Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,72 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import numpy as np
+import requests
+
+
+# Streamlit UI for Price Prediction
+st.title("SuperKart Sales Predictor")
+st.write("This tool predicts the sales based on various store parameters.")
+
+st.subheader("Enter the store details(Single Predication):")
+
+# Collect user input
+product_weight = st.number_input("Product Weight (in kg)", min_value=1.0, max_value=30.0)
+product_sugar = st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
+product_area = st.slider("Allocated Area (sq m)", min_value=0.0, max_value=1.0, step=0.01)
+product_type = st.selectbox("Product Type", ["Fruits and Vegetables", "Snack Foods", "Frozen Foods", "Dairy", "Household","Baking Goods", "Canned", "Health and Hygiene", "Meat", "Breads","Hard Drinks", "Soft Drinks", "Seafood", "Starchy Foods", "Others"])
+product_mrp = st.number_input("Product MRP", min_value=10.0, max_value=300.0)
+store_year = st.number_input("Store Establishment Year", min_value=1980, max_value=2025)
+store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+store_city = st.selectbox("City Type", ["Tier 1", "Tier 2", "Tier 3"])
+store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Food Mart", "Departmental Store"])
+
+# Prepare input
+if st.button("Predict Sales"):
+    input_df = {
+        "Product_Weight": product_weight,
+        "Product_Sugar_Content": product_sugar,
+        "Product_Allocated_Area": product_area,
+        "Product_Type": product_type,
+        "Product_MRP": product_mrp,
+        "Store_Establishment_Year": 2025 - store_year,  # we have modified this to get the  store age
+        "Store_Size": store_size,
+        "Store_Location_City_Type": store_city,
+        "Store_Type": store_type
+    }
+    response = requests.post("https://harasar-SuperKartBackend.hf.space/v1/customer", json=input_df)    # enter user name and space name before running the cell
+    if response.status_code == 200:
+        result = response.json()
+        churn_prediction = result["predicted_sales"]  # Extract only the value
+        st.write(f"Based on the information provided, the sproject sales is likely to {churn_prediction}.")
+    else:
+        st.error("Error in API request")
+
+#Batch Prediction
+uploaded_file = st.file_uploader("Upload CSV file", type=["csv"])
+
+if st.button("Predict for Batch"):
+    if uploaded_file is not None:
+        try:
+            # Convert uploaded file to a DataFrame
+            df = pd.read_csv(uploaded_file)
+
+            # Convert DataFrame to CSV bytes like your working script
+            csv_bytes = df.to_csv(index=False).encode('utf-8')
+
+            # Send POST request with raw bytes
+            response = requests.post(
+                "https://harasar-SuperKartBackend.hf.space/v1/customerbatch",
+                files={"file": ("SuperKart.csv", csv_bytes, "text/csv")}
+            )
+
+            if response.status_code == 200:
+                st.success("Batch prediction successful!")
+                st.write(response.json())
+            else:
+                st.error(f"Error {response.status_code}: {response.text}")
 
-"""
-# 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])),
-    ))
+        except Exception as e:
+            st.error(f"Upload failed: {e}")
+    else:
+        st.warning("Please upload a CSV file first.")