Update app.py

app.py

@@ -8,6 +8,10 @@ import matplotlib.pyplot as plt
 # Disable PyplotGlobalUseWarning
 st.set_option('deprecation.showPyplotGlobalUse', False)
 
+# Initialize session state for results if not already present
+if 'results' not in st.session_state:
+    st.session_state['results'] = []
+
 # Create an image classification pipeline with scores
 pipe = pipeline("image-classification", model="trpakov/vit-face-expression", top_k=None)
 
@@ -159,45 +163,45 @@ st.write("results info:", results)
 
 # Generate DataFrame from results
 if st.button("Generate DataFrame"):
-
-#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': [],
-        'Happy': [],
-        '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 update the score in the current set
-            emotion = result['label'].capitalize()
-            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)
-
-    # Display the DataFrame
-    st.write(df_emotions)
-
-    # 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')
-
+    # Access the results from the session state
+    results = st.session_state['results']
+    if results:
+        st.write("results info inner loop:", results)
+        # Initialize an empty dictionary to store the scores
+        emotion_scores = {
+            'Neutral': [],
+            'Happy': [],
+            '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 update the score in the current set
+                emotion = result['label'].capitalize()
+                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)
+
+        # Display the DataFrame
+        st.write(df_emotions)
+
+        # 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')
+    else:
+        st.error("No results to generate DataFrame. Please predict emotions first.")