Spaces:
Runtime error
Runtime error
File size: 3,016 Bytes
56a8be9 3ad9db3 56a8be9 11038f2 7f9e70c 56a8be9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
import gradio as gr
from transformers import pipeline
import torch
# Load the physician and patient models via Hugging Face Model Hub
physician = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B") # Replace with actual medical model
patient = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B") # General conversational model
def generate_conversation(topic, turns):
conversation = []
total_tokens = 0
physician_tokens = 0
patient_tokens = 0
# Initial prompt for the patient
patient_prompt = f"I'm here to talk about {topic}."
patient_response = patient(patient_prompt, max_length=50, num_return_sequences=1)[0]['generated_text']
patient_tokens += len(patient_response.split())
conversation.append({"role": "patient", "message": patient_response, "tokens": len(patient_response.split())})
for turn in range(turns):
# Physician's turn
physician_prompt = f"As a physician, how would you respond to: {patient_response}"
physician_response = physician(physician_prompt, max_length=50, num_return_sequences=1)[0]['generated_text']
physician_tokens += len(physician_response.split())
conversation.append({"role": "physician", "message": physician_response, "tokens": len(physician_response.split())})
# Patient's turn
patient_prompt = f"As a patient, how would you respond to: {physician_response}"
patient_response = patient(patient_prompt, max_length=50, num_return_sequences=1)[0]['generated_text']
patient_tokens += len(patient_response.split())
conversation.append({"role": "patient", "message": patient_response, "tokens": len(patient_response.split())})
# Summarize the conversation
summary = {
"total_tokens": physician_tokens + patient_tokens,
"physician_tokens": physician_tokens,
"patient_tokens": patient_tokens
}
return conversation, summary
def app_interface(topic, turns):
conversation, summary = generate_conversation(topic, turns)
output = {
"input": {"topic": topic, "turns": turns},
"conversation": conversation,
"summary": summary
}
return output
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown("## 👨⚕️ Synthetic Data Generation: Physician-Patient Role-Play 👤")
with gr.Row():
topic_input = gr.Textbox(label="Enter Disease/Topic", placeholder="e.g., chest pain")
turns_input = gr.Number(label="Number of Turns", value=5)
submit_button = gr.Button("🚀 Start Interaction")
output_json = gr.JSON(label="Generated Conversation")
# Download button for the conversation
download_button = gr.Button("📥 Download Conversation")
download_button.click(
fn=lambda data: gr.File.download(data),
inputs=output_json,
outputs=gr.File()
)
submit_button.click(
fn=app_interface,
inputs=[topic_input, turns_input],
outputs=output_json
)
demo.launch()
|