Update app.py

app.py

@@ -1,95 +1,95 @@
 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
+
+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):
+    """Convert audio answers to text, evaluate and provide a summary."""
+    text_answers = understand_answers(audio_answers)
+    summarized_text = modified_summarize(text_answers)
+    score = ask_questions(text_answers)
+    return summarized_text, f"Your score is: {score}/{len(questions)}", text_answers  # Return text_answers as well
+
+# Create the Gradio Blocks interface with button click
+
+def update():
+    audio_answers = [audio.value for audio in inp]  # Using inp as it collects all the audio inputs
+    # Handling the three returned values from the assistant function
+    summarized_text, score_string, text_answers = assistant(*audio_answers)
+    out_last_transcription.value = summarized_text  # Displaying the summarized text
+    out_score.value = score_string  # Displaying the score
+
+with gr.Blocks() as demo:
+    gr.Markdown("Start recording your responses below and then click **Run** to see the transcription and your score.")
     
-    # Initialize the GPT2 model and tokenizer
-    tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-    model = GPT2LMHeadModel.from_pretrained("gpt2")
+    # Clearly initializing Inputs and Outputs lists for the button click
+    inp = []
     
-    # Initialize the Whisper GPT model
-    translation_pipeline =  pipeline("automatic-speech-recognition", model="openai/whisper-large-v2")
+    # Using Column to nest questions
+    with gr.Column(scale=1, min_width=600):
+        for i, question in enumerate(questions):
+            gr.Markdown(f"**Question {i+1}:** {question}")
+            audio_input = gr.Audio(source="microphone")
+            inp.append(audio_input)
     
-    # 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?"   
-    ]
+    # Two output textboxes: one for the last transcribed answer and another for the score
+    out_last_transcription = gr.Textbox(label="Last Transcribed Answer", placeholder="Last transcribed answer will appear here.")
+    out_score = gr.Textbox(label="Score", placeholder="Your score will appear here.")
     
-    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
-    
-    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):
-        """Convert audio answers to text, evaluate and provide a summary."""
-        text_answers = understand_answers(audio_answers)
-        summarized_text = modified_summarize(text_answers)
-        score = ask_questions(text_answers)
-        return summarized_text, f"Your score is: {score}/{len(questions)}", text_answers  # Return text_answers as well
-    
-    # Create the Gradio Blocks interface with button click
-    
-    def update():
-        audio_answers = [audio.value for audio in inp]  # Using inp as it collects all the audio inputs
-        # Handling the three returned values from the assistant function
-        summarized_text, score_string, text_answers = assistant(*audio_answers)
-        out_last_transcription.value = summarized_text  # Displaying the summarized text
-        out_score.value = score_string  # Displaying the score
-    
-    with gr.Blocks() as demo:
-        gr.Markdown("Start recording your responses below and then click **Run** to see the transcription and your score.")
-        
-        # Clearly initializing Inputs and Outputs lists for the button click
-        inp = []
-        
-        # Using Column to nest questions
-        with gr.Column(scale=1, min_width=600):
-            for i, question in enumerate(questions):
-                gr.Markdown(f"**Question {i+1}:** {question}")
-                audio_input = gr.Audio(source="microphone")
-                inp.append(audio_input)
-        
-        # Two output textboxes: one for the last transcribed answer and another for the score
-        out_last_transcription = gr.Textbox(label="Last Transcribed Answer", placeholder="Last transcribed answer will appear here.")
-        out_score = gr.Textbox(label="Score", placeholder="Your score will appear here.")
-        
-        # Button with click event
-        btn = gr.Button("Run")
-        btn.click(fn=update, inputs=inp, outputs=[out_last_transcription, out_score])
-    
-    
-    demo.launch()
+    # Button with click event
+    btn = gr.Button("Run")
+    btn.click(fn=update, inputs=inp, outputs=[out_last_transcription, out_score])
+
+
+demo.launch()