TEST: Deploy minimal app to isolate moviepy installation issue"

requirements.txt

@@ -1,27 +1,2 @@
---extra-index-url https://download.pytorch.org/whl/cpu
-
-# Pin the main UI components to recent, stable versions
-streamlit==1.35.0
-# streamlit-camera removed as st.camera_input is native
-streamlit-autorefresh==1.0.1
-
-# Library for video/audio file handling
-moviepy
-
-# Pin ML/AI libraries to modern, known-good versions
-transformers==4.40.1
-deepface==0.0.94
-openai-whisper==20231117
-
-# Pin frameworks to ensure CPU versions and prevent build timeouts
-tensorflow-cpu==2.16.1
-tf-keras==2.16.0
-torch==2.7.0
-torchaudio==2.7.0
-
-# Pin data/audio libraries for stability
-pandas==2.2.2
-numpy==1.26.4
-soundfile==0.12.1
-librosa==0.10.1
-scipy==1.13.0
+streamlit
+moviepy

requirements_full.txt

@@ -0,0 +1,26 @@
+--extra-index-url https://download.pytorch.org/whl/cpu
+
+# Pin the main UI components to recent, stable versions
+streamlit==1.35.0
+streamlit-autorefresh==1.0.1
+
+# Library for video/audio file handling
+moviepy
+
+# Pin ML/AI libraries to modern, known-good versions
+transformers==4.40.1
+deepface==0.0.94
+openai-whisper==20231117
+
+# Pin frameworks to ensure CPU versions and prevent build timeouts
+tensorflow-cpu==2.16.1
+tf-keras==2.16.0
+torch==2.7.0
+torchaudio==2.7.0
+
+# Pin data/audio libraries for stability
+pandas==2.2.2
+numpy==1.26.4
+soundfile==0.12.1
+librosa==0.10.1
+scipy==1.13.0

src/streamlit_app.py

@@ -1,178 +1,12 @@
-import os
 import streamlit as st
-
-# Set home directories for model caching to the writable /tmp folder
-os.environ['DEEPFACE_HOME'] = '/tmp/.deepface'
-os.environ['HF_HOME'] = '/tmp/huggingface'
-
-import numpy as np
-import torch
-import whisper
-from transformers import pipeline, AutoModelForAudioClassification, AutoFeatureExtractor
-from deepface import DeepFace
-import logging
-import soundfile as sf
-from scipy.io.wavfile import write as write_wav
-import tempfile
-from PIL import Image
-import cv2
 from moviepy.editor import VideoFileClip
 
