)
+    ax.grid(False)
+
+    # Adjust layout to prevent clipping
+    fig.tight_layout()
+
+    # Display the plot in Streamlit
+    st.pyplot(fig)
+
+    

cnn_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bc7b471fe33286efef8309dfa70e30846d46453843b5e5b7241126fe04a9b64f
+size 134111056