REFAC: Enhance UI layout for video analysis results and improve logging configuration in Streamlit app

src/streamlit_app.py

@@ -29,11 +29,7 @@ st.title("AffectLink: Post-Hoc Emotion Analysis")
 st.write("Upload a short video clip (under 30 seconds) to see a multimodal emotion analysis.")
 
 # --- Logger Configuration ---
-# [Logger setup remains the same]
 logging.basicConfig(level=logging.INFO)
-logging.getLogger('deepface').setLevel(logging.ERROR)
-logging.getLogger('huggingface_hub').setLevel(logging.WARNING)
-logging.getLogger('moviepy').setLevel(logging.ERROR)
 
 # --- Emotion Mappings ---
 UNIFIED_EMOTIONS = ['angry', 'happy', 'sad', 'neutral']
@@ -77,7 +73,6 @@ uploaded_file = st.file_uploader("Choose a video file...", type=["mp4", "mov", "
 
 if uploaded_file is not None:
     temp_video_path = None
-    video_clip_for_duration = None
     try:
         with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tfile:
             tfile.write(uploaded_file.read())
@@ -165,11 +160,17 @@ if uploaded_file is not None:
             
             similarities = [cosine_similarity([fer_vector], [text_vector])[0][0], cosine_similarity([fer_vector], [ser_vector])[0][0], cosine_similarity([ser_vector], [text_vector])[0][0]]
             avg_similarity = np.nanmean([s for s in similarities if not np.isnan(s)])
+            
+            # --- NEW LAYOUT ---
+            # Display the full-width transcription first
+            st.subheader("Transcription")
+            st.markdown(f"> *{full_transcription}*")
+            st.divider()
 
+            # Now create two columns for the summary and the plot
             col1, col2 = st.columns([1, 2])
             with col1:
                 st.subheader("Multimodal Summary")
-                st.write(f"**Transcription:** \"{full_transcription}\"")
                 st.metric("Dominant Facial Emotion", dominant_fer)
                 st.metric("Dominant Text Emotion", dominant_text)
                 st.metric("Dominant Speech Emotion", dominant_ser)