Update app.py

app.py

@@ -1,335 +1,158 @@
 # app.py - Audio & Text Sentiment Analyzer
-# Transcription: openai/whisper-base.en (official HF version)
-# Sentiment: nlptown/bert-base-multilingual-uncased-sentiment (5-star accurate model)
+# Uses exact model: google-bert/bert-base-uncased (Masked LM)
+# Runs locally with Gradio interface
 
 import gradio as gr
+import whisper
 import torch
 import numpy as np
-import librosa
-from transformers import (
-    AutoProcessor,
-    AutoModelForSpeechSeq2Seq,
-    AutoTokenizer,
-    AutoModelForSequenceClassification
-)
-import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler
 
-print("Loading models... Please wait.")
+print("Loading models... This may take a moment.")
 
-# === Load Whisper exactly as requested ===
-processor = AutoProcessor.from_pretrained("openai/whisper-base.en")
-whisper_model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-base.en")
-whisper_model.eval()
-device = "cuda" if torch.cuda.is_available() else "cpu"
-whisper_model.to(device)
+# Load Whisper for audio transcription
+whisper_model = whisper.load_model("base")  # Fast and works well; use "small" for better accuracy
 
-# === Load Sentiment model exactly as requested ===
-sentiment_tokenizer = AutoTokenizer.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')
-sentiment_model = AutoModelForSequenceClassification.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')
-sentiment_model.eval()
-sentiment_model.to(device)
+# Load exact requested BERT model
+tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-uncased")
+model.eval()
 
-print("All models loaded successfully!")
-
-
-# Transcribe audio using official Whisper
-def transcribe_audio(audio_path):
-    if audio_path is None:
-        return ""
-
-    try:
-        # Load and resample to 16kHz
-        speech, _ = librosa.load(audio_path, sr=16000)
-
-        # Process input
-        input_features = processor(speech, sampling_rate=16000, return_tensors="pt").input_features
-        input_features = input_features.to(device)
-
-        # Generate transcription
-        with torch.no_grad():
-            predicted_ids = whisper_model.generate(input_features)
-
-        transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
-        return transcription.strip()
-
-    except Exception as e:
-        print(f"Transcription error: {e}")
-        return "[Transcription failed]"
-
-
-# Sentiment analysis with 5-star rating and confidence
-def analyze_sentiment(text):
-    if not text.strip():
-        return "⭐⭐⭐ Neutral", "0%"
-
-    inputs = sentiment_tokenizer(text[:512], return_tensors="pt", truncation=True).to(device)
+print("Models loaded successfully!")
 
+# Function to get [CLS] embedding
+def get_cls_embedding(text):
+    inputs = tokenizer(text[:512], return_tensors="pt", truncation=True, padding=True)
     with torch.no_grad():
-        logits = sentiment_model(**inputs).logits
+        outputs = model(**inputs, output_hidden_states=True)
+        cls_embedding = outputs.hidden_states[-1][:, 0, :].cpu().numpy()
+    return cls_embedding.flatten()
 
-    probabilities = F.softmax(logits, dim=-1)[0]
-    predicted_class = torch.argmax(probabilities).item() + 1  # 1 to 5
-    confidence = probabilities[predicted_class - 1].item() * 100
-    conf_str = f"{confidence:.1f}%"
+# Training examples for simple sentiment classifier
+example_texts = [
+    "I love this, it's absolutely amazing", "Best thing ever", "Fantastic experience",
+    "Highly recommend", "Super happy with it", "This is terrible", "Worst product ever",
+    "Very disappointed", "Complete waste", "Poor quality", "It's okay", "Nothing special",
+    "Arrived on time", "Works as expected", "Average"
+]
 
-    stars = "⭐" * predicted_class
-    if predicted_class == 1:
-        level = f"{stars} Very Negative"
-    elif predicted_class == 2:
-        level = f"{stars} Negative"
-    elif predicted_class == 3:
-        level = f"{stars} Neutral"
-    elif predicted_class == 4:
-        level = f"{stars} Positive"
-    else:
-        level = f"{stars} Very Positive"
-
-    return level, conf_str
+example_labels = [1,1,1,1,1, -1,-1,-1,-1,-1, 0,0,0,0,0]  # 1=Positive, -1=Negative, 0=Neutral
 
+# Train classifier
+X_train = np.array([get_cls_embedding(t) for t in example_texts])
+y_train = np.array(example_labels)
 
-# Main unified function
-def analyze_input(audio_path, input_text):
-    # Use typed text if provided
-    if input_text and input_text.strip():
-        final_text = input_text.strip()
-
-    # Otherwise transcribe audio
-    elif audio_path is not None:
-        print("Transcribing audio...")
-        final_text = transcribe_audio(audio_path)
-        if not final_text or "failed" in final_text.lower():
-            return "Transcription failed or no speech detected.", "", "", "Please try again with clearer English audio."
+scaler = StandardScaler()
+X_train_scaled = scaler.fit_transform(X_train)
 
-    else:
-        return "No input provided.", "", "", "Please type text or record/upload audio."
-
-    # Sentiment analysis
-    level, confidence = analyze_sentiment(final_text)
-    final_result = f"{level} (Confidence: {confidence})"
-
-    return final_text, level, confidence, final_result
+clf = LogisticRegression(multi_class='ovr', class_weight='balanced')
+clf.fit(X_train_scaled, y_train)
 
+print("Sentiment classifier trained!")
 
