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import os
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
import gradio as gr
import spaces
device = "cuda" if torch.cuda.is_available() else "cpu"
model_name = "syubraj/MedicalChat-Phi-3.5-mini-instruct"
try:
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name)
print("Model and Tokenizer loaded successfully.")
except Exception as e:
raise ValueError(f"Error loading Model and Tokenizer: {e}")
@torch.inference_mode()
@spaces.GPU(duration=120)
def generate_response(user_query: str, system_message: str = None, max_length: int = 1024) -> str:
"""
Generates a response based on the given user query.
:param user_query: The user's input message.
:param system_message: Custom system instruction (optional, defaults to medical assistant).
:param max_length: Max tokens to generate.
:return: Generated assistant response.
"""
torch.cuda.empty_cache()
if not user_query.strip():
return "Error: User query cannot be empty."
if system_message is None:
system_message = ("You are a trusted AI-powered medical assistant. "
"Analyze patient queries carefully and provide accurate, professional, and empathetic responses. "
"Prioritize patient safety, adhere to medical best practices, and recommend consulting a healthcare provider when necessary.")
messages = [
{"role": "system", "content": system_message},
{'role': "user", "content": user_query}
]
pipe = pipeline("text-generation",
model=model,
tokenizer=tokenizer)
generation_args = {
"max_new_tokens": max_length,
"return_full_text": False,
"temperature": 0.0,
"do_sample": False,
}
try:
output = pipe(messages, **generation_args)
return(output[0]['generated_text'])
except Exception as e:
return f"Error generating response: {e}"
# Gradio Interface
@torch.inference_mode()
@spaces.GPU(duration=120)
def chat_interface(user_query, system_message=None):
response = generate_response(user_query, system_message)
return response
with gr.Blocks() as demo:
gr.Markdown("# Medical Chatbot")
gr.Markdown("Ask your medical questions, and the AI will provide professional responses.")
with gr.Row():
user_query = gr.Textbox(label="Your Query", placeholder="Enter your question here...", lines=3)
system_message = gr.Textbox(label="System Message (Optional)", placeholder="Custom system instruction...", lines=3)
submit_button = gr.Button("Submit")
output = gr.Textbox(label="Assistant Response", lines=5)
submit_button.click(chat_interface, inputs=[user_query, system_message], outputs=output)
demo.launch(share=True)
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