Update app.py

app.py

@@ -154,9 +154,13 @@ else:
 st.write("selected_images info:", selected_images)
 st.write("selected_file_names info:", selected_file_names)
 st.write("results info:", results)
-        
+
+
+
 # Generate DataFrame from results
 if st.button("Generate DataFrame") and results:
+    st.write("results info inner loop:", results)
+    
     # Initialize an empty dictionary to store the scores
     emotion_scores = {
         'Neutral': [],
@@ -164,21 +168,25 @@ if st.button("Generate DataFrame") and results:
         'Surprise': [],
         'Disgust': [],
         'Angry': [],
+        # Add other emotions if necessary
         'Sad': [],  # Add this if you have 'sad' scores in your results
         'Fear': []   # Add this if you have 'fear' scores in your results
     }
 
     # Iterate over the results and populate the dictionary
     for result_set in results:
+        # Initialize a dictionary for the current set with zeros
+        current_scores = {emotion: 0 for emotion in emotion_scores.keys()}
+        
         for result in result_set:
-            # Capitalize the label and add the score to the corresponding list
+            # Capitalize the label and update the score in the current set
             emotion = result['label'].capitalize()
-            score = result['score']
-            if emotion in emotion_scores:
-                emotion_scores[emotion].append(score)
-            else:
-                # If the emotion is not in the dictionary, initialize a new list
-                emotion_scores[emotion] = [score]
+            score = round(result['score'], 4)  # Round the score to 4 decimal places
+            current_scores[emotion] = score
+        
+        # Add the current scores to the emotion_scores dictionary
+        for emotion, score in current_scores.items():
+            emotion_scores[emotion].append(score)
 
     # Convert the dictionary into a pandas DataFrame
     df_emotions = pd.DataFrame(emotion_scores)
@@ -189,3 +197,4 @@ if st.button("Generate DataFrame") and results:
     # Optional: Save the DataFrame to a CSV file
     df_emotions.to_csv('emotion_scores.csv', index=False)
     st.success('DataFrame generated and saved as emotion_scores.csv')
+