REFAC: Enhance emotion vector creation and add ECI timeline calculation in Streamlit app

src/streamlit_app.py

@@ -55,6 +55,7 @@ whisper_model, text_classifier, ser_model, ser_feature_extractor = load_models()
 def create_unified_vector(scores_dict, mapping_dict):
     vector = np.zeros(len(UNIFIED_EMOTIONS))
     total_score = 0
+    # Use .items() to iterate over keys and values
     for label, score in scores_dict.items():
         unified_label = mapping_dict.get(label)
         if unified_label in UNIFIED_EMOTIONS:
@@ -169,7 +170,8 @@ if uploaded_file is not None:
             fer_avg_scores = get_avg_unified_scores(fer_df)
             ser_avg_scores = get_avg_unified_scores(ser_df)
             ter_avg_scores = get_avg_unified_scores(ter_df)
-
+            
+            # Use the raw dictionaries for vector creation, not the mapped ones
             fer_vector = create_unified_vector(fer_avg_scores, {e:e for e in UNIFIED_EMOTIONS})
             ser_vector = create_unified_vector(ser_avg_scores, {e:e for e in UNIFIED_EMOTIONS})
             text_vector = create_unified_vector(ter_avg_scores, {e:e for e in UNIFIED_EMOTIONS})
@@ -197,19 +199,21 @@ if uploaded_file is not None:
                 
                 full_index = np.arange(0, duration, 0.5)
                 combined_df = pd.DataFrame(index=full_index)
-
+                
                 # --- NEW: ECI Timeline Calculation ---
                 eci_timeline = {}
                 for t_stamp in full_index:
                     vectors = []
-                    # Get interpolated facial vector if available
-                    if not fer_df.empty and t_stamp in fer_df.index:
-                        vectors.append(create_unified_vector(fer_df.loc[t_stamp].to_dict(), {e:e for e in UNIFIED_EMOTIONS}))
-                    # Get speech vector if available
-                    if not ser_df.empty and int(t_stamp) in ser_df.index:
+                    
+                    # Interpolate to get a value for any timestamp
+                    fer_scores = fer_df.reindex(fer_df.index.union([t_stamp])).interpolate(method='linear').loc[t_stamp]
+                    if not fer_scores.isnull().all():
+                        vectors.append(create_unified_vector(fer_scores.to_dict(), {e:e for e in UNIFIED_EMOTIONS}))
+
+                    if int(t_stamp) in ser_df.index:
                         vectors.append(create_unified_vector(ser_df.loc[int(t_stamp)].to_dict(), {e:e for e in UNIFIED_EMOTIONS}))
-                    # Get text vector if available
-                    if not ter_df.empty and int(t_stamp) in ter_df.index:
+                    
+                    if int(t_stamp) in ter_df.index:
                         vectors.append(create_unified_vector(ter_df.loc[int(t_stamp)].to_dict(), {e:e for e in UNIFIED_EMOTIONS}))
                     
                     if len(vectors) >= 2:
@@ -228,10 +232,9 @@ if uploaded_file is not None:
                     ter_df_resampled = ter_df.reindex(ter_df.index.union(full_index)).interpolate(method='linear').reindex(full_index)
                     for e in UNIFIED_EMOTIONS: combined_df[f'Text_{e}'] = ter_df_resampled.get(e, 0.0)
                 
-                # Add ECI timeline to the DataFrame
                 if eci_timeline:
-                    eci_df = pd.Series(eci_timeline, name="ECI")
-                    combined_df['ECI'] = eci_df
+                    eci_series = pd.Series(eci_timeline).reindex(full_index).interpolate(method='linear')
+                    combined_df['ECI'] = eci_series
 
                 combined_df.fillna(0, inplace=True)
                 
@@ -246,7 +249,6 @@ if uploaded_file is not None:
                         if emotion in colors:
                             ax.plot(combined_df.index, combined_df[col], label=f'{modality} {emotion.capitalize()}', color=colors[emotion], linestyle=styles[modality], alpha=0.7)
                     
-                    # Plot ECI on the same axis
                     if 'ECI' in combined_df.columns:
                         ax.plot(combined_df.index, combined_df['ECI'], label='Emotion Consistency', color='black', linewidth=2.5, alpha=0.9)