Initial Project commit

.env

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+BACKEND_URL = https://mohitg012-bi-assistant-backend.hf.space/

Dockerfile

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+FROM python:3.12-slim
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libgl1 libglib2.0-0 && \
+    rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app.py .
+ENV PORT=7860
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

app.py

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+import os, io, base64, json, time
+from typing import Optional, Tuple
+import requests
+from PIL import Image, ImageOps, ImageDraw
+import streamlit as st
+
+# -----------------------
+# Config
+# -----------------------
+BACKEND = os.getenv("BACKEND_URL", "http://localhost:8000").rstrip("/")
+TIMEOUT = 60
+
+st.set_page_config(page_title="Realtime BI Assistant (Frontend)", layout="centered")
+
+# -----------------------
+# Helpers
+# -----------------------
+def ping_backend() -> Tuple[bool, Optional[dict]]:
+    try:
+        r = requests.get(f"{BACKEND}/", timeout=10)
+        return r.ok, (r.json() if r.ok else None)
+    except Exception as e:
+        return False, {"error": str(e)}
+
+def pil_from_upload(file) -> Optional[Image.Image]:
+    try:
+        return Image.open(file).convert("RGB")
+    except Exception:
+        return None
+
+def compress_and_b64(img: Image.Image, max_side: int = 1280, quality: int = 85) -> str:
+    img = ImageOps.exif_transpose(img)  # respect EXIF orientation
+    w, h = img.size
+    scale = max(w, h) / max_side if max(w, h) > max_side else 1.0
+    if scale > 1.0:
+        img = img.resize((int(w/scale), int(h/scale)))
+    buf = io.BytesIO()
+    img.save(buf, format="JPEG", quality=quality, optimize=True)
+    return base64.b64encode(buf.getvalue()).decode()
+
+def draw_bbox(img: Image.Image, bbox: list[int], color=(0, 255, 0), width: int = 4) -> Image.Image:
+    out = img.copy()
+    draw = ImageDraw.Draw(out)
+    x1, y1, x2, y2 = bbox
+    draw.rectangle([x1, y1, x2, y2], outline=color, width=width)
+    return out
+
+def post_json(path: str, payload: dict) -> requests.Response:
+    return requests.post(f"{BACKEND}{path}", json=payload, timeout=TIMEOUT)
+
+def post_multipart(path: str, files: dict, params: dict) -> requests.Response:
+    return requests.post(f"{BACKEND}{path}", files=files, params=params, timeout=TIMEOUT)
+
+# -----------------------
+# UI
+# -----------------------
+st.title("Realtime BI Assistant (Demo Frontend)")
+st.caption("Face upsert/identify + BI Q&A (text) via your FastAPI backend")
+
+ok, info = ping_backend()
+status_col, url_col = st.columns([1,3])
+with status_col:
+    st.metric("Backend", "Online ✅" if ok else "Offline ❌")
+with url_col:
+    st.code(BACKEND, language="text")
+
+if not ok and info:
+    st.warning(f"Backend unreachable: {info}")
+
+# Persist chosen user
+if "user_name" not in st.session_state:
+    st.session_state.user_name = "mohit"
+
+# -----------------------
+# 1) Enroll / Upsert face
+# -----------------------
+with st.expander("1) Enroll / Upsert face (optional)", expanded=False):
+    st.session_state.user_name = st.text_input("User name", value=st.session_state.user_name, key="user_name_input")
+    c1, c2 = st.columns(2)
+    with c1:
+        upload_img = st.file_uploader("Upload a face image (jpg/png)", type=["jpg","jpeg","png"])
+    with c2:
+        cam_img = st.camera_input("Or capture from camera")
+
+    chosen = None
+    if cam_img is not None:
+        chosen = pil_from_upload(cam_img)
+    elif upload_img is not None:
+        chosen = pil_from_upload(upload_img)
+
+    if st.button("Upsert to local index"):
+        if not chosen:
+            st.error("Please provide an image (upload or camera).")
+        elif not st.session_state.user_name.strip():
+            st.error("Please enter a user name.")
+        else:
+            buf = io.BytesIO()
+            chosen.save(buf, format="JPEG", quality=90)
+            buf.seek(0)
+            try:
+                with st.spinner("Upserting…"):
+                    resp = post_multipart(
+                        "/index/upsert_image",