-# Gradio Interface
-with gr.Blocks(title="Audio & Text Sentiment Analyzer", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🎤✍️ Audio to Text + 5-Star Sentiment Analyzer")
-    gr.Markdown("""
-    - **Transcription**: OpenAI Whisper-base.en (excellent English accuracy)  
-    - **Sentiment**: Multilingual BERT fine-tuned on reviews → accurate **1–5 star** ratings  
-    - Record/upload audio **or** type text directly
-    """)
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            audio_input = gr.Audio(
-                sources=["microphone", "upload"],
-                type="filepath",
-                label="Record or Upload Audio (English recommended)"
-            )
-
-            gr.Markdown("**OR**")
-
-            text_input = gr.Textbox(
-                label="Type or Paste Text",
-                placeholder="Enter your review, feedback, or transcribed text...",
-                lines=6
-            )
-
-    btn = gr.Button("Transcribe & Analyze Sentiment", variant="primary", size="lg")
-
-    with gr.Column():
-        gr.Markdown("### 📝 Transcribed / Entered Text")
-        text_display = gr.Textbox(label="Text", lines=8, interactive=False)
-
-        gr.Markdown("### 🌟 Sentiment Result")
-        with gr.Row():
-            level_out = gr.Textbox(label="Sentiment Level", scale=2)
-            conf_out = gr.Textbox(label="Confidence", scale=1)
-
-        result_out = gr.Textbox(label="Final Verdict", lines=2, interactive=False)
-
-    btn.click(
-        fn=analyze_input,
-        inputs=[audio_input, text_input],
-        outputs=[text_display, level_out, conf_out, result_out]
-    )
-
-    gr.Markdown("""
-    ### Notes
-    - Best performance with **clear English speech**
-    - Sentiment model excels at review-style language (opinions, experiences)
-    - Confidence >80% = very reliable prediction
-    - Runs completely locally — perfect for privacy
-    - Built with ❤️ in Accra by Chris (@chrisbekor99)
-    """)
-
-# Launch
-if __name__ == "__main__":
-    demo.launch(
-        server_name="127.0.0.1",
-        server_port=7860,
-        share=False  # Change to True for public link via ngrok
-    )# app.py - Audio & Text Sentiment Analyzer
-# Transcription: openai/whisper-base.en (official HF version)
-# Sentiment: nlptown/bert-base-multilingual-uncased-sentiment (5-star accurate model)
-
-import gradio as gr
-import torch
-import numpy as np
-import librosa
-from transformers import (
-    AutoProcessor,
-    AutoModelForSpeechSeq2Seq,
-    AutoTokenizer,
-    AutoModelForSequenceClassification
-)
-import torch.nn.functional as F
-
-print("Loading models... Please wait.")
-
-# === Load Whisper exactly as requested ===
-processor = AutoProcessor.from_pretrained("openai/whisper-base.en")
-whisper_model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-base.en")
-whisper_model.eval()
-device = "cuda" if torch.cuda.is_available() else "cpu"
-whisper_model.to(device)
-
-# === Load Sentiment model exactly as requested ===
-sentiment_tokenizer = AutoTokenizer.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')
-sentiment_model = AutoModelForSequenceClassification.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')
-sentiment_model.eval()
-sentiment_model.to(device)
-
-print("All models loaded successfully!")
-
-# Transcribe audio using official Whisper
-def transcribe_audio(audio_path):
-    if audio_path is None:
-        return ""
-
-    try:
-        # Load and resample to 16kHz
-        speech, _ = librosa.load(audio_path, sr=16000)
-
-        # Process input
-        input_features = processor(speech, sampling_rate=16000, return_tensors="pt").input_features
-        input_features = input_features.to(device)
-
-        # Generate transcription
-        with torch.no_grad():
-            predicted_ids = whisper_model.generate(input_features)
-
-        transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
-        return transcription.strip()
-
-    except Exception as e:
-        print(f"Transcription error: {e}")
-        return "[Transcription failed]"
-
-# Sentiment analysis with 5-star rating and confidence
-def analyze_sentiment(text):
+# Predict sentiment with stars and confidence
+def predict_sentiment(text):
     if not text.strip():
         return "⭐⭐⭐ Neutral", "0%"
-
-    inputs = sentiment_tokenizer(text[:512], return_tensors="pt", truncation=True).to(device)
-
-    with torch.no_grad():
-        logits = sentiment_model(**inputs).logits
-
-    probabilities = F.softmax(logits, dim=-1)[0]
-    predicted_class = torch.argmax(probabilities).item() + 1  # 1 to 5
-    confidence = probabilities[predicted_class - 1].item() * 100
+    
+    embedding = get_cls_embedding(text)
+    embedding_scaled = scaler.transform([embedding])
+    
+    probabilities = clf.predict_proba(embedding_scaled)[0]
+    pred = clf.predict(embedding_scaled)[0]
+    confidence = np.max(probabilities) * 100
     conf_str = f"{confidence:.1f}%"
-
-    stars = "⭐" * predicted_class
-    if predicted_class == 1:
-        level = f"{stars} Very Negative"
-    elif predicted_class == 2:
-        level = f"{stars} Negative"
-    elif predicted_class == 3:
-        level = f"{stars} Neutral"
-    elif predicted_class == 4:
-        level = f"{stars} Positive"
+    
+    if pred == 1:
+        level = "⭐⭐⭐⭐⭐ Very Positive"
+    elif pred == -1:
+        level = "⭐ Very Negative"
     else:
-        level = f"{stars} Very Positive"
-
+        level = "⭐⭐⭐ Neutral"
+    
     return level, conf_str
 
