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import gradio as gr
from transformers import pipeline
from datetime import datetime, timezone
# Initialize the model
generator = pipeline('text-generation', model='facebook/opt-350m') # We'll update this with your medical model later
def get_timestamp():
"""Get current UTC datetime in specified format"""
return datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S')
def format_system_info():
"""Format system information header"""
return (
f"Current Date and Time (UTC - YYYY-MM-DD HH:MM:SS formatted): {get_timestamp()}\n"
f"Current User's Login: Raj-VedAI\n"
)
def chat(message, history):
if history is None:
history = []
# Add system information
system_info = format_system_info()
# Format the prompt with system info
prompt = f"{system_info}\nPatient Query: {message}\nMedical AI Assistant:"
try:
# Generate response
response = generator(
prompt,
max_length=512,
temperature=0.7,
top_p=0.95,
do_sample=True
)[0]['generated_text']
# Extract and format the response
ai_response = response[len(prompt):].strip()
formatted_response = f"{system_info}\n{ai_response}"
history.append((message, formatted_response))
return history
except Exception as e:
return [(message, f"Error: {str(e)}")]
# Create custom theme
theme = gr.themes.Default().set(
body_background_fill="#f0f8ff", # Light blue background
block_background_fill="#ffffff",
block_border_width="1px",
block_border_color="#2c3e50",
block_radius="10px"
)
# Create the Gradio interface
demo = gr.ChatInterface(
fn=chat,
title="Medical Decision Support AI",
description="""A medical decision support system that provides healthcare-related information and guidance.
Current UTC Time: """ + get_timestamp(),
theme=theme,
examples=[
"What are the symptoms of hypertension?",
"What are common drug interactions with aspirin?",
"What are the warning signs of diabetes?",
],
retry_on_error=True
)
demo.launch() |