Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,42 @@
-import altair as alt
-import numpy as np
+import requests
 import pandas as pd
 import streamlit as st
 
-"""
-# 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])),
-    ))
+# ←––– Point this at your deployed Flask API
+BACKEND_URL = "https://Pushpak21-Pharmacy.hf.space/predict"
+
+st.title("🎯 Pharmacy College Predictor")
+st.write("Enter your details below:")
+
+# Input controls
+category = st.selectbox("Category", [
+    'GOPEN', 'GSC', 'GNTB', 'GOBC', 'GSEBC', 'LOPEN', 'LST', 'LOBC',
+    'EWS', 'GST', 'GNTC', 'LSC', 'LNTB', 'LNTD', 'LNTC', 'LSEBC',
+    'GNTA', 'GNTD', 'LNTA'
+])
+rank = st.number_input("Rank", min_value=1, value=1500)
+percentage = st.number_input("Percentage", min_value=0.0, max_value=100.0, value=75.0)
+
+if st.button("Predict Top 20 Choices"):
+    payload = {
+        "Category": category,
+        "Rank": rank,
+        "Percentage": percentage
+    }
+
+    # Call your backend API
+    resp = requests.post(BACKEND_URL, json=payload)
+    if resp.status_code != 200:
+        st.error(f"Backend error: {resp.status_code}")
+        st.write(resp.text)
+    else:
+        data = resp.json().get("top_20_predictions", [])
+        if not data:
+            st.warning("No predictions returned.")
+        else:
+            df = pd.DataFrame(data)
+            df.index = df.index + 1
+            df.reset_index(inplace=True)
+            df.rename(columns={"index": "Prediction Rank"}, inplace=True)
+            st.write("### Top‑20 Predicted Choices")
+            st.dataframe(df)