Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the physician and patient models via Hugging Face Model Hub
+physician = pipeline("text-generation", model="distilmed")  # Replace with actual medical model
+patient = pipeline("text-generation", model="distilgpt2")  # 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()