Added the files

Dockerfile

@@ -0,0 +1,25 @@
+FROM python:3.10-slim
+
+ENV PYTHONUNBUFFERED=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PORT=7860 \
+    BACKEND_URL=""
+
+WORKDIR /app
+
+# System deps
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+ && rm -rf /var/lib/apt/lists/*
+
+# Python deps
+COPY requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir -r /app/requirements.txt
+
+# App code
+COPY streamlit_app /app/streamlit_app
+
+EXPOSE 7860
+
+# Start Streamlit on HF-provided $PORT
+CMD ["bash", "-lc", "streamlit run streamlit_app/app_streamlit.py --server.port=${PORT} --server.address=0.0.0.0"]

README.md

@@ -8,3 +8,20 @@ pinned: false
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+# ExtraaLearn – Frontend (Docker Space)
+
+This Space hosts the **Streamlit UI** for the ExtraaLearn Lead Conversion demo.
+It **calls the Backend API Space** for predictions.
+
+## 🔧 Environment Variable
+
+Set in **Settings → Repository secrets / variables**:
+
+- `BACKEND_URL` → your backend Space base URL (without trailing slash)
+  Example: `https://<username>-<backendspace>.hf.space`
+
+## ▶️ Run
+
+This is a **Hugging Face Docker Space**. The app starts with:

requirements.txt

@@ -0,0 +1,4 @@
+streamlit==1.31.0
+pandas>=1.5.0
+numpy>=1.23.0
+requests>=2.31.0

streamlit_app/app_streamlit.py

@@ -0,0 +1,163 @@
+import os
+import json
+import time
+import pandas as pd
+import streamlit as st
+import requests
+
+# ----------------------------
+# Configuration
+# ----------------------------
+# Set this via Hugging Face Space secrets / variables or Docker env
+BACKEND_URL = os.getenv("BACKEND_URL", "").rstrip("/")
+if BACKEND_URL == "":
+    st.warning(
+        "⚠️ BACKEND_URL is not set. "
+        "Set an environment variable `BACKEND_URL` to your backend Space base URL, "
+        "e.g. https://<username>-<backendspace>.hf.space"
+    )
+
+st.set_page_config(page_title="ExtraaLearn – Lead Conversion", page_icon="📈", layout="wide")
+st.title("📈 ExtraaLearn – Lead Conversion (Frontend)")
+
+with st.expander("ℹ️ Instructions", expanded=False):
+    st.markdown(
+        """
+- Set environment variable **`BACKEND_URL`** to your backend Space URL (without trailing slash).
+- Use the **Single Prediction** tab for an individual lead.
+- Use the **Batch Scoring** tab to upload a CSV and get predictions back.
+        """
+    )
+
+# ----------------------------
+# Utilities
+# ----------------------------
+def ping_backend():
+    if not BACKEND_URL:
+        return False, {"error": "BACKEND_URL not set"}
+    try:
+        r = requests.get(f"{BACKEND_URL}/", timeout=10)
+        return r.ok, r.json()
+    except Exception as e:
+        return False, {"error": str(e)}
+
+def post_json(endpoint: str, payload):
+    url = f"{BACKEND_URL}{endpoint}"
+    r = requests.post(url, json=payload, timeout=60)
+    r.raise_for_status()
+    return r
+
+# ----------------------------
+# Health check
+# ----------------------------
+colA, colB = st.columns([1, 2])
+with colA:
+    if st.button("🔁 Check Backend Health"):
+        ok, info = ping_backend()
+        if ok:
+            st.success(f"Backend OK · model_loaded={info.get('model_loaded')}")
+        else:
+            st.error(f"Backend unreachable: {info}")
+
+# ----------------------------
+# Tabs
+# ----------------------------
+tab1, tab2 = st.tabs(["Single Prediction", "Batch Scoring"])
+
+# ---- Tab 1: Single ----
+with tab1:
+    st.subheader("Single Prediction")
+    c1, c2, c3 = st.columns(3)
+
+    with c1:
+        age = st.number_input("Age", min_value=10, max_value=100, value=30)
+        website_visits = st.number_input("Website Visits", min_value=0, max_value=500, value=3)
+        time_spent_on_website = st.number_input("Time Spent on Website (sec)", min_value=0, max_value=200000, value=300)
+        page_views_per_visit = st.number_input("Page Views per Visit", min_value=0.0, max_value=100.0, value=3.5, step=0.1)
+
+    with c2:
+        current_occupation = st.selectbox("Current Occupation", ["Professional", "Student", "Unemployed"])
+        first_interaction = st.selectbox("First Interaction", ["Website", "Mobile App"])
+        last_activity = st.selectbox("Last Activity", ["Email Activity", "Phone Activity", "Website Activity"])
+        profile_completed = st.selectbox(
+            "Profile Completed",
+            ["Low (0-50%)", "Medium (50-75%)", "High (75-100%)"]
+        )
+
+    with c3:
+        print_media_type1 = st.selectbox("Saw Newspaper Ad (print_media_type1)", ["No", "Yes"])
+        print_media_type2 = st.selectbox("Saw Magazine Ad (print_media_type2)", ["No", "Yes"])
+        digital_media = st.selectbox("Saw Digital Ad", ["No", "Yes"])
+        educational_channels = st.selectbox("Heard via Educational Channels", ["No", "Yes"])
+        referral = st.selectbox("Referred by Someone", ["No", "Yes"])
+
+    if st.button("🚀 Predict"):
+        if not BACKEND_URL:
+            st.error("Please set BACKEND_URL to your backend Space URL.")
+        else:
+            payload = {
+                "age": age,
+                "website_visits": website_visits,
+                "time_spent_on_website": time_spent_on_website,
+                "page_views_per_visit": page_views_per_visit,
+                "print_media_type1": print_media_type1,
+                "print_media_type2": print_media_type2,
+                "digital_media": digital_media,
+                "educational_channels": educational_channels,
+                "referral": referral,
+                "current_occupation": current_occupation,
+                "first_interaction": first_interaction,
+                "last_activity": last_activity,
+                "profile_completed": profile_completed
+            }
+            try:
+                start = time.time()
+                r = post_json("/predict", payload)
+                elapsed = time.time() - start
+                result = r.json()
+                st.success(f"✅ Inference OK · {elapsed:.2f}s")
+                st.metric("Conversion Probability", f"{result.get('probability', 0.0):.3f}")
+                pred = result.get("prediction", 0)
+                st.write("Prediction:", "✅ Will Convert" if pred == 1 else "❌ Unlikely to Convert")
+                st.code(json.dumps(payload, indent=2), language="json")
+            except requests.HTTPError as http_err:
+                st.error(f"HTTP error: {http_err} · {r.text}")
+            except Exception as e:
+                st.error(f"Request failed: {e}")
+
+# ---- Tab 2: Batch ----
+with tab2:
+    st.subheader("Batch Scoring (CSV)")
+    st.caption("The CSV must include the same columns used during model training.")
+
+    sample_schema = [
+        "age","website_visits","time_spent_on_website","page_views_per_visit",
+        "print_media_type1","print_media_type2","digital_media","educational_channels","referral",
+        "current_occupation","first_interaction","last_activity","profile_completed"
+    ]
+    st.text_area("Expected columns", ", ".join(sample_schema), height=70, disabled=True)
+
+    up = st.file_uploader("Upload CSV", type=["csv"])
+    if up is not None:
+        try:
+            df = pd.read_csv(up)
+            st.write("Preview:", df.head())
+            if st.button("📤 Send to Backend"):
+                if not BACKEND_URL:
+                    st.error("Please set BACKEND_URL to your backend Space URL.")
+                else:
+                    records = df.to_dict(orient="records")
+                    start = time.time()
+                    r = post_json("/predict-batch", {"records": records})
+                    elapsed = time.time() - start
+                    out = pd.DataFrame(r.json())
+                    st.success(f"✅ Batch inference OK · {elapsed:.2f}s")
+                    st.dataframe(out.head(20), use_container_width=True)
+                    st.download_button(
+                        "⬇️ Download Results",
+                        out.to_csv(index=False).encode("utf-8"),
+                        file_name="predictions.csv",
+                        mime="text/csv"
+                    )
+        except Exception as e:
+            st.error(f"Failed to process CSV: {e}")