Update app.py

app.py

@@ -32,15 +32,14 @@ def inspect_file(file):
         return "No file uploaded.", [], "No preview available."
     file_path = file.name
     _, file_ext = os.path.splitext(file_path)
-
     file_ext = file_ext.lower()
+
     if file_ext == ".fif":
-        # FIF files: We know they're MNE compatible
-        # No columns to choose from, just proceed with default analysis
+        # FIF files: MNE compatible, no columns needed
         return (
-            "FIF file detected. No need for time column selection. Default sampling frequency will be read from file.",
+            "FIF file detected. No need for time column selection. The file's sampling frequency will be used.",
             [],
-            "FIF file doesn't require further inspection."
+            "FIF file doesn't require column inspection."
         )
     elif file_ext == ".csv":
         # Read a small portion of the CSV to determine columns
@@ -52,14 +51,13 @@ def inspect_file(file):
         cols = list(df.columns)
         preview = df.head().to_markdown()
         return (
-            "CSV file detected. Select a time column if available, or leave it blank and specify a default frequency.",
+            "CSV file detected. Select a time column if available, or choose (No time column) and specify a default frequency.",
             cols,
             preview
         )
     else:
         return "Unsupported file format.", [], "No preview available."
 
-
 def load_eeg_data(file_path, default_sfreq=256.0, time_col='time'):
     """
     Load EEG data with flexibility.
@@ -77,8 +75,8 @@ def load_eeg_data(file_path, default_sfreq=256.0, time_col='time'):
     elif file_ext == '.csv':
         df = pd.read_csv(file_path)
 
-        # If time_col is specified and in df, use it to compute sfreq
         if time_col and time_col in df.columns:
+            # Use the selected time column to compute sfreq
             time = df[time_col].values
             data_df = df.drop(columns=[time_col])
 
@@ -88,20 +86,28 @@ def load_eeg_data(file_path, default_sfreq=256.0, time_col='time'):
                     data_df = data_df.drop(columns=[col])
 
             if len(time) < 2:
-                # Not enough time points, fallback to default_sfreq
+                # Not enough time points to compute sfreq, fallback
                 sfreq = default_sfreq
             else:
                 # Compute sfreq from time
-                sfreq = 1.0 / np.mean(np.diff(time))
+                dt = np.mean(np.diff(time))
+                # Ensure dt is positive
+                if dt <= 0:
+                    sfreq = default_sfreq
+                else:
+                    sfreq = 1.0 / dt
         else:
             # No time column used, assume default_sfreq
-            # Drop non-numeric columns
             for col in df.columns:
                 if not pd.api.types.is_numeric_dtype(df[col]):
                     df = df.drop(columns=[col])
             data_df = df
             sfreq = default_sfreq
 
+        # Ensure sfreq is positive
+        if sfreq <= 0:
+            sfreq = 256.0  # fallback if something odd happens
+
         ch_names = list(data_df.columns)
         data = data_df.values.T  # shape: (n_channels, n_samples)
 
@@ -117,9 +123,15 @@ def load_eeg_data(file_path, default_sfreq=256.0, time_col='time'):
 def analyze_eeg(file, default_sfreq, time_col):
     if time_col == "(No time column)":
         time_col = None
-    raw = load_eeg_data(file.name, default_sfreq=float(default_sfreq), time_col=time_col)
 
-    psd, freqs = mne.time_frequency.psd_welch(raw, fmin=1, fmax=40)
+    fs = float(default_sfreq)
+    if fs <= 0:
+        fs = 256.0
+
+    raw = load_eeg_data(file.name, default_sfreq=fs, time_col=time_col)
+
+    # Use the directly imported psd_welch function
+    psd, freqs = psd_welch(raw, fmin=1, fmax=40)
     alpha_power = compute_band_power(psd, freqs, 8, 12)
     beta_power = compute_band_power(psd, freqs, 13, 30)
 
@@ -130,7 +142,6 @@ def analyze_eeg(file, default_sfreq, time_col):
 
     prompt = f"""You are a neuroscientist analyzing EEG features.
 Data Summary: {data_summary}
-
 Provide a concise, user-friendly interpretation of these findings in simple terms.
 """
     inputs = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
@@ -140,49 +151,43 @@ Provide a concise, user-friendly interpretation of these findings in simple term
     summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return summary
 
-
-#########################
-# BUILD THE GRADIO INTERFACE
-#########################
-
-# Step 1: Inspect file
 def preview_file(file):
     msg, cols, preview = inspect_file(file)
-    # Add placeholder choice
-    if len(cols) > 0:
-        cols = ["(No time column)"] + cols
-        default_value = "(No time column)"
-    else:
-        cols = []
-        default_value = None
-    
-    # Use gr.update(...) for the dropdown output
-    return msg, gr.update(choices=cols, value=default_value), preview
-
+    # Always include (No time column) as the first choice
+    # If no columns were found, we still have (No time column) as an option
+    cols = ["(No time column)"] + cols
+    default_value = "(No time column)"
 
+    # Return an update dict for the dropdown
+    return msg, gr.update(choices=cols, value=default_value), preview
 
 with gr.Blocks() as demo:
     gr.Markdown("# NeuroNarrative-Lite: EEG Summary with Flexible Preprocessing")
     gr.Markdown(
-        "Upload an EEG file (FIF or CSV). If it's CSV, we will inspect the file and let you choose a time column. "
-        "If no suitable time column is found, leave it blank and provide a default sampling frequency."
+        "Upload an EEG file (FIF or CSV). If it's CSV, click 'Inspect File' to preview columns. "
+        "Select a time column if available or '(No time column)' if not. "
+        "If no time column is chosen, provide a default sampling frequency. "
+        "Then click 'Run Analysis'."
     )
 
     file_input = gr.File(label="Upload your EEG data (FIF or CSV)")
     preview_button = gr.Button("Inspect File")
     msg_output = gr.Markdown()
-    cols_dropdown = gr.Dropdown(label="Select Time Column (optional)", interactive=True)
+    # Allow custom values in case something goes off
+    cols_dropdown = gr.Dropdown(label="Select Time Column (optional)", allow_custom_value=True, interactive=True)
     preview_output = gr.Markdown()
 
     preview_button.click(preview_file, inputs=[file_input], outputs=[msg_output, cols_dropdown, preview_output])
 
-    default_sfreq_input = gr.Textbox(label="Default Sampling Frequency (Hz) if no time column", value="256")
+    default_sfreq_input = gr.Textbox(label="Default Sampling Frequency (Hz) if no time column", value="100")
     analyze_button = gr.Button("Run Analysis")
     result_output = gr.Textbox(label="Analysis Summary")
 
-    analyze_button.click(analyze_eeg, 
-                          inputs=[file_input, default_sfreq_input, cols_dropdown],
-                          outputs=[result_output])
+    analyze_button.click(
+        analyze_eeg, 
+        inputs=[file_input, default_sfreq_input, cols_dropdown],
+        outputs=[result_output]
+    )
 
 if __name__ == "__main__":
     demo.launch()