Removed chart

app.py

@@ -65,64 +65,36 @@ print("Model files ready.")
 # Global model cache
 models = {}
 
-def create_emotion_plot(valence, arousal):
-    """Create a valence-arousal plot with the predicted emotion point"""
-    # Create figure in a process-safe way
-    fig = plt.figure(figsize=(8, 8), dpi=100)
-    ax = fig.add_subplot(111)
+def create_emotion_text(valence, arousal):
+    """Create a text-based representation of the emotion coordinates"""
+    # Helper function to get emotion description
+    def get_emotion_description(v, a):
+        if v > 0.5:
+            if a > 0.5: return "Joyful/Excited"
+            elif a < -0.5: return "Content/Peaceful"
+            else: return "Happy/Pleasant"
+        elif v < -0.5:
+            if a > 0.5: return "Angry/Tense"
+            elif a < -0.5: return "Sad/Depressed"
+            else: return "Unhappy/Unpleasant"
+        else:
+            if a > 0.5: return "Alert/Energetic"
+            elif a < -0.5: return "Tired/Calm"
+            else: return "Neutral"
     
-    # Set background color and style
-    plt.style.use('default')  # Use default style instead of seaborn
-    fig.patch.set_facecolor('#ffffff')
-    ax.set_facecolor('#ffffff')
+    emotion = get_emotion_description(valence, arousal)
     
-    # Create the coordinate system with a light grid
-    ax.grid(True, linestyle='--', alpha=0.2)
-    ax.axhline(y=0, color='#666666', linestyle='-', alpha=0.3, linewidth=1)
-    ax.axvline(x=0, color='#666666', linestyle='-', alpha=0.3, linewidth=1)
+    return f"""
+    ### Predicted Emotions
     
-    # Plot region
-    circle = plt.Circle((0, 0), 1, fill=False, color='#666666', alpha=0.3, linewidth=1.5)
-    ax.add_artist(circle)
+    **Emotion Category:** {emotion}
     
-    # Add labels with nice fonts
-    font = {'family': 'sans-serif', 'weight': 'medium', 'size': 12}
-    label_dist = 1.35  # Increased distance for labels
-    ax.text(label_dist, 0, 'Positive', ha='left', va='center', **font)
-    ax.text(-label_dist, 0, 'Negative', ha='right', va='center', **font)
-    ax.text(0, label_dist, 'Excited', ha='center', va='bottom', **font)
-    ax.text(0, -label_dist, 'Calm', ha='center', va='top', **font)
+    **Coordinates:**
+    - **Valence:** {valence:.2f} (negative → positive)
+    - **Arousal:** {arousal:.2f} (calm → excited)
     
-    # Plot the point with a nice style
-    ax.scatter([valence], [arousal], c='#4f46e5', s=150, zorder=5, alpha=0.8)
-    
-    # Set limits and labels with more padding
-    ax.set_xlim(-1.6, 1.6)
-    ax.set_ylim(-1.6, 1.6)
-    
-    # Format ticks
-    ax.set_xticks([-1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5])
-    ax.set_yticks([-1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5])
-    ax.tick_params(axis='both', which='major', labelsize=10)
-    
-    # Add axis labels with padding
-    ax.set_xlabel('Valence', **font, labelpad=15)
-    ax.set_ylabel('Arousal', **font, labelpad=15)
-    
-    # Remove spines
-    for spine in ax.spines.values():
-        spine.set_visible(False)
-    
-    # Adjust layout with more padding
-    plt.tight_layout(pad=1.5)
-    
-    # Save to a temporary file and return the path
-    temp_path = os.path.join(os.path.dirname(__file__), "output", "emotion_plot.png")
-    os.makedirs(os.path.dirname(temp_path), exist_ok=True)
-    plt.savefig(temp_path, bbox_inches='tight', dpi=100)
-    plt.close(fig)  # Close the figure to free memory
-    
-    return temp_path
+    These values are used to generate music that matches the emotional tone of your image.
+    """
 
 def get_model(conditioning_type):
     """Get or initialize model with specified conditioning"""
@@ -183,7 +155,7 @@ def generate_music(image, conditioning_type, gen_len, temperature, top_p, min_in
     model = get_model(conditioning_type)
     if model is None:
         return {
-            emotion_chart: None,
+            emotion_display: None,
             midi_output: None,
             results: f"⚠️ Error: Failed to initialize {conditioning_type} model. Please check the logs."
         }
@@ -212,24 +184,20 @@ def generate_music(image, conditioning_type, gen_len, temperature, top_p, min_in
         wav_path = convert_midi_to_wav(midi_path)
         if wav_path is None:
             return {
-                emotion_chart: None,
+                emotion_display: None,
                 midi_output: None,
                 results: "⚠️ Error: Failed to convert MIDI to WAV for playback"
             }
         
-        # Create emotion plot and get its path
-        plot_path = create_emotion_plot(valence, arousal)
+        # Create emotion text display
+        emotion_text = create_emotion_text(valence, arousal)
         
         return {
-            emotion_chart: plot_path,
+            emotion_display: emotion_text,
             midi_output: wav_path,
             results: f"""
             **Model Type:** {conditioning_type}
             
-            **Predicted Emotions:**
-            - Valence: {valence:.3f} (negative → positive)
-            - Arousal: {arousal:.3f} (calm → excited)
-            
             **Generation Parameters:**
             - Temperature: {temperature}
             - Top-p: {top_p}
@@ -240,7 +208,7 @@ def generate_music(image, conditioning_type, gen_len, temperature, top_p, min_in
         }
     except Exception as e:
         return {
-            emotion_chart: None,
+            emotion_display: None,
             midi_output: None,
             results: f"⚠️ Error generating music: {str(e)}"
         }
@@ -344,14 +312,11 @@ with gr.Blocks(title="ARIA - Art to Music Generator", theme=gr.themes.Soft(
             )
         
         with gr.Column(scale=2):
-            emotion_chart = gr.Image(
-                label="Predicted Emotions",
-                type="filepath"
-            )
             midi_output = gr.Audio(
                 type="filepath",
                 label="Generated Music"
             )
+            emotion_display = gr.Markdown()
             results = gr.Markdown()
     
     gr.Markdown("""
@@ -385,7 +350,7 @@ with gr.Blocks(title="ARIA - Art to Music Generator", theme=gr.themes.Soft(
     generate_btn.click(
         fn=generate_music_wrapper,
         inputs=[image_input, conditioning_type, gen_len, note_temperature, rest_temperature, top_p, min_instruments],
-        outputs=[emotion_chart, midi_output, results]
+        outputs=[emotion_display, midi_output, results]
     )
 
 # Launch app