Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import tempfile
+import cv2
+import os
+
+st.set_page_config(page_title="AI Health Lab", layout="wide")
+
+st.title("🧬 Face-based Health Lab")
+st.markdown("Upload a **facial image** (for Hb) and a **20-30 s face video** (for HR).\n\n"
+            "This is a baseline app – models will be added for each test step-by-step.")
+
+# -------------------------------------------------
+# Sidebar instructions
+# -------------------------------------------------
+st.sidebar.header("Instructions")
+st.sidebar.info(
+    "1. Upload a good quality **face / eye image** for Hemoglobin.\n"
+    "2. Upload a **20-30 s face video** for Heart-Rate.\n"
+    "3. Click **Run Analysis** to see test results.\n\n"
+    "Later we will add more tests and replace the dummy predictions with real models."
+)
+
+# -------------------------------------------------
+# Input widgets
+# -------------------------------------------------
+uploaded_img = st.file_uploader("Upload Face / Eye Image for Hemoglobin", type=["jpg","jpeg","png"])
+uploaded_vid = st.file_uploader("Upload Face Video for Heart-Rate (20-30 s)", type=["mp4","avi","mov"])
+
+run_btn = st.button("Run Analysis")
+
+# -------------------------------------------------
+# Placeholder prediction functions
+# -------------------------------------------------
+def predict_hemoglobin(image: np.ndarray) -> float:
+    # TODO: replace with actual Hb model
+    return float(np.random.uniform(11.0,15.0))
+
+def predict_heart_rate(video_path: str) -> float:
+    # TODO: replace with real rPPG inference
+    return float(np.random.uniform(65,85))
+
+# -------------------------------------------------
+# Run inference
+# -------------------------------------------------
+if run_btn:
+    results = []
+
+    # ---- Hemoglobin ----
+    if uploaded_img is not None:
+        file_bytes = np.asarray(bytearray(uploaded_img.read()), dtype=np.uint8)
+        img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+        hb = predict_hemoglobin(img)
+        results.append({"Test": "Hemoglobin", "Result": f"{hb:.2f} g/dL"})
+    else:
+        results.append({"Test": "Hemoglobin", "Result": "No image uploaded"})
+
+    # ---- Heart Rate ----
+    if uploaded_vid is not None:
+        # save video temporarily
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmpf:
+            tmpf.write(uploaded_vid.read())
+            tmp_path = tmpf.name
+
+        hr = predict_heart_rate(tmp_path)
+        results.append({"Test": "Heart Rate", "Result": f"{hr:.1f} bpm"})
+
+        # clean up
+        os.remove(tmp_path)
+    else:
+        results.append({"Test": "Heart Rate", "Result": "No video uploaded"})
+
+    # ---- Future tests placeholders ----
+    results.append({"Test": "SpO₂", "Result": "—"})
+    results.append({"Test": "Respiration Rate", "Result": "—"})
+    # ... add more as needed later
+
+    # -------------------------------------------------
+    # Show results table
+    # -------------------------------------------------
+    df = pd.DataFrame(results)
+    st.subheader("🩺 Test Results")
+    st.table(df)
+
+else:
+    st.info("⬆ Upload image and/or video, then click **Run Analysis**.")