Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,42 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+
+st.set_page_config(
+    page_title="Facial Keypoints Detection",
+    layout="centered"
+)
+
+st.title("Facial Keypoints Detection")
+st.write("Upload a face image and the model will predict facial keypoints.")
+
+MODEL_PATH = "facial_keypoints_resnet.h5"
+
+@st.cache_resource
+def load_model():
+    return tf.keras.models.load_model(
+        MODEL_PATH,
+        safe_mode=False 
+    )
+
+model = load_model()
+
+uploaded_file = st.file_uploader(
+    "Upload an image",
+    type=["jpg", "png", "jpeg"]
+)
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert("L")
+    image = image.resize((96, 96))
+
+    st.image(image, caption="Uploaded image", width=250)
+
+    img_array = np.array(image).reshape(1, 96, 96, 1) / 255.0
+    preds = model.predict(img_array)[0]
+
+    keypoints = preds.reshape(-1, 2)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    st.subheader("Predicted Keypoints (x, y)")
+    st.write(keypoints)