Update streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,48 @@
-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 pandas as pd
+import requests
+import io
+
+st.set_page_config(page_title="SuperKart Sales Prediction", page_icon="🛒")
+
+st.title("SuperKart Sales Prediction")
+st.write("Upload a CSV file to get sales predictions")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+
+if uploaded_file is not None:
+    # Display the uploaded data
+    df = pd.read_csv(uploaded_file)
+    st.write("Preview of uploaded data:")
+    st.dataframe(df.head())
+    
+    if st.button("Get Predictions"):
+        # Prepare the file for API request
+        files = {"file": ("SuperKart.csv", uploaded_file.getvalue(), "text/csv")}
+        
+        try:
+            # Make request to the backend API
+            response = requests.post("https://huggingface.co/spaces/abhishek-kumar/superkart_sales_backend/predict", files=files)
+            
+            if response.status_code == 200:
+                predictions = response.json()["predictions"]
+                
+                # Add predictions to the dataframe
+                df["Predicted_Sales"] = predictions
+                
+                st.write("Predictions:")
+                st.dataframe(df)
+                
+                # Download button for results
+                csv = df.to_csv(index=False)
+                st.download_button(
+                    label="Download predictions",
+                    data=csv,
+                    file_name="predictions.csv",
+                    mime="text/csv"
+                )
+            else:
+                st.error("Error getting predictions from the API")
+        except Exception as e:
+            st.error(f"Error: {str(e)}")