Update streamlit_app.py

src/streamlit_app.py

@@ -3,46 +3,75 @@ import pandas as pd
 import requests
 import io
 
-st.set_page_config(page_title="SuperKart Sales Prediction", page_icon="🛒")
+# Configure Streamlit page
+st.set_page_config(
+    page_title="SuperKart Sales Prediction",
+    page_icon="🛒",
+    layout="wide"
+)
+
+# Add custom CSS
+st.markdown("""
+    <style>
+    .stButton>button {
+        width: 100%;
+        background-color: #4CAF50;
+        color: white;
+        padding: 10px;
+        border: none;
+        border-radius: 4px;
+        cursor: pointer;
+    }
+    .stButton>button:hover {
+        background-color: #45a049;
+    }
+    </style>
+    """, unsafe_allow_html=True)
 
 st.title("SuperKart Sales Prediction")
-st.write("Upload a CSV file to get sales predictions")
+st.write("Upload your 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": ("data.csv", uploaded_file.getvalue(), "text/csv")}
+    try:
+        # Display the uploaded data
+        df = pd.read_csv(uploaded_file)
+        st.write("Preview of uploaded data:")
+        st.dataframe(df.head())
         
-        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)
+        # Add a button for predictions
+        if st.button("Get Predictions", key="predict_button"):
+            with st.spinner("Getting predictions..."):
+                # Prepare the file for API request
+                files = {"file": ("data.csv", uploaded_file.getvalue(), "text/csv")}
                 
-                # 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)}") 
+                try:
+                    # Make request to the backend API
+                    response = requests.post("ttps://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.success("Predictions generated successfully!")
+                        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(f"Error getting predictions from the API. Status code: {response.status_code}")
+                except Exception as e:
+                    st.error(f"Error: {str(e)}")
+    except Exception as e:
+        st.error(f"Error reading the CSV file: {str(e)}")