Upload app.py

app.py

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+import gradio as gr
+
+
+
+
+from transformers import GPT2LMHeadModel, GPT2Tokenizer, pipeline
+
+# Initialize the GPT2 model and tokenizer
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+model = GPT2LMHeadModel.from_pretrained("gpt2")
+
+# Initialize the Whisper GPT model
+translation_pipeline =  pipeline("automatic-speech-recognition", model="openai/whisper-large-v2")
+
+
+# Geriatric Depression Scale Quiz Questions
+questions = [
+"Are you basically satisfied with your life?",
+"Have you dropped many of your activities and interests?",
+"Do you feel that your life is empty?",
+"Do you often get bored?",
+"Are you in good spirits most of the time?",
+"Are you afraid that something bad is going to happen to you?",
+"Do you feel happy most of the time?",
+"Do you often feel helpless?",
+"Do you prefer to stay at home, rather than going out and doing things?",
+"Do you feel that you have more problems with memory than most?",
+"Do you think it is wonderful to be alive now?",
+"Do you feel worthless the way you are now?",
+"Do you feel full of energy?",
+"Do you feel that your situation is hopeless?",
+"Do you think that most people are better off than you are?"
+]
+
+
+def ask_questions(answers):
+    """Calculate score based on answers."""
+    score = 0
+    for answer in answers:
+        if answer.lower() == 'yes':
+            score += 1
+        elif answer.lower() != 'no':
+            raise ValueError(f"Invalid answer: {answer}")
+    return score
+
+def understand_answers(audio_answers):
+    """Convert audio answers to text using the Whisper ASR model."""
+    asr_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-large-v2")
+    text_answers = []
+    for audio in audio_answers:
+        transcript = asr_pipeline(audio)
+        text_answers.append(transcript[0]['generated_text'])
+    return text_answers
+
+# Removing the understand function as it's functionality is covered by understand_answers
+
+# Keeping the whisper function for text-to-speech conversion
+def whisper(text):
+    """Convert text to speech using the Whisper TTS model."""
+    tts_pipeline = pipeline("text-to-speech", model="facebook/wav2vec2-base-960h")
+    speech = tts_pipeline(text)
+    return speech[0]['generated_text']
+    
+
+def modified_summarize(answers):
+    """Summarize answers using the GPT2 model."""
+    answers_str = " ".join(answers)
+    inputs = tokenizer.encode("summarize: " + answers_str, return_tensors='pt')
+    summary_ids = model.generate(inputs, max_length=150, num_beams=5, early_stopping=True)
+    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+def assistant(*audio_answers):
+    """Calculate score, translate and summarize answers."""
+    # Convert audio answers to text
+    answers = understand_answers(audio_answers)
+    
+    # Calculate score and summarize
+    score = ask_questions(answers)
+    summary = modified_summarize(answers)
+    
+    # Convert the summary to speech
+    speech = whisper(summary)
+    
+    # Convert the first answer from audio to text (already done in answers[0])
+    text = answers[0]
+    
+    return {"score": f"Score: {score}", "summary": f"Summary: {summary}", "speech": speech, "text": text}
+
+iface_score = gr.Interface(fn=assistant, 
+                           inputs=[gr.inputs.Audio(source="microphone")] * len(questions), 
+                           outputs=["text", "text", gr.outputs.Audio(type="auto"), "text"])
+iface_score.launch()
+