+                        files={"image": ("face.jpg", buf, "image/jpeg")},
+                        params={"user": st.session_state.user_name.strip()},
+                    )
+                if resp.ok:
+                    st.success("Face vector upserted ✅")
+                    st.json(resp.json())
+                else:
+                    st.error(f"Upsert failed: {resp.status_code}")
+                    st.text(resp.text)
+            except Exception as e:
+                st.error(f"Request error: {e}")
+
+# -----------------------
+# 2) Identify user
+# -----------------------
+with st.expander("2) Identify from image (optional)", expanded=False):
+    col_u, col_c = st.columns(2)
+    with col_u:
+        test_upload = st.file_uploader("Upload test image", type=["jpg","jpeg","png"], key="test_upload")
+    with col_c:
+        test_cam = st.camera_input("Or capture from camera", key="test_cam")
+
+    test_img = None
+    src_lbl = None
+    if test_cam is not None:
+        test_img, src_lbl = pil_from_upload(test_cam), "camera"
+    elif test_upload is not None:
+        test_img, src_lbl = pil_from_upload(test_upload), "upload"
+
+    if st.button("Identify"):
+        if test_img is None:
+            st.warning("Please provide an image first.")
+        else:
+            b64 = compress_and_b64(test_img)
+            try:
+                with st.spinner("Identifying…"):
+                    r = post_json("/identify", {"image_b64": b64, "top_k": 3})
+                if not r.ok:
+                    st.error(f"Identify failed: {r.status_code}")
+                    st.text(r.text)
+                else:
+                    data = r.json()
+                    st.success(f"Decision: {data.get('decision')}  |  best_score={data.get('best_score'):.3f}  |  margin={data.get('margin'):.3f}")
+                    bbox = data.get("bbox")
+                    if bbox and isinstance(bbox, list) and len(bbox) == 4:
+                        st.caption("Detected face:")
+                        st.image(draw_bbox(test_img, bbox), use_column_width=True)
+                    else:
+                        st.image(test_img, use_column_width=True)
+
+                    if data.get("topk"):
+                        st.caption("Top-k candidates:")
+                        st.json(data["topk"])
+            except Exception as e:
+                st.error(f"Request error: {e}")
+
+# -----------------------
+# 3) Ask a BI question
+# -----------------------
+st.subheader("3) Ask a BI question")
+bi_col, filt_col = st.columns([3,1])
+
+with bi_col:
+    default_q = "What is revenue for region NCR on 2025-09-06?"
+    q_text = st.text_area("Your question", value=default_q, height=100)
+
+with filt_col:
+    region = st.text_input("region", value="NCR")
+    day    = st.text_input("day (YYYY-MM-DD)", value="2025-09-06")
+
+if st.button("Ask"):
+    payload = {
+        "user_id": st.session_state.user_name or None,
+        "text": q_text.strip(),
+        "filters": {"region": region.strip(), "day": day.strip()}
+    }
+    try:
+        with st.spinner("Querying…"):
+            r = post_json("/query", payload)
+        if r.ok:
+            resp = r.json()
+            st.success(resp.get("answer_text", ""))
+            if resp.get("metrics"):
+                with st.expander("Metrics", expanded=True):
+                    st.json(resp["metrics"])
+            if resp.get("citations"):
+                with st.expander("Citations", expanded=False):
+                    st.json(resp["citations"])
+            if resp.get("chart_refs"):
+                with st.expander("Charts", expanded=False):
+                    st.json(resp["chart_refs"])
+            if "uncertainty" in resp:
+                st.caption(f"Uncertainty: {resp['uncertainty']:.2f}")
+        else:
+            st.error(f"Backend error: {r.status_code}")
+            st.text(r.text)
+    except Exception as e:
+        st.error(f"Request error: {e}")
+
+st.markdown("---")
+st.caption("Tip: Set `BACKEND_URL` in your Space Variables to point this UI to your FastAPI backend.")

requirements.txt

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+streamlit
+requests
+Pillow