updated results ++ styling

app.py

@@ -221,38 +221,62 @@ def plot_brain_slices(data, learning_stage):
     
     return fig
 
+def interpret_learning_stage(score):
+    """human-readable learning stage"""
+    if score < 0.2:
+        return "NOVICE: minimal task familiarity, primarily exploratory behavior"
+    elif score < 0.4:
+        return "EARLY LEARNING: basic pattern recognition emerging"
+    elif score < 0.6:
+        return "INTERMEDIATE: developing systematic approach"
+    elif score < 0.8:
+        return "ADVANCED: robust strategy application"
+    else:
+        return "EXPERT: automated processing, highly optimized performance"
+
 def plot_results(data, region_acts, temporal_pattern, learning_stage):
-    fig = plt.figure(figsize=(15,10))
-    gs = gridspec.GridSpec(2, 2)
+    # 16:9 aspect for modern displays
+    fig = plt.figure(figsize=(16, 9))
+    
+    # main brain plot: 60% height
+    gs = gridspec.GridSpec(2, 2, height_ratios=[6, 4])
     
-    # brain slices
-    ax1 = plt.subplot(gs[0,:])
+    # brain visualization (now larger)
+    ax1 = plt.subplot(gs[0, :])
     mean_activation = data.mean(axis=0)
     slice_idx = mean_activation.shape[-1]//2
     brain_slice = mean_activation[...,slice_idx]
     
-    # find most active region
+    # find activation peaks
     peak_coords = np.unravel_index(np.argmax(brain_slice), brain_slice.shape)
+    peak_val = brain_slice[peak_coords]
     
+    # enhanced brain viz
     im = ax1.imshow(brain_slice.T, cmap='hot')
     plt.colorbar(im, ax=ax1)
-    ax1.plot(peak_coords[0], peak_coords[1], 'r*', markersize=15, 
-             label=f'peak ({peak_coords[0]}, {peak_coords[1]})')
-    ax1.legend()
-    ax1.set_title(f'brain activation (z={slice_idx})\nlearning stage: {learning_stage:.3f}')
+    ax1.plot(peak_coords[0], peak_coords[1], 'r*', markersize=15)
+    
+    learning_desc = interpret_learning_stage(learning_stage)
+    ax1.set_title(f'brain activation map (axial slice z={slice_idx})\n{learning_desc}', 
+                  fontsize=12, pad=20)
+    ax1.axis('off')
     
-    # region activations
-    ax2 = plt.subplot(gs[1,0])
-    max_region = np.argmax(region_acts)
-    sns.heatmap(region_acts.reshape(1,-1), cmap='RdBu_r', center=0, ax=ax2)
-    ax2.set_title(f'region activations\nmost active: {max_region}')
-    ax2.set_xlabel('brain region')
+    # region plot (lower left)
+    ax2 = plt.subplot(gs[1, 0])
+    top_n = 5
+    region_ranking = np.argsort(-region_acts.flatten())[:top_n]
+    region_data = region_acts.reshape(1,-1)
+    sns.heatmap(region_data, cmap='RdBu_r', center=0, ax=ax2)
+    ax2.set_title('regional activity profile\n' + 
+                  'top regions: ' + ', '.join(f'{r}' for r in region_ranking))
     
-    # temporal pattern
-    ax3 = plt.subplot(gs[1,1])
-    ax3.plot(temporal_pattern.squeeze())
-    ax3.set_title('temporal dynamics')
-    ax3.set_xlabel('time')
+    # temporal dynamics (lower right)
+    ax3 = plt.subplot(gs[1, 1])
+    ax3.plot(temporal_pattern.squeeze(), 'k-', linewidth=2)
+    ax3.set_title('temporal evolution')
+    ax3.set_xlabel('time (volumes)')
+    ax3.set_ylabel('activation (a.u.)')
+    ax3.grid(True, alpha=0.3)
     
     plt.tight_layout()
     return fig
@@ -315,15 +339,13 @@ def process_fmri(file_obj):
             except Exception as e:
                 return f"error in {stage} model: {str(e)}", None
         
-        # enhanced results text w/ peak info
-        stage_results = "\n".join([
-            f"{stage.upper()} MODEL:"
-            f"\nlearning stage: {res['learning_stage']:.3f}"
-            f"\npeak region: {np.argmax(res['region_activation'])}"
-            f"\npeak activation: {np.max(res['region_activation']):.3f}"
-            f"\n"
-            for stage, res in results.items()
-        ])
+        stage_results = "fMRI ANALYSIS SUMMARY\n" + "="*50 + "\n\n"
+        for stage, res in results.items():
+            stage_results += f"MODEL: {stage}\n"
+            stage_results += f"learning assessment: {interpret_learning_stage(res['learning_stage'])}\n"
+            stage_results += f"confidence score: {res['learning_stage']:.3f}\n"
+            stage_results += f"dominant regions: {', '.join(str(r) for r in np.argsort(-res['region_activation'])[:3])}\n"
+            stage_results += "-"*50 + "\n\n"
         
         return stage_results, figs[0]
         
@@ -332,16 +354,41 @@ def process_fmri(file_obj):
 
 iface = gr.Interface(
     fn=process_fmri,
-    inputs=gr.File(label="upload 4D fMRI nifti (.nii/.nii.gz)"),
+    inputs=gr.File(
+        label="fMRI Data Input",
+        file_types=[".nii", ".nii.gz"],
+        file_count="single"
+    ),
     outputs=[
-        gr.Textbox(label="classification results"),
-        gr.Plot(label="brain activation + analysis")
+        gr.Textbox(
+            label="Analysis Results",
+            placeholder="Upload an fMRI scan to begin analysis...",
+            lines=10
+        ),
+        gr.Plot(label="Neural Activity Analysis")
     ],
-    title="fmri learning stage classifier",
-    description="upload a 4D fMRI nifti file to classify learning stages and visualize brain patterns",
+    title="🧠 fMRI Learning Stage Classifier",
+    description="""
+    ## Neural Learning Stage Analysis
+    
+    Upload a 4D fMRI scan to:
+    - Classify learning stage progression
+    - Visualize brain activation patterns
+    - Analyze regional activity profiles
+    - Track temporal dynamics
+    
+    Supports standard NIFTI format (.nii/.nii.gz)
+    """,
+    article="""
+    ### interpretation guide
+    
+    - learning stage: ranges from novice (0.0) to expert (1.0)
+    - brain map: warmer colors = higher activation
+    - regional profile: shows activity across 116 brain regions
+    - temporal pattern: activation changes over time
+    
+    model trained on weatherprediction task fMRI data from openneuro ds000052
+    """,
+    theme="default",
     examples=[],
-    cache_examples=False
-)
-
-if __name__ == "__main__":
-    iface.launch()
+    cache_examples=False