Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,146 @@
-import altair as alt
+import json
+from pathlib import Path
+
 import numpy as np
-import pandas as pd
 import streamlit as st
+from PIL import Image
+import tensorflow as tf
+import matplotlib.pyplot as plt
+
+
+# -------------------------
+# Page config
+# -------------------------
+st.set_page_config(
+    page_title='Facial Keypoints Predictor (CNN)',
+    page_icon='😊',
+    layout='centered'
+)
+
+st.title('😊 Facial Keypoints Predictor (CNN)')
+st.write('Upload a face image and the CNN predicts 30 facial keypoint coordinates (x/y).')
+
+
+# -------------------------
+# Paths (HF-friendly: repo root)
+# -------------------------
+BASE_DIR = Path(__file__).resolve().parent
+
+MODEL_PATH = BASE_DIR / 'final_keypoints_cnn.keras'
+TARGET_COLS_PATH = BASE_DIR / 'target_cols.json'
+PREPROCESS_PATH = BASE_DIR / 'preprocess_config.json'
+
+
+# -------------------------
+# Loaders
+# -------------------------
+@st.cache_resource
+def load_model():
+    if not MODEL_PATH.exists():
+        raise FileNotFoundError(
+            f'Model not found: {MODEL_PATH.name}. Put it in the repo root (same folder as app.py).'
+        )
+    return tf.keras.models.load_model(MODEL_PATH, compile=False)
+
+
+@st.cache_data
+def load_json(path: Path):
+    if not path.exists():
+        raise FileNotFoundError(
+            f'File not found: {path.name}. Put it in the repo root (same folder as app.py).'
+        )
+    with open(path, 'r') as f:
+        return json.load(f)
+
+
+model = load_model()
+target_cols = load_json(TARGET_COLS_PATH)
+pre_cfg = load_json(PREPROCESS_PATH)
+
+IMG_H, IMG_W = pre_cfg.get('img_size', [96, 96])
+
+
+# -------------------------
+# Preprocess + Postprocess
+# -------------------------
+def preprocess_image(pil_img: Image.Image) -> np.ndarray:
+    # Convert to grayscale like training data
+    img = pil_img.convert('L')
+    img = img.resize((IMG_W, IMG_H))
+    arr = np.array(img, dtype=np.float32)  # 0..255
+    arr = arr / 255.0
+    arr = arr.reshape(1, IMG_H, IMG_W, 1)
+    return arr
+
+
+def denormalize_keypoints(pred_norm: np.ndarray) -> np.ndarray:
+    # Training normalization: (y - 48) / 48  -> invert: y = pred*48 + 48
+    pred = pred_norm * 48.0 + 48.0
+    return pred
+
+
+def plot_keypoints(pil_img: Image.Image, keypoints_xy: np.ndarray):
+    img = pil_img.convert('L').resize((IMG_W, IMG_H))
+    xs = keypoints_xy[0::2]
+    ys = keypoints_xy[1::2]
+
+    fig, ax = plt.subplots(figsize=(5, 5))
+    ax.imshow(img, cmap='gray')
+    ax.scatter(xs, ys, s=25)
+    ax.axis('off')
+    return fig
+
+
+# -------------------------
+# UI
+# -------------------------
+uploaded = st.file_uploader('Upload an image (jpg/png)', type=['jpg', 'jpeg', 'png'])
+
+with st.expander('Settings', expanded=False):
+    show_table = st.checkbox('Show predicted coordinates table', value=True)
+    show_overlay = st.checkbox('Show keypoints overlay', value=True)
+
+if uploaded is None:
+    st.info('Upload an image to get predictions.')
+    st.stop()
+
+pil_img = Image.open(uploaded)
+
+st.subheader('Input Image')
+st.image(pil_img, use_container_width=True)
+
+x = preprocess_image(pil_img)
+
+with st.spinner('Predicting keypoints...'):
+    pred_norm = model.predict(x, verbose=0)[0]  # shape (30,)
+pred_px = denormalize_keypoints(pred_norm)
+
+# Build table
+rows = []
+for i, name in enumerate(target_cols):
+    rows.append({'feature': name, 'value_px': float(pred_px[i])})
+
+if show_table:
+    st.subheader('Predicted Keypoints (Pixels)')
+    st.dataframe(rows, use_container_width=True)
+
+if show_overlay:
+    st.subheader('Overlay')
+    fig = plot_keypoints(pil_img, pred_px)
+    st.pyplot(fig, clear_figure=True)
+
+# Optional: downloadable single-image "submission-like" csv (ImageId=1)
+st.subheader('Download (optional)')
+st.caption('This creates a single-image submission-like file: ImageId=1, RowId=1..30.')
+
+csv_lines = ['RowId,ImageId,FeatureName,Location']
+for idx, (name, val) in enumerate(zip(target_cols, pred_px), start=1):
+    csv_lines.append(f'{idx},1,{name},{val}')
+csv_text = '\n'.join(csv_lines)
 
-"""
-# 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.download_button(
+    label='Download predicted_keypoints.csv',
+    data=csv_text.encode('utf-8'),
+    file_name='predicted_keypoints.csv',
+    mime='text/csv'
+)