Deploy Streamlit frontend with model and CSV

Dockerfile

@@ -1,3 +1,5 @@
-# Backend
-#docker build -t superkart-backend .
-#docker run -p 7860:7860 superkart-backend
+
+#docker build -t superkart-frontend .
+#docker run -p 7860:7860 superkart-frontend
+
+

app.py

@@ -1,62 +1,52 @@
-import joblib
+import streamlit as st
 import pandas as pd
-from flask import Flask, request, jsonify
-from waitress import serve
-
-app = Flask("SuperKart_Quarterly_Sales_Predictor")
-
-# Load model
-model = joblib.load("deployment_files/quarterly_sales_prediction_model_v1_0.joblib")
-
-@app.get('/')
-def home():
-    return "✅ SuperKart API is live"
-
-@app.post('/v1/storesales')
-def predict_sales():
-    try:
-        data = request.get_json()
-        df = pd.DataFrame(data)
-
-        feature_cols = [
-            'Product_Weight', 'Product_Allocated_Area',
-            'Product_MRP', 'Product_Sugar_Content', 'Product_Type',
-            'Store_Establishment_Year', 'Store_Id',  'Store_Type',
-            'Store_Size', 'Store_Location_City_Type'
-        ]
-# 'Product_Sugar_Content', 'Product_Type',
-        predictions = model.predict(df[feature_cols])
-        total_sales = round(predictions.sum(), 2)
-
-        return jsonify({'Total_Store_Sales': total_sales})
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
-
-@app.post('/v1/storesalesbatch')
-def predict_batch():
-    try:
-        file = request.files['file']
-        df = pd.read_csv(file)
-
-         feature_cols = [
-            'Product_Weight', 'Product_Allocated_Area',
-            'Product_MRP', 'Product_Sugar_Content', 'Product_Type',
-            'Store_Establishment_Year', 'Store_Id',  'Store_Type',
-            'Store_Size', 'Store_Location_City_Type'
-        ]
-
-        df['Predicted_Sales'] = model.predict(df[feature_cols])
-        summary = (
-            df.groupby('Store_Id')['Predicted_Sales']
-            .sum()
-            .round(2)
-            .reset_index()
-            .rename(columns={'Predicted_Sales': 'Total_Sales'})
-        )
-
-        return summary.to_dict(orient="records")
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
-
-if __name__ == '__main__':
-    serve(app, host="0.0.0.0", port=7860)
+import requests
+
+st.title("🛒 SuperKart Quarterly Sales Predictor")
+
+# Input form
+st.subheader("🔍 Predict Store's Quarterly Sales")
+
+store_id = st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"])
+product_type = st.selectbox("Product Type", ["Dairy", "Soft Drinks", "Meat", "Canned", "Frozen Foods"])
+sugar_content = st.selectbox("Product Sugar Content", ["Low", "Medium", "High", "No Added Sugar"])
+store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Grocery Store", "Food Mart"])
+city_type = st.selectbox("City Type", ["Urban", "Semi-Urban", "Rural"])
+store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
+
+est_year = st.number_input("Store Establishment Year", min_value=1980, max_value=2025, value=2005)
+weight = st.number_input("Product Weight", min_value=0.0, value=12.0)
+area = st.number_input("Product Allocated Area", min_value=0.0, value=125.0)
+mrp = st.number_input("Product MRP", min_value=0.0, value=120.0)
+
+input_data = pd.DataFrame([{
+    'Store_Id': store_id,
+    'Product_Type': product_type,
+    'Product_Sugar_Content': sugar_content,
+    'Store_Type': store_type,
+    'Store_Location_City_Type': city_type,
+    'Store_Size': store_size,
+    'Store_Establishment_Year': est_year,
+    'Product_Weight': weight,
+    'Product_Allocated_Area': area,
+    'Product_MRP': mrp
+}])
+
+if st.button("Predict Sales"):
+    api_url = "https://<your-backend-space>.hf.space/v1/storesales"
+    response = requests.post(api_url, json=input_data.to_dict(orient='records'))
+    if response.status_code == 200:
+        result = response.json()
+        st.success(f"📦 Predicted Total Sales: ₹{result['Total_Store_Sales']:,.2f}")
+    else:
+        st.error(f"❌ API Error: {response.text}")
+
+st.subheader("📁 Batch Prediction via CSV")
+file = st.file_uploader("Upload CSV", type=["csv"])
+
+if file and st.button("Predict Batch"):
+    response = requests.post("https://<your-backend-space>.hf.space/v1/storesalesbatch", files={"file": file})
+    if response.status_code == 200:
+        st.write(pd.DataFrame(response.json()))
+    else:
+        st.error(f"❌ Batch API Error: {response.text}")

requirements.txt

@@ -1,8 +1,3 @@
-Flask==2.2.2
-waitress==2.1.2
+streamlit==1.34.0
 pandas==2.2.2
-numpy==1.26.4
-joblib==1.4.2
-scikit-learn==1.6.1
-xgboost==2.1.4
-
+requests==2.31.0