Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,30 @@
-import altair as alt
-import numpy as np
-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])),
-    ))
+import requests
+
+st.title("🛒 SuperKart Sales Predictor")
+
+# Input fields
+inputs = {
+    'Product_Weight': st.number_input("Weight (kg)", value=12.0),
+    'Product_Allocated_Area': st.number_input("Allocated Area", value=0.05),
+    'Product_MRP': st.number_input("MRP (₹)", value=150.0),
+    'Store_Age': st.number_input("Store Age", value=15),
+    'Product_Sugar_Content': st.selectbox("Sugar Content", ['Low', 'Regular', 'No Sugar']),
+    'Product_Type': st.selectbox("Product Type", ['Dairy', 'Meat', 'Snack Foods', 'Others']),
+    'Store_Size': st.selectbox("Store Size", ['Small', 'Medium', 'High']),
+    'Store_Location_City_Type': st.selectbox("City Tier", ['Tier 1', 'Tier 2', 'Tier 3']),
+    'Store_Type': st.selectbox("Store Type", ['Departmental Store', 'Supermarket Type1', 'Food Mart'])
+}
+
+# Prediction button
+if st.button("Predict Sales"):
+    try:
+        #response = requests.post("http://host.docker.internal:7860/predict", json=inputs)
+        response = requests.post("https://labhara-superkart-backend.hf.space/predict", json=inputs)
+        if response.ok:
+            result = response.json()["predicted_sales"]
+            st.success(f"💰 Predicted Sales: ₹{result}")
+        else:
+            st.error("Failed to get prediction.")
+    except:
+        st.error("Could not reach backend.")