msg

app.py

@@ -18,24 +18,27 @@ def predict_sentiment(texts):
     return [sentiment_map[p] for p in torch.argmax(probabilities, dim=-1).tolist()]
 
 # Streamlit UI
+st.title("Sentiment Analysis App")
+st.write("Analyze sentiment from uploaded text data.")
 
-st.sidebar.title("Sentiment Analysis App")
-st.sidebar.write("Upload an Excel file containing text data, and we'll analyze its sentiment.")
-
-uploaded_file = st.file_uploader("Upload Excel File", type=["xlsx", "xls"])
+# Sidebar for File Upload
+st.sidebar.header("Upload File")
+uploaded_file = st.sidebar.file_uploader("Choose an Excel File", type=["xlsx", "xls"])
 
 if uploaded_file is not None:
-    df = pd.read_excel(uploaded_file)
-    st.write("Preview of Uploaded Data:")
+    df = pd.read_excel(uploaded_file, engine="openpyxl")  # Ensure openpyxl is installed
+    st.sidebar.write("✅ File Uploaded Successfully!")
+    
+    st.write("### Preview of Uploaded Data:")
     st.dataframe(df.head())
     
-    text_column = st.selectbox("Select the column containing text", df.columns)
+    text_column = st.sidebar.selectbox("Select Text Column", df.columns)
     
-    if st.button("Analyze Sentiment"):
+    if st.sidebar.button("Analyze Sentiment"):
         df["Sentiment"] = predict_sentiment(df[text_column].astype(str).tolist())
         
         # Display results
-        st.write("Sentiment Analysis Results:")
+        st.write("### Sentiment Analysis Results:")
         st.dataframe(df[[text_column, "Sentiment"]])
         
         # Pie chart
@@ -46,4 +49,5 @@ if uploaded_file is not None:
         st.pyplot(fig)
         
         # Download option
-        st.download_button("Download Results", df.to_csv(index=False).encode('utf-8'), "sentiment_results.csv", "text/csv")
+        st.sidebar.download_button("Download Results", df.to_csv(index=False).encode('utf-8'), "sentiment_results.csv", "text/csv")
+