Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import cv2
+from PIL import Image
+import tensorflow as tf
+
+# Function to load the model
+@st.cache_resource
+def load_model():
+    model = tf.keras.models.load_model('path_to_your_saved_model.h5')  # Provide the path to your model
+    return model
+
+# Function to preprocess the image
+def preprocess_image(image):
+    image = np.array(image.convert('RGB'))
+    image = cv2.resize(image, (224, 224))  # Resize the image to the input shape required by your model
+    image = image / 255.0  # Normalize the image
+    image = np.expand_dims(image, axis=0)
+    return image
+
+# Function to predict the class
+def predict(image, model):
+    processed_image = preprocess_image(image)
+    prediction = model.predict(processed_image)
+    return prediction
+
+# Main app
+def main():
+    st.title("Food Item Recognition and Estimation")
+    st.write("Upload an image of a food item and the model will recognize the food item and estimate its calories.")
+
+    model = load_model()
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image.', use_column_width=True)
+        
+        st.write("")
+        st.write("Classifying...")
+        prediction = predict(image, model)
+        
+        st.write(f"Predicted class: {np.argmax(prediction)}")  # Update with your model's prediction logic
+
+if __name__ == "__main__":
+    main()