-# Main unified function
+# Main analysis function
 def analyze_input(audio_path, input_text):
-    # Use typed text if provided
+    # Prefer typed text if provided
     if input_text and input_text.strip():
         final_text = input_text.strip()
-
-    # Otherwise transcribe audio
+    
+    # Otherwise, transcribe audio
     elif audio_path is not None:
-        print("Transcribing audio...")
-        final_text = transcribe_audio(audio_path)
-        if not final_text or "failed" in final_text.lower():
-            return "Transcription failed or no speech detected.", "", "", "Please try again with clearer English audio."
-
+        try:
+            print("Transcribing audio...")
+            result = whisper_model.transcribe(audio_path)
+            final_text = result["text"].strip()
+            if not final_text:
+                return "No speech detected in the audio.", "", "", "Please speak clearly and try again."
+        except Exception as e:
+            return "Error transcribing audio.", "", "", f"Error: {str(e)}"
+    
     else:
         return "No input provided.", "", "", "Please type text or record/upload audio."
-
-    # Sentiment analysis
-    level, confidence = analyze_sentiment(final_text)
+    
+    # Perform sentiment analysis
+    level, confidence = predict_sentiment(final_text)
     final_result = f"{level} (Confidence: {confidence})"
-
+    
     return final_text, level, confidence, final_result
 
 # Gradio Interface
 with gr.Blocks(title="Audio & Text Sentiment Analyzer", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🎤✍️ Audio to Text + 5-Star Sentiment Analyzer")
+    gr.Markdown("# 🎤✍️ Audio to Text + Sentiment Analyzer")
     gr.Markdown("""
-    - **Transcription**: OpenAI Whisper-base.en (excellent English accuracy)  
-    - **Sentiment**: Multilingual BERT fine-tuned on reviews → accurate **1–5 star** ratings  
-    - Record/upload audio **or** type text directly
+    - Record or upload audio → **Automatically transcribed**  
+    - Or type text directly  
+    - Analyzes sentiment using **google-bert/bert-base-uncased** ([CLS] embedding)
     """)
-
+    
     with gr.Row():
         with gr.Column(scale=1):
             audio_input = gr.Audio(
                 sources=["microphone", "upload"],
                 type="filepath",
-                label="Record or Upload Audio (English recommended)"
+                label="Record or Upload Audio"
             )
-
+            
             gr.Markdown("**OR**")
-
+            
             text_input = gr.Textbox(
                 label="Type or Paste Text",
-                placeholder="Enter your review, feedback, or transcribed text...",
+                placeholder="Enter your review or feedback here...",
                 lines=6
             )
-
+    
     btn = gr.Button("Transcribe & Analyze Sentiment", variant="primary", size="lg")
-
+    
     with gr.Column():
         gr.Markdown("### 📝 Transcribed / Entered Text")
         text_display = gr.Textbox(label="Text", lines=8, interactive=False)
-
+        
         gr.Markdown("### 🌟 Sentiment Result")
         with gr.Row():
             level_out = gr.Textbox(label="Sentiment Level", scale=2)
             conf_out = gr.Textbox(label="Confidence", scale=1)
-
+        
         result_out = gr.Textbox(label="Final Verdict", lines=2, interactive=False)
-
+    
     btn.click(
         fn=analyze_input,
         inputs=[audio_input, text_input],
         outputs=[text_display, level_out, conf_out, result_out]
     )
-
+    
     gr.Markdown("""
     ### Notes
-    - Best performance with **clear English speech**
-    - Sentiment model excels at review-style language (opinions, experiences)
-    - Confidence >80% = very reliable prediction
-    - Runs completely locally — perfect for privacy
-    - Built with ❤️ in Accra by Chris (@chrisbekor99)
+    - Works with any language (Whisper handles transcription)
+    - Uses raw BERT base model → educational demo
+    - Run locally, no data leaves your machine
+    - Made with ❤️ in Accra by Chris (@chrisbekor99)
     """)
 
+
 # Run app
 if __name__ == "__main__":
     demo.launch()