Update streamlit_app.py

streamlit_app.py

@@ -33,59 +33,40 @@ st.write("Upload your CSV dataset or use a generated sample dataset.")
 # Option to use generated sample dataset
 if st.button("Use Sample Dataset (sample_dataset.csv)"):
     # Path to the sample_dataset.csv relative to streamlit_app.py
-    # Assumes sample_dataset.csv is in the 'data' folder at the root of the project
     sample_csv_path = os.path.join(os.path.dirname(__file__), 'data', 'sample_dataset.csv')
     
     if os.path.exists(sample_csv_path):
         with open(sample_csv_path, 'rb') as f:
             csv_content = f.read()
-        
-        # Prepare the file for upload using 'files' parameter for multipart/form-data
-        # 'file' is the name of the input field Flask expects (request.files['file'])
-        # 'sample_dataset.csv' is the filename
-        # csv_content is the actual binary content of the file
-        # 'text/csv' is the content type
+
         files = {'file': ('sample_dataset.csv', csv_content, 'text/csv')}
-        
+
         try:
-            # Send the file to Flask backend
             response = requests.post(f"{FLASK_API_URL}/preprocess/upload", files=files)
-            response.raise_for_status() # Raise an HTTPError for bad responses (4xx or 5xx)
+            response.raise_for_status()
             processed_data_json = response.json()
-            
-            # Update Streamlit session state with processed data and columns
+
             st.session_state.processed_data = processed_data_json['data']
             st.session_state.processed_columns = processed_data_json['columns']
             st.success("Sample dataset loaded and preprocessed successfully!")
-            
-            # Optional: Display the columns or a snippet of data for confirmation
             st.json(processed_data_json['columns'])
-            
+
         except requests.exceptions.ConnectionError:
             st.error(f"Could not connect to Flask API at {FLASK_API_URL}. Please ensure the backend is running.")
-        except requests.exceptions.HTTPError as http_err: # Catch HTTPError specifically for detailed error
+        except requests.exceptions.HTTPError as http_err:
             st.error(f"HTTP error occurred: {http_err} - Server response: {http_err.response.text}")
         except Exception as e:
             st.error(f"An unexpected error occurred: {e}")
+
     else:
         st.error(f"Sample dataset not found at {sample_csv_path}. Please ensure it exists in your 'data' folder.")
-        
-        if response.status_code == 200:
-            result = response.json()
-            st.session_state.processed_data = result['data']
-            st.session_state.processed_columns = result['columns']
-            st.success("Sample dataset preprocessed successfully!")
-            st.dataframe(pd.DataFrame(st.session_state.processed_data).head()) # Display first few rows
-        else:
-            st.error(f"Error preprocessing sample dataset: {response.json().get('detail', 'Unknown error')}")
-        except Exception as e:
-            st.error(f"Could not load or process sample dataset: {e}")
-
 
+# Option to upload your own CSV
 uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
 if uploaded_file is not None:
     st.info("Uploading and preprocessing data...")
     files = {'file': (uploaded_file.name, uploaded_file.getvalue(), 'text/csv')}
+
     try:
         response = requests.post(f"{FLASK_API_URL}/preprocess/upload", files=files)
         if response.status_code == 200:
@@ -93,7 +74,7 @@ if uploaded_file is not None:
             st.session_state.processed_data = result['data']
             st.session_state.processed_columns = result['columns']
             st.success("File preprocessed successfully!")
-            st.dataframe(pd.DataFrame(st.session_state.processed_data).head()) # Display first few rows
+            st.dataframe(pd.DataFrame(st.session_state.processed_data).head())
         else:
             st.error(f"Error during preprocessing: {response.json().get('detail', 'Unknown error')}")
     except requests.exceptions.ConnectionError: