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app.py

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+# streamlit_app.py
+
+import streamlit as st
+import numpy as np
+import pandas as pd
+from keras.models import load_model
+from PIL import Image, ImageDraw
+import io
+
+# Load the trained model
+model = load_model('keypoint_model.h5')
+
+def load_image(image):
+    image = Image.open(image).convert('L')  # Convert to grayscale
+    image = image.resize((96, 96))  # Resize to match model input
+    image_array = np.array(image)
+    image_array = image_array / 255.0  # Normalize
+    return image_array.reshape(-1, 96, 96, 1)  # Reshape for model input
+
+def draw_keypoints(image, keypoints):
+    # Draw keypoints on the image
+    draw = ImageDraw.Draw(image)
+    for (x, y) in keypoints:
+        draw.ellipse((x - 3, y - 3, x + 3, y + 3), fill='red')  # Draw a circle for each keypoint
+    return image
+
+# Title of the app
+st.title("Keypoint Prediction App")
+
+# Upload an image
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Load and preprocess the image
+    image = load_image(uploaded_file)
+    
+    # Display the uploaded image
+    original_image = Image.open(uploaded_file).convert('L').resize((96, 96))  # Convert and resize for displaying
+    st.image(original_image, caption='Uploaded Image.', use_column_width=True)
+    
+    # Make predictions
+    if st.button("Predict"):
+        predictions = model.predict(image)
+        # Reshape predictions to (15, 2) for x and y coordinates
+        keypoints = predictions.reshape(-1, 2)
+        
+        # Draw keypoints on the original image
+        keypoint_image = draw_keypoints(original_image.copy(), keypoints)
+        
+        # Display the image with keypoints
+        st.image(keypoint_image, caption='Image with Predicted Keypoints', use_column_width=True)
+
+        # Display the keypoints
+        st.write("Predicted Keypoints:")
+        for i, (x, y) in enumerate(keypoints):
+            st.write(f"Keypoint {i+1}: (X: {x:.2f}, Y: {y:.2f})")

keypoint_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ae40978ed7325ae8ea1c953ffcab3b3d41fb7a97bd4b5c63cd03bfb446fcf85b
+size 3424376

requirements.txt

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+streamlit
+tensorflow
+opencv-python
+scikit-learn
+torch
+torchvision
+matplotlib
+transformers
+sentencepiece
+plotly