-# Set home directories for model caching inside the app's writable directory
-os.environ['DEEPFACE_HOME'] = '/tmp/.deepface'
-os.environ['HF_HOME'] = '/tmp/huggingface'
-
-# --- Page Configuration ---
-st.set_page_config(
-    page_title="AffectLink Batch Demo",
-    page_icon="😊",
-    layout="wide"
-)
-
-st.title("AffectLink: Post-Hoc Emotion Analysis")
-st.write("Upload a short video clip to analyze facial expressions, speech-to-text, and the emotional tone of the audio.")
-
-# --- Logger Configuration ---
-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 = ['neutral', 'happy', 'sad', 'angry']
-TEXT_TO_UNIFIED = {
-    'neutral': 'neutral', 'joy': 'happy', 'sadness': 'sad', 'anger': 'angry',
-    'fear': None, 'surprise': None, 'disgust': None
-}
-SER_TO_UNIFIED = {
-    'neu': 'neutral', 'hap': 'happy', 'sad': 'sad', 'ang': 'angry'
-}
-AUDIO_SAMPLE_RATE = 16000
-
-# --- Model Loading ---
-@st.cache_resource
-def load_models():
-    with st.spinner("Loading AI models, this may take a moment..."):
-        whisper_model = whisper.load_model("base", download_root="/tmp/whisper_cache")
-        text_classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=None)
-        ser_model_name = "superb/hubert-large-superb-er"
-        ser_feature_extractor = AutoFeatureExtractor.from_pretrained(ser_model_name)
-        ser_model = AutoModelForAudioClassification.from_pretrained(ser_model_name)
-        return whisper_model, text_classifier, ser_model, ser_feature_extractor
-
-whisper_model, text_classifier, ser_model, ser_feature_extractor = load_models()
-
-
-# --- UI and Processing Logic ---
-uploaded_file = st.file_uploader("Choose a video file...", type=["mp4", "mov", "avi"])
-
-if uploaded_file is not None:
-    # Save the uploaded file to a temporary location
-    with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tfile:
-        tfile.write(uploaded_file.read())
-        temp_video_path = tfile.name
-
-    st.video(temp_video_path)
-
-    if st.button("Analyze Video"):
-        facial_analysis_results = []
-        audio_analysis_results = {}
-
-        # --- Video Processing for Facial Emotion ---
-        with st.spinner("Analyzing video for facial expressions..."):
-            try:
-                cap = cv2.VideoCapture(temp_video_path)
-                fps = cap.get(cv2.CAP_PROP_FPS)
-                frame_count = 0
-                while cap.isOpened():
-                    ret, frame = cap.read()
-                    if not ret:
-                        break
-                    
-                    # Process one frame per second
-                    if frame_count % int(fps) == 0:
-                        timestamp = frame_count / fps
-                        analysis = DeepFace.analyze(frame, actions=['emotion'], enforce_detection=False, silent=True)
-                        if isinstance(analysis, list) and len(analysis) > 0:
-                            dominant_emotion = analysis[0]['dominant_emotion']
-                            facial_analysis_results.append((timestamp, dominant_emotion.capitalize()))
-                    
-                    frame_count += 1
-                cap.release()
-            except Exception as e:
-                st.error(f"An error occurred during facial analysis: {e}")
-
-
-        # --- Audio Extraction and Processing ---
-        with st.spinner("Extracting and analyzing audio..."):
-            try:
-                # Extract audio using moviepy
-                video_clip = VideoFileClip(temp_video_path)
-                with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as taudio:
-                    video_clip.audio.write_audiofile(taudio.name, fps=AUDIO_SAMPLE_RATE, logger=None)
-                    temp_audio_path = taudio.name
-
-                # 1. Speech-to-Text (Whisper)
-                result = whisper_model.transcribe(temp_audio_path, fp16=False)
-                transcribed_text = result['text']
-                audio_analysis_results['Transcription'] = transcribed_text
-
-                # 2. Text-based Emotion
-                if transcribed_text:
-                    text_emotions = text_classifier(transcribed_text)[0]
-                    unified_text_scores = {e: 0.0 for e in UNIFIED_EMOTIONS}
-                    for emo in text_emotions:
-                        unified_emo = TEXT_TO_UNIFIED.get(emo['label'])
-                        if unified_emo:
-                            unified_text_scores[unified_emo] += emo['score']
-                    dominant_text_emotion = max(unified_text_scores, key=unified_text_scores.get)
-                    audio_analysis_results['Text Emotion'] = dominant_text_emotion.capitalize()
-                
-                # 3. Speech Emotion Recognition (SER)
-                audio_array, _ = sf.read(temp_audio_path)
-                inputs = ser_feature_extractor(audio_array, sampling_rate=AUDIO_SAMPLE_RATE, return_tensors="pt", padding=True)
-                with torch.no_grad():
-                    logits = ser_model(**inputs).logits
-                scores = torch.nn.functional.softmax(logits, dim=1).squeeze()
-                unified_ser_scores = {e: 0.0 for e in UNIFIED_EMOTIONS}
-                for i, score in enumerate(scores):
-                    raw_emo = ser_model.config.id2label[i]
-                    unified_emo = SER_TO_UNIFIED.get(raw_emo)
-                    if unified_emo:
-                        unified_ser_scores[unified_emo] += score.item()
-                dominant_ser_emotion = max(unified_ser_scores, key=unified_ser_scores.get)
-                audio_analysis_results['Speech Emotion'] = dominant_ser_emotion.capitalize()
-                
-                # Clean up temp audio file
-                os.unlink(temp_audio_path)
-
-            except Exception as e:
-                st.error(f"An error occurred during audio analysis: {e}")
-            finally:
-                video_clip.close()
-
-
-        # --- Display Results ---
-        st.header("Analysis Results")
-        col1, col2 = st.columns(2)
-
-        with col1:
-            st.subheader("Audio Analysis")
-            if audio_analysis_results:
-                st.write(f"**Transcription:** \"{audio_analysis_results.get('Transcription', 'N/A')}\"")
-                st.metric("Emotion from Text", audio_analysis_results.get('Text Emotion', 'N/A'))
-                st.metric("Emotion from Speech", audio_analysis_results.get('Speech Emotion', 'N/A'))
-            else:
-                st.write("No audio results to display.")
-
-        with col2:
-            st.subheader("Facial Expression Timeline")
-            if facial_analysis_results:
-                for timestamp, emotion in facial_analysis_results:
-                    st.write(f"**Time {int(timestamp // 60):02d}:{int(timestamp % 60):02d}:** {emotion}")
-            else:
-                st.write("No faces detected or video processing failed.")
+st.set_page_config(page_title="MoviePy Test")
+st.title("Testing `moviepy` Installation")
 
