Update app.py

app.py

@@ -31,23 +31,19 @@ st.set_page_config(layout="wide", page_title="Voice Based Sentiment Analysis")
 st.title("🎙 Voice Based Sentiment Analysis")
 st.write("Detect emotions, sentiment, and sarcasm from your voice with state-of-the-art accuracy using OpenAI Whisper.")
 
-
-# Emotion Detection Function
 # Emotion Detection Function
 @st.cache_resource
 def get_emotion_classifier():
     try:
-        tokenizer = AutoTokenizer.from_pretrained("SamLowe/roberta-base-go_emotions", use_fast=True)
-        model = AutoModelForSequenceClassification.from_pretrained("SamLowe/roberta-base-go_emotions")
+        tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion", use_fast=True)
+        model = AutoModelForSequenceClassification.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion")
         model = model.to(device)
 
-        # Changed from device=-1 if device.type == "cpu" else 0
-        # to ensure proper device selection
         classifier = pipeline("text-classification",
-                              model=model,
-                              tokenizer=tokenizer,
-                              top_k=None,
-                              device=0 if torch.cuda.is_available() else -1)
+                             model=model,
+                             tokenizer=tokenizer,
+                             top_k=None,
+                             device=0 if torch.cuda.is_available() else -1)
 
         # Add a verification test to make sure the model is working
         test_result = classifier("I am happy today")
@@ -57,65 +53,54 @@ def get_emotion_classifier():
     except Exception as e:
         print(f"Error loading emotion model: {str(e)}")
         st.error(f"Failed to load emotion model. Please check logs.")
-        # Return a basic fallback that won't crash
         return None
 
-
 def perform_emotion_detection(text):
     try:
         if not text or len(text.strip()) < 3:
             return {}, "neutral", {}, "NEUTRAL"
 
         emotion_classifier = get_emotion_classifier()
-        emotion_results = emotion_classifier(text)[0]
+        if emotion_classifier is None:
+            st.error("Emotion classifier not available.")
+            return {}, "neutral", {}, "NEUTRAL"
+
+        emotion_results = emotion_classifier(text)
+        print(f"Raw emotion classifier output: {emotion_results}")
+        if not emotion_results or not isinstance(emotion_results, list):
+            st.error("Emotion classifier returned invalid results.")
+            return {}, "neutral", {}, "NEUTRAL"
 
         emotion_map = {
-            "admiration": "🤩", "amusement": "😄", "anger": "😡", "annoyance": "😒",
-            "approval": "👍", "caring": "🤗", "confusion": "😕", "curiosity": "🧐",
-            "desire": "😍", "disappointment": "😞", "disapproval": "👎", "disgust": "🤢",
-            "embarrassment": "😳", "excitement": "🤩", "fear": "😨", "gratitude": "🙏",
-            "grief": "😢", "joy": "😊", "love": "❤", "nervousness": "😰",
-            "optimism": "🌈", "pride": "😌", "realization": "💡", "relief": "😌",
-            "remorse": "😔", "sadness": "😭", "surprise": "😲", "neutral": "😐"
+            "joy": "😊", "anger": "😡", "disgust": "🤢", "fear": "😨",
+            "sadness": "😭", "surprise": "😲"
         }
+        positive_emotions = ["joy"]
+        negative_emotions = ["anger", "disgust", "fear", "sadness"]
+        neutral_emotions = ["surprise"]
 
-        positive_emotions = ["admiration", "amusement", "approval", "caring", "desire",
-                             "excitement", "gratitude", "joy", "love", "optimism", "pride", "relief"]
-        negative_emotions = ["anger", "annoyance", "disappointment", "disapproval", "disgust",
-                             "embarrassment", "fear", "grief", "nervousness", "remorse", "sadness"]
-        neutral_emotions = ["confusion", "curiosity", "realization", "surprise", "neutral"]
-
-        # Fix 1: Create a clean emotions dictionary from results
         emotions_dict = {}
         for result in emotion_results:
             emotions_dict[result['label']] = result['score']
 
