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app.py

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+# streamlit_app.py
+import streamlit as st
+from fastai.vision.all import *
+import shutil
+import os
+
+# Function to get the label from the file name
+def get_label(file_name):
+    return file_name.split('-')[0]
+
+# Function to prepare data (similar to your code)
+def prepare_data(food_path, label_a, label_b):
+    for img in get_image_files(food_path):
+        if label_a in str(img):
+            img.rename(f"{img.parent}/{label_a}-{img.name}")
+        elif label_b in str(img):
+            img.rename(f"{img.parent}/{label_b}-{img.name}")
+        else:
+            os.remove(img)
+
+# Function to train the model
+def train_model(food_path, label_func):
+    dls = ImageDataLoaders.from_name_func(
+        food_path, get_image_files(food_path), valid_pct=0.2, seed=420,
+        label_func=label_func, item_tfms=Resize(230)
+    )
+
+    learn = cnn_learner(dls, resnet34, metrics=error_rate, pretrained=True)
+    learn.fine_tune(epochs=1)
+
+    return learn
+
+# ... (previous code)
+
+# ... (previous code)
+
+# Streamlit app
+def main():
+    st.title("Food Classifier Streamlit App")
+
+    # Sidebar options
+    options = ["Train Model", "Upload Image", "Test Random Images", "Confusion Matrix"]
+    choice = st.sidebar.selectbox("Choose an option", options)
+
+    if choice == "Train Model":
+        st.subheader("Training the Model")
+        food_path = Path("~/.fastai/data/food-101/food-101").expanduser()
+        if not food_path.exists():
+            try:
+                food_path = untar_data(URLs.FOOD)
+            except FileExistsError:
+                st.warning("Data directory already exists. Skipping download.")
+            label_a = st.text_input("Enter label A:", "samosa")
+            label_b = st.text_input("Enter label B:", "hot_and_sour_soup")
+
+            prepare_data(food_path, label_a, label_b)
+            learn = train_model(food_path, get_label)
+
+            st.session_state.model = learn  # Save the model to session state
+            st.success("Model trained successfully!")
+
+    # ... (rest of the code remains unchanged)
+
+
+
+
+
+
+
+            if uploaded_files:
+                for img in uploaded_files:
+                    img = PILImage.create(img)
+                    label, _, probs = st.session_state.model.predict(img)
+
+                    st.image(img, caption=f"This is a {label}.")
+                    st.write(f"{label_a}: {probs[1].item():.6f}")
+                    st.write(f"{label_b}: {probs[0].item():.6f}")
+
+    elif choice == "Test Random Images":
+        st.subheader("Test Using Images in Dataset")
+        if "model" not in st.session_state:
+            st.warning("Please train the model first.")
+        else:
+            for i in range(0, 5):  # Change 5 to the number of images you want to display
+                random_index = random.randint(0, len(get_image_files(food_path)) - 1)
+                img_path = get_image_files(food_path)[random_index]
+                img = mpimg.imread(img_path)
+                label, _, probs = st.session_state.model.predict(img)
+
+                st.image(img, caption=f"Predicted label: {label}")
+
+    elif choice == "Confusion Matrix":
+        st.subheader("Confusion Matrix")
+        if "model" not in st.session_state:
+            st.warning("Please train the model first.")
+        else:
+            interp = ClassificationInterpretation.from_learner(st.session_state.model)
+            st.pyplot(interp.plot_confusion_matrix())
+
+# Run the Streamlit app
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+torch
+fastai
+Pillow
+matplotlib