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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+final_model.keras filter=lfs diff=lfs merge=lfs -text

app.py

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+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import numpy as np
+import os
+
+# Load the model
+MODEL_PATH = "/home/petpooja-504/Desktop/cnn/final_model.keras"
+model = tf.keras.models.load_model(MODEL_PATH)
+
+# Define the class names directly for the Food-101 dataset
+CLASS_NAMES = [
+    'apple_pie', 'baby_back_ribs', 'baklava', 'beef_carpaccio', 'beef_tartare', 'beet_salad', 'beignets',
+    'bibimbap', 'bread_pudding', 'breakfast_burrito', 'bruschetta', 'caesar_salad', 'cannoli', 'caprese_salad',
+    'carrot_cake', 'ceviche', 'cheese_plate', 'cheesecake', 'chicken_curry', 'chicken_quesadilla',
+    'chicken_wings', 'chocolate_cake', 'chocolate_mousse', 'churros', 'clam_chowder', 'club_sandwich',
+    'crab_cakes', 'creme_brulee', 'croque_madame', 'cup_cakes', 'deviled_eggs', 'donuts', 'dumplings',
+    'edamame', 'eggs_benedict', 'escargots', 'falafel', 'filet_mignon', 'fish_and_chips', 'foie_gras',
+    'french_fries', 'french_onion_soup', 'french_toast', 'fried_calamari', 'fried_rice', 'frozen_yogurt',
+    'garlic_bread', 'gnocchi', 'greek_salad', 'grilled_cheese_sandwich', 'grilled_salmon', 'guacamole',
+    'gyoza', 'hamburger', 'hot_and_sour_soup', 'hot_dog', 'huevos_rancheros', 'hummus', 'ice_cream',
+    'lasagna', 'lobster_bisque', 'lobster_roll_sandwich', 'macaroni_and_cheese', 'macarons', 'miso_soup',
+    'mussels', 'nachos', 'omelette', 'onion_rings', 'oysters', 'pad_thai', 'paella', 'pancakes', 'panna_cotta',
+    'peking_duck', 'pho', 'pizza', 'pork_chop', 'poutine', 'prime_rib', 'pulled_pork_sandwich', 'ramen',
+    'ravioli', 'red_velvet_cake', 'risotto', 'samosa', 'sashimi', 'scallops', 'seaweed_salad', 'shrimp_and_grits',
+    'spaghetti_bolognese', 'spaghetti_carbonara', 'spring_rolls', 'steak', 'strawberry_shortcake', 'sushi',
+    'tacos', 'takoyaki', 'tiramisu', 'tuna_tartare', 'waffles'
+]
+
+
+
+# Define the function to predict the image
+# Define the function to predict the image
+def predict_image(img_path):
+    # Load and preprocess the image
+    img = image.load_img(img_path, target_size=(224, 224))  # Resize to match model's expected input size
+    img_array = image.img_to_array(img)  # Convert image to array
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    img_array = img_array / 255.0  # Normalize the image as done during training
+
+    # Make prediction
+    predictions = model.predict(img_array)
+
+    # Get prediction probabilities
+    prediction_probs = predictions[0]  # Prediction probabilities
+    predicted_class_index = np.argmax(prediction_probs)
+    predicted_class = CLASS_NAMES[predicted_class_index]  # Fetch the class name
+
+    return predicted_class
+
+
+
+# Streamlit UI components
+st.title("Food-101 Classification Model")
+st.write("Upload an image of food to predict its class.")
+
+# Upload image
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+
+    # Save the image temporarily
+    img_path = "uploaded_image.jpg"
+    with open(img_path, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+
+    # Make prediction
+    predicted_class = predict_image(img_path)
+
+    # Display the predicted class
+    st.write(f"Predicted Class: {predicted_class}")

final_model.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c918e0962bfa2d1399c493b089924bbbd44ab9b11147e4e03a3947d625d00d1d
+size 296632716