-        # Fix 2: Filter out very low scores (below threshold)
-        filtered_emotions = {k: v for k, v in emotions_dict.items() if v > 0.02}
+        filtered_emotions = {k: v for k, v in emotions_dict.items() if v > 0.01}
 
-        # If filtered dictionary is empty, fall back to original
         if not filtered_emotions:
             filtered_emotions = emotions_dict
 
-        # Fix 3: Make sure we properly find the top emotion
         top_emotion = max(filtered_emotions, key=filtered_emotions.get)
         top_score = filtered_emotions[top_emotion]
 
-        # Fix 4: More robust sentiment assignment
         if top_emotion in positive_emotions:
             sentiment = "POSITIVE"
         elif top_emotion in negative_emotions:
             sentiment = "NEGATIVE"
         else:
-            # If the top emotion is neutral but there are strong competing emotions, use them
             competing_emotions = sorted(filtered_emotions.items(), key=lambda x: x[1], reverse=True)[:3]
-
-            # Check if there's a close second non-neutral emotion
             if len(competing_emotions) > 1:
                 if (competing_emotions[0][0] in neutral_emotions and
                         competing_emotions[1][0] not in neutral_emotions and
-                        competing_emotions[1][1] > 0.5 * competing_emotions[0][1]):
-                    # Use the second strongest emotion instead
+                        competing_emotions[1][1] > 0.7 * competing_emotions[0][1]):
                     top_emotion = competing_emotions[1][0]
                     if top_emotion in positive_emotions:
                         sentiment = "POSITIVE"
@@ -128,15 +113,13 @@ def perform_emotion_detection(text):
             else:
                 sentiment = "NEUTRAL"
 
-        # Log for debugging
         print(f"Text: {text[:50]}...")
         print(f"Top 3 emotions: {sorted(filtered_emotions.items(), key=lambda x: x[1], reverse=True)[:3]}")
         print(f"Selected top emotion: {top_emotion} ({filtered_emotions.get(top_emotion, 0):.3f})")
         print(f"Sentiment determined: {sentiment}")
-
-        print(f"All emotions detected: {filtered_emotions}")
+        print(f"All emotions detected: {emotions_dict}")
         print(f"Filtered emotions: {filtered_emotions}")
-        print(f"Emotion classification threshold: 0.02")
+        print(f"Emotion classification threshold: 0.01")
 
         return emotions_dict, top_emotion, emotion_map, sentiment
     except Exception as e:
@@ -144,7 +127,6 @@ def perform_emotion_detection(text):
         print(f"Exception in emotion detection: {str(e)}")
         return {}, "neutral", {}, "NEUTRAL"
 
-
 # Sarcasm Detection Function
 @st.cache_resource
 def get_sarcasm_classifier():
@@ -153,7 +135,7 @@ def get_sarcasm_classifier():
         model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-irony")
         model = model.to(device)
         classifier = pipeline("text-classification", model=model, tokenizer=tokenizer,
-                              device=0 if torch.cuda.is_available() else -1)
+                             device=0 if torch.cuda.is_available() else -1)
 
         # Add a verification test to ensure the model is working
         test_result = classifier("This is totally amazing")
@@ -165,7 +147,6 @@ def get_sarcasm_classifier():
         st.error(f"Failed to load sarcasm model. Please check logs.")
         return None
 
-
 def perform_sarcasm_detection(text):
     try:
         if not text or len(text.strip()) < 3:
@@ -184,7 +165,6 @@ def perform_sarcasm_detection(text):
         st.error(f"Sarcasm detection failed: {str(e)}")
         return False, 0.0
 
-
 # Validate audio quality
 def validate_audio(audio_path):
     try:
@@ -200,9 +180,7 @@ def validate_audio(audio_path):
         st.error("Invalid or corrupted audio file.")
         return False
 
