Update app.py

app.py

@@ -80,45 +80,54 @@ def main():
     dataset_url = st.text_input("Paste the URL of the dataset")
 
     if uploaded_file or dataset_url:
-        if dataset_url:
-            st.info("Downloading dataset from URL...")
-            uploaded_file = download_dataset(dataset_url)
-        
-        if uploaded_file:
-            st.success("File uploaded successfully!")
-            st.write("Processing the file...")
-
-            # Load dataset
-            df = pd.read_csv(uploaded_file)
-            st.write("**Original Dataset**")
-            st.dataframe(df)
-
-            # Process data
-            st.info("Cleaning and simplifying the dataset...")
-            df_cleaned = process_data(df)
-
-            # Display cleaned data
-            st.write("**Cleaned Dataset**")
-            st.dataframe(df_cleaned)
-
-            # Chunk data
-            st.info("Creating chunks for AI models...")
-            processed_files = chunk_dataset(df_cleaned)
-
-            # Allow download of processed chunks
-            st.success(f"Processing complete! {len(processed_files)} chunk(s) created.")
-            for file_name in processed_files:
-                with open(file_name, 'rb') as file:
-                    st.download_button(
-                        label=f"Download {file_name}",
-                        data=file,
-                        file_name=file_name,
-                        mime="text/csv",
-                    )
-
-            # Cleanup generated files
-            for file_name in processed_files:
-                os.remove(file_name)
+        try:
+            if dataset_url:
+                st.info("Downloading dataset from URL...")
+                uploaded_file = download_dataset(dataset_url)
+            
+            if uploaded_file:
+                st.success("File uploaded successfully!")
+                st.write("Processing the file...")
+
+                # Load dataset
+                if hasattr(uploaded_file, 'read'):  # Streamlit file upload
+                    df = pd.read_csv(uploaded_file)
+                else:  # File from URL download or local path
+                    with open(uploaded_file, 'r') as file:
+                        df = pd.read_csv(file)
+
+                st.write("**Original Dataset**")
+                st.dataframe(df)
+
+                # Process data
+                st.info("Cleaning and simplifying the dataset...")
+                df_cleaned = process_data(df)
+
+                # Display cleaned data
+                st.write("**Cleaned Dataset**")
+                st.dataframe(df_cleaned)
+
+                # Chunk data
+                st.info("Creating chunks for AI models...")
+                processed_files = chunk_dataset(df_cleaned)
+
+                # Allow download of processed chunks
+                st.success(f"Processing complete! {len(processed_files)} chunk(s) created.")
+                for file_name in processed_files:
+                    with open(file_name, 'rb') as file:
+                        st.download_button(
+                            label=f"Download {file_name}",
+                            data=file,
+                            file_name=file_name,
+                            mime="text/csv",
+                        )
+
+                # Cleanup generated files
+                for file_name in processed_files:
+                    os.remove(file_name)
+
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
 
 if __name__ == "__main__":
     main()