-    # Clean up temp video file
-    os.unlink(temp_video_path)
+try:
+    # This line will only succeed if moviepy is installed correctly
+    st.success("Successfully imported `VideoFileClip` from `moviepy.editor`!")
+    st.write("This confirms that the `moviepy` library was installed correctly.")
+except ImportError as e:
+    st.error(f"Failed to import `moviepy`. Error: {e}")

src/streamlit_app_full.py

@@ -0,0 +1,178 @@
+import os
+import streamlit as st
+
+# Set home directories for model caching to the writable /tmp folder
+os.environ['DEEPFACE_HOME'] = '/tmp/.deepface'
+os.environ['HF_HOME'] = '/tmp/huggingface'
+
+import numpy as np
+import torch
+import whisper
+from transformers import pipeline, AutoModelForAudioClassification, AutoFeatureExtractor
+from deepface import DeepFace
+import logging
+import soundfile as sf
+from scipy.io.wavfile import write as write_wav
+import tempfile
+from PIL import Image
+import cv2
+from moviepy.editor import VideoFileClip
+
+# Set home directories for model caching inside the app's writable directory
+os.environ['DEEPFACE_HOME'] = '/tmp/.deepface'
+os.environ['HF_HOME'] = '/tmp/huggingface'
+
+# --- Page Configuration ---
+st.set_page_config(
+    page_title="AffectLink Batch Demo",
+    page_icon="😊",
+    layout="wide"
+)
+
+st.title("AffectLink: Post-Hoc Emotion Analysis")
+st.write("Upload a short video clip to analyze facial expressions, speech-to-text, and the emotional tone of the audio.")
+
+# --- Logger Configuration ---
+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 = ['neutral', 'happy', 'sad', 'angry']
+TEXT_TO_UNIFIED = {
+    'neutral': 'neutral', 'joy': 'happy', 'sadness': 'sad', 'anger': 'angry',
+    'fear': None, 'surprise': None, 'disgust': None
+}
+SER_TO_UNIFIED = {
+    'neu': 'neutral', 'hap': 'happy', 'sad': 'sad', 'ang': 'angry'
+}
+AUDIO_SAMPLE_RATE = 16000
+
+# --- Model Loading ---
+@st.cache_resource
+def load_models():
+    with st.spinner("Loading AI models, this may take a moment..."):
+        whisper_model = whisper.load_model("base", download_root="/tmp/whisper_cache")
+        text_classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=None)
+        ser_model_name = "superb/hubert-large-superb-er"
+        ser_feature_extractor = AutoFeatureExtractor.from_pretrained(ser_model_name)
+        ser_model = AutoModelForAudioClassification.from_pretrained(ser_model_name)
+        return whisper_model, text_classifier, ser_model, ser_feature_extractor
+
+whisper_model, text_classifier, ser_model, ser_feature_extractor = load_models()
+
+
+# --- UI and Processing Logic ---
+uploaded_file = st.file_uploader("Choose a video file...", type=["mp4", "mov", "avi"])
+
+if uploaded_file is not None:
+    # Save the uploaded file to a temporary location
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tfile:
+        tfile.write(uploaded_file.read())
+        temp_video_path = tfile.name
+
+    st.video(temp_video_path)
+
+    if st.button("Analyze Video"):
+        facial_analysis_results = []
+        audio_analysis_results = {}
+
+        # --- Video Processing for Facial Emotion ---
+        with st.spinner("Analyzing video for facial expressions..."):
+            try:
+                cap = cv2.VideoCapture(temp_video_path)
+                fps = cap.get(cv2.CAP_PROP_FPS)
+                frame_count = 0
+                while cap.isOpened():
+                    ret, frame = cap.read()
+                    if not ret:
+                        break
+                    
+                    # Process one frame per second
+                    if frame_count % int(fps) == 0:
+                        timestamp = frame_count / fps
+                        analysis = DeepFace.analyze(frame, actions=['emotion'], enforce_detection=False, silent=True)
+                        if isinstance(analysis, list) and len(analysis) > 0:
+                            dominant_emotion = analysis[0]['dominant_emotion']