-
 # Speech Recognition with Whisper
-# @st.cache_resource
 @st.cache_resource
 def load_whisper_model():
     try:
@@ -245,7 +223,6 @@ def transcribe_audio(audio_path, show_alternative=False):
         st.error(f"Transcription failed: {str(e)}")
         return "", [] if show_alternative else ""
 
-
 # Function to handle uploaded audio files
 def process_uploaded_audio(audio_file):
     if not audio_file:
@@ -271,7 +248,6 @@ def process_uploaded_audio(audio_file):
         st.error(f"Error processing uploaded audio: {str(e)}")
         return None
 
-
 # Show model information
 def show_model_info():
     st.sidebar.header("🧠 About the Models")
@@ -280,11 +256,11 @@ def show_model_info():
 
     with model_tabs[0]:
         st.markdown("""
-        *Emotion Model*: SamLowe/roberta-base-go_emotions
-        - Fine-tuned on GoEmotions dataset (58k Reddit comments, 27 emotions)
-        - Architecture: RoBERTa base
-        - Micro-F1: 0.46
-        [🔍 Model Hub](https://huggingface.co/SamLowe/roberta-base-go_emotions)
+        *Emotion Model*: distilbert-base-uncased-emotion
+        - Fine-tuned for six emotions (joy, anger, disgust, fear, sadness, surprise)
+        - Architecture: DistilBERT base
+        - High accuracy for basic emotion classification
+        [🔍 Model Hub](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion)
         """)
 
     with model_tabs[1]:
@@ -307,7 +283,6 @@ def show_model_info():
         [🔍 Model Details](https://github.com/openai/whisper)
         """)
 
-
 # Custom audio recorder using HTML/JS
 def custom_audio_recorder():
     st.warning("Browser-based recording requires microphone access and a modern browser. If recording fails, try uploading an audio file instead.")
@@ -440,10 +415,8 @@ def custom_audio_recorder():
 
     return components.html(audio_recorder_html, height=150)
 
-
 # Function to display analysis results
 def display_analysis_results(transcribed_text):
-    # Fix 5: Add debugging to track what's happening
     st.session_state.debug_info = st.session_state.get('debug_info', [])
     st.session_state.debug_info.append(f"Processing text: {transcribed_text[:50]}...")
     st.session_state.debug_info = st.session_state.debug_info[-100:]  # Keep last 100 entries
@@ -493,7 +466,6 @@ def display_analysis_results(transcribed_text):
         else:
             st.write("No emotions detected.")
 
-    # Fix 6: Add debug expander for troubleshooting
     with st.expander("Debug Information", expanded=False):
         st.write("Debugging information for troubleshooting:")
         for i, debug_line in enumerate(st.session_state.debug_info[-10:]):
@@ -508,7 +480,7 @@ def display_analysis_results(transcribed_text):
         st.write("""
         *How this works:*
         1. *Speech Recognition*: Audio transcribed using OpenAI Whisper (large-v3)
-        2. *Emotion Analysis*: RoBERTa model trained on GoEmotions (27 emotions)
+        2. *Emotion Analysis*: DistilBERT model trained for six emotions
         3. *Sentiment Analysis*: Derived from dominant emotion
         4. *Sarcasm Detection*: RoBERTa model for irony detection
         *Accuracy depends on*:
@@ -518,7 +490,6 @@ def display_analysis_results(transcribed_text):
         - Speech patterns
         """)
 
-
 # Process base64 audio data
 def process_base64_audio(base64_data):
     try:
@@ -539,10 +510,8 @@ def process_base64_audio(base64_data):
         st.error(f"Error processing audio data: {str(e)}")
         return None
 
-
 # Main App Logic
 def main():
-    # Fix 7: Initialize session state for debugging
     if 'debug_info' not in st.session_state:
         st.session_state.debug_info = []
 
@@ -616,6 +585,5 @@ def main():
 
     show_model_info()
 
-
 if __name__ == "__main__":
     main()