app.py

import gradio as gr
from huggingface_hub import InferenceClient
from pydantic import BaseModel, Field
from typing import Optional

# Define the schema
class Medication(BaseModel):
    drug_name: str = Field(description="The name of the drug.")
    is_generic: bool = Field(
        description="Indicates if the drug name is a generic drug name (e.g. 'Tylenol' is not generic, 'paracetamol' or 'acetaminophen' is generic)."
    )
    strength: Optional[str] = Field(default=None, description="The strength of the drug.")
    unit: Optional[str] = Field(default=None, description="The unit of measurement for the drug strength.")
    dosage_form: Optional[str] = Field(default=None, description="The form of the drug (e.g., patch, tablet).")
    frequency: Optional[str] = Field(default=None, description="The frequency of drug administration.")
    route: Optional[str] = Field(default=None, description="The route of administration (e.g., oral, topical).")
    is_prn: Optional[bool] = Field(default=None, description="Whether the medication is taken 'as needed' (pro re nata).")
    total_daily_dose_mg: Optional[float] = Field(default=None, description="The total daily dose in milligrams.")

# Get the schema for structured generation
schema = Medication.schema()

# Connect to your model
client = InferenceClient("cmcmaster/drug_parsing_Llama-3.2-1B-Instruct")

# Response function
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for user_msg, assistant_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages.append({"role": "assistant", "content": assistant_msg})

    messages.append({"role": "user", "content": message})

    # Structured generation with schema
    output = client.chat_completion(
        messages=messages,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        stream=False,
        response_format={"type": "json", "value": schema},
    )

    content = output.choices[0].message.content
    yield content

# Gradio app
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="Extract structured medication details from this input.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
    ],
)

if __name__ == "__main__":
    demo.launch()