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Upload app.py
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app.py
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import gradio as gr
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from transformers import GPT2LMHeadModel, GPT2Tokenizer, pipeline
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# Initialize the GPT2 model and tokenizer
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
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model = GPT2LMHeadModel.from_pretrained("gpt2")
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translation_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-large-v2")
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# Geriatric Depression Scale Quiz Questions
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questions = [
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"Are you basically satisfied with your life?",
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"Do you feel worthless the way you are now?",
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"Do you feel full of energy?",
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"Do you feel that your situation is hopeless?",
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"Do you think that most people are better off than you are?"
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]
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def ask_questions(answers):
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"""Calculate score based on answers."""
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score = 0
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text_answers.append(transcript[0]['generated_text'])
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return text_answers
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# Removing the understand function as it's functionality is covered by understand_answers
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# Keeping the whisper function for text-to-speech conversion
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def whisper(text):
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"""Convert text to speech using the Whisper TTS model."""
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tts_pipeline = pipeline("text-to-speech", model="facebook/wav2vec2-base-960h")
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speech = tts_pipeline(text)
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return speech[0]['generated_text']
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def modified_summarize(answers):
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"""Summarize answers using the GPT2 model."""
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answers_str = " ".join(answers)
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summary_ids = model.generate(inputs, max_length=150, num_beams=5, early_stopping=True)
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return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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def assistant(*audio_answers):
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"""
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#
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summary = modified_summarize(answers)
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#labeled_inputs = [(f"Question {i+1}: {question}", gr.components.Audio(source="microphone")) for i, question in enumerate(questions)]
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labeled_inputs = [
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{'name': 'input_1', 'label': 'Question 1: Are you basically satisfied with your life?', 'type': 'audio', 'source': 'microphone'},
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{'name': 'input_2', 'label': 'Question 2: Have you dropped many of your activities and interests?', 'type': 'audio', 'source': 'microphone'},
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{'name': 'input_3', 'label': 'Question 3:Do you feel that your life is empty?', 'type': 'audio', 'source': 'microphone'},
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{'name': 'input_4', 'label':'Question 4:Do you often get bored?','type': 'audio', 'source': 'microphone'},
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{'name': 'input_5', 'label':'Question 5:Are you in good spirits most of the time?','type': 'audio', 'source': 'microphone'},
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{'name': 'input_6', 'label':'Question 6:Are you afraid that something bad is going to happen to you?','type': 'audio', 'source': 'microphone'},
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{'name': 'input_7', 'label': 'Question 7:Do you feel happy most of the time?','type': 'audio', 'source': 'microphone'},
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{'name': 'input_8', 'label':'Question 8:Do you often feel helpless?','type': 'audio', 'source': 'microphone'},
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{'name': 'input_9', 'label':'Question 9: Do you prefer to stay at home, rather than going out and doing things?','type': 'audio', 'source': 'microphone'},
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{'name': 'input_10', 'label': 'Question 10: Do you feel that you have more problems with memory than most?','type': 'audio', 'source': 'microphone'},
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{'name': 'input_11', 'label':'Question 11: Do you think it is wonderful to be alive now?','type': 'audio', 'source': 'microphone'},
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{'name': 'input_12', 'label': 'Question 12:Do you feel worthless the way you are now?','type': 'audio', 'source': 'microphone'},
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{'name': 'input_13', 'label': 'Question 13:Do you feel full of energy?','type': 'audio', 'source': 'microphone'},
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{'name': 'input_14', 'label': 'Question 14:Do you feel that your situation is hopeless?','type': 'audio', 'source': 'microphone'},
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{'name': 'input_15', 'label': 'Question 15:Do you think that most people are better off than you are?','type': 'audio', 'source': 'microphone'}
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]
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# Labeled output components
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labeled_outputs = [
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("Score", "text"),
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("Summary", "text"),
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("Summary (Audio)", gr.components.Audio(type="numpy")),
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("First Answer (Text)", "text")
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]
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# Constructing the Gradio Interface with labeled components
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iface_score = gr.Interface(fn=assistant, inputs=labeled_inputs, outputs=labeled_outputs)
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iface_score.launch()
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# Labeled output components
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labeled_outputs = [
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("Score", "text"),
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("Summary", "text"),
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("Summary (Audio)", gr.components.Audio(type="numpy")),
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("First Answer (Text)", "text")
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]
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iface_score = gr.Interface(fn=assistant,
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inputs=labeled_inputs,
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outputs=labeled_outputs)
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iface_score.launch()
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import gradio as gr
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from transformers import GPT2LMHeadModel, GPT2Tokenizer, pipeline
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# Initialize the GPT2 model and tokenizer
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
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model = GPT2LMHeadModel.from_pretrained("gpt2")
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translation_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-large-v2")
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# Geriatric Depression Scale Quiz Questions
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questions = [
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"Are you basically satisfied with your life?",
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"Do you feel worthless the way you are now?",
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"Do you feel full of energy?",
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"Do you feel that your situation is hopeless?",
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"Do you think that most people are better off than you are?"
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]
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def ask_questions(answers):
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"""Calculate score based on answers."""
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score = 0
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text_answers.append(transcript[0]['generated_text'])
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return text_answers
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def modified_summarize(answers):
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"""Summarize answers using the GPT2 model."""
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answers_str = " ".join(answers)
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summary_ids = model.generate(inputs, max_length=150, num_beams=5, early_stopping=True)
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return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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def assistant(*audio_answers):
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"""Convert audio answers to text, evaluate and provide a summary."""
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text_answers = understand_answers(audio_answers)
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summarized_text = modified_summarize(text_answers)
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score = ask_questions(text_answers)
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return summarized_text, f"Your score is: {score}/{len(questions)}", text_answers # Return text_answers as well
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# Create the Gradio Blocks interface with button click
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def update():
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audio_answers = [audio.value for audio in inp] # Using inp as it collects all the audio inputs
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# Handling the three returned values from the assistant function
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summarized_text, score_string, text_answers = assistant(*audio_answers)
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out_last_transcription.value = summarized_text # Displaying the summarized text
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out_score.value = score_string # Displaying the score
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with gr.Blocks() as demo:
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gr.Markdown("Start recording your responses below and then click **Run** to see the transcription and your score.")
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# Clearly initializing Inputs and Outputs lists for the button click
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inp = []
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# Using Column to nest questions
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with gr.Column(scale=1, min_width=600):
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for i, question in enumerate(questions):
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gr.Markdown(f"**Question {i+1}:** {question}")
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audio_input = gr.Audio(source="microphone")
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inp.append(audio_input)
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# Two output textboxes: one for the last transcribed answer and another for the score
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out_last_transcription = gr.Textbox(label="Last Transcribed Answer", placeholder="Last transcribed answer will appear here.")
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out_score = gr.Textbox(label="Score", placeholder="Your score will appear here.")
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# Button with click event
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btn = gr.Button("Run")
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btn.click(fn=update, inputs=inp, outputs=[out_last_transcription, out_score])
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demo.launch()
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