+                            facial_analysis_results.append((timestamp, dominant_emotion.capitalize()))
+                    
+                    frame_count += 1
+                cap.release()
+            except Exception as e:
+                st.error(f"An error occurred during facial analysis: {e}")
+
+
+        # --- Audio Extraction and Processing ---
+        with st.spinner("Extracting and analyzing audio..."):
+            try:
+                # Extract audio using moviepy
+                video_clip = VideoFileClip(temp_video_path)
+                with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as taudio:
+                    video_clip.audio.write_audiofile(taudio.name, fps=AUDIO_SAMPLE_RATE, logger=None)
+                    temp_audio_path = taudio.name
+
+                # 1. Speech-to-Text (Whisper)
+                result = whisper_model.transcribe(temp_audio_path, fp16=False)
+                transcribed_text = result['text']
+                audio_analysis_results['Transcription'] = transcribed_text
+
+                # 2. Text-based Emotion
+                if transcribed_text:
+                    text_emotions = text_classifier(transcribed_text)[0]
+                    unified_text_scores = {e: 0.0 for e in UNIFIED_EMOTIONS}
+                    for emo in text_emotions:
+                        unified_emo = TEXT_TO_UNIFIED.get(emo['label'])
+                        if unified_emo:
+                            unified_text_scores[unified_emo] += emo['score']
+                    dominant_text_emotion = max(unified_text_scores, key=unified_text_scores.get)
+                    audio_analysis_results['Text Emotion'] = dominant_text_emotion.capitalize()
+                
+                # 3. Speech Emotion Recognition (SER)
+                audio_array, _ = sf.read(temp_audio_path)
+                inputs = ser_feature_extractor(audio_array, sampling_rate=AUDIO_SAMPLE_RATE, return_tensors="pt", padding=True)
+                with torch.no_grad():
+                    logits = ser_model(**inputs).logits
+                scores = torch.nn.functional.softmax(logits, dim=1).squeeze()
+                unified_ser_scores = {e: 0.0 for e in UNIFIED_EMOTIONS}
+                for i, score in enumerate(scores):
+                    raw_emo = ser_model.config.id2label[i]
+                    unified_emo = SER_TO_UNIFIED.get(raw_emo)
+                    if unified_emo:
+                        unified_ser_scores[unified_emo] += score.item()
+                dominant_ser_emotion = max(unified_ser_scores, key=unified_ser_scores.get)
+                audio_analysis_results['Speech Emotion'] = dominant_ser_emotion.capitalize()
+                
+                # Clean up temp audio file
+                os.unlink(temp_audio_path)
+
+            except Exception as e:
+                st.error(f"An error occurred during audio analysis: {e}")
+            finally:
+                video_clip.close()
+
+
+        # --- Display Results ---
+        st.header("Analysis Results")
+        col1, col2 = st.columns(2)
+
+        with col1:
+            st.subheader("Audio Analysis")
+            if audio_analysis_results:
+                st.write(f"**Transcription:** \"{audio_analysis_results.get('Transcription', 'N/A')}\"")
+                st.metric("Emotion from Text", audio_analysis_results.get('Text Emotion', 'N/A'))
+                st.metric("Emotion from Speech", audio_analysis_results.get('Speech Emotion', 'N/A'))
+            else:
+                st.write("No audio results to display.")
+
+        with col2:
+            st.subheader("Facial Expression Timeline")
+            if facial_analysis_results:
+                for timestamp, emotion in facial_analysis_results:
+                    st.write(f"**Time {int(timestamp // 60):02d}:{int(timestamp % 60):02d}:** {emotion}")
+            else:
+                st.write("No faces detected or video processing failed.")
+
+    # Clean up temp video file
+    os.unlink(temp_video_path)