Upload app.py

app.py

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+import streamlit as st
+from fastai.vision.all import *
+
+# Load the pre-trained model
+model_path = "assets/model.pkl"  # Replace with the path to your model file
+learn = load_learner(model_path)
+
+# Define a function to make predictions on an image
+def predict(image):
+    img = PILImage.create(image)
+    pred, _, probs = learn.predict(img)
+    return pred, probs[pred]
+
+# Streamlit app
+st.title("Hand Gesture Recognition")
+
+# Upload an image
+uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+
+if uploaded_image is not None:
+    # Display the uploaded image
+    st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
+
+    # Make predictions
+    prediction, confidence = predict(uploaded_image)
+    
+    # Display the prediction
+    st.subheader("Prediction:")
+    st.write(f"Gesture: {prediction}")
+    st.write(f"Confidence: {confidence:.2f}")
+
+# Capture an image from webcam
+capture = st.checkbox("Capture an Image from Webcam")
+
+if capture:
+    st.write("Click the button to capture the image")
+    capture_button = st.button("Capture")
+
+    if capture_button:
+        # Capture the image from the webcam
+        # You can use Python libraries like OpenCV for this
+        # Replace this with your code for capturing an image
+        
+        # For example:
+        # cap = cv2.VideoCapture(0)
+        # ret, frame = cap.read()
+        # cv2.imwrite("captured_image.jpg", frame)
+        # cap.release()
+        
+        # After capturing the image, predict and display it
+        captured_image_path = "captured_image.jpg"  # Replace with the actual path
+        st.image(captured_image_path, caption="Captured Image", use_column_width=True)
+        
+        # Make predictions
+        prediction, confidence = predict(captured_image_path)
+        
+        # Display the prediction
+        st.subheader("Prediction:")
+        st.write(f"Gesture: {prediction}")
+        st.write(f"Confidence: {confidence:.2f}")
+
+# Example images
+st.sidebar.title("Example Images")
+example_images = {
+    "Image 1": "example_image1.jpg",
+    "Image 2": "example_image2.jpg",
+    "Image 3": "example_image3.jpg",
+}
+
+selected_example = st.sidebar.selectbox("Select an Example Image", list(example_images.keys()))
+
+if selected_example:
+    selected_image_path = example_images[selected_example]
+    st.image(selected_image_path, caption=selected_example, use_column_width=True)
+    
+    # Make predictions for the example image
+    example_prediction, example_confidence = predict(selected_image_path)
+    
+    # Display the prediction
+    st.sidebar.subheader("Prediction:")
+    st.sidebar.write(f"Gesture: {example_prediction}")
+    st.sidebar.write(f"Confidence: {example_confidence:.2f}")