app.py

import gradio as gr
from ctransformers import AutoModelForCausalLM
from transformers import AutoTokenizer, pipeline
import torch
import re

# Initialize the model
model = AutoModelForCausalLM.from_pretrained("bmi-labmedinfo/Igea-1B-QA-v0.1-GGUF", model_file="unsloth.Q4_K_M.gguf", model_type="mistral", hf=True)
tokenizer = AutoTokenizer.from_pretrained( "bmi-labmedinfo/Igea-1B-QA-v0.1")


gen_pipeline = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer
)

# Define the function to generate text
def generate_text(question, opa, opb, opc, opd, context, temperature=1, max_new_tokens=30):

    options_filtered = [option for option in [opa, opb, opc, opd] if option is not None and len(option)>0]
    options_string = "; ".join([["A) ","B) ","C) ","D) "][i]+options_filtered[i] for i in range(len(options_filtered))])+"."
    
    closed_prompt = """
    ### Contesto:
    {}
    
    ### Domanda:
    {}
    
    ### Opzioni:
    {}
    
    ### Risposta:
    """
    
    closed_prompt_no_context = """
    ### Domanda:
    {}
    
    ### Opzioni:
    {}
    
    ### Risposta:
    """
    
    open_prompt = """
    ### Domanda:
    {}
    
    ### Risposta:
    """

    #valid context, valid options
    if context is not None and len(context)>1 and len(options_filtered)>1:
        prompt = closed_prompt.format(context, question, options_string)
    #invalid context, valid options
    elif context is None or len(context)<1 and len(options_filtered)>1:
        prompt = closed_prompt_no_context.format(question, options_string)
    #invalid context, invalid options
    else:
        prompt = open_prompt.format(question)
    print(prompt)
    output = gen_pipeline(
        prompt,
        max_new_tokens=max_new_tokens,
        temperature=temperature,
        return_full_text = False
    )
    generated_text = output[0]['generated_text']
    return f"<span>{question} </span><b style='color: blue;'>{generated_text}</b>"

# Create the Gradio interface
question = gr.Textbox(lines=1, placeholder="L'ostruzione uretrale cronica dovuta a iperplasia prismatica benigna può portare al seguente cambiamento nel parenchima renale", label="Domanda (Opzioni facoltative)")
opa = gr.Textbox(lines=1, placeholder="Iperplasia", label="A:")
opb = gr.Textbox(lines=1, placeholder="Iperofia", label="B:")
opc = gr.Textbox(lines=1, placeholder="Atrofia", label="C:")
opd = gr.Textbox(lines=1, placeholder="Displasia", label="D:")
context = gr.Textbox(lines=2, placeholder="L'ostruzione uretrale cronica dovuta a calcoli urinari, iperofia prostatica, tumori, gravidanza normale, tumori, prolasso uterino o disturbi funzionali causano idronefrosi che per definizione viene utilizzata per descrivere la dilatazione della pelvi renale e dei calcoli associati ad atrofia progressiva del rene dovuta a ostruzione dell'uretra. Deflusso di urina. Fare riferimento a Robbins 7yh/9,1012,9/e. P950.", label="Contesto (facoltativo)")

temperature = gr.Slider(minimum=0.1, maximum=2.0, value=1.0, step=0.1, label="Temperature")

with gr.Blocks(css="#outbox { border-radius: 8px !important; border: 1px solid #e5e7eb !important; padding: 8px !important; text-align:center !important;}") as iface:
    gr.Markdown("# Igea Question Answering Interface ⚕️🩺")
    gr.Markdown("🐢💬 To guarantee a reasonable througput (<1 min to answer with default settings), this space employs a **GGUF quantized version of [Igea 1B](https://huggingface.co/bmi-labmedinfo/Igea-1B-v0.0.1)**, optimized for **hardware-limited, CPU-only machines** like the free-tier HuggingFace space.")
    gr.Markdown("⚠️ Read the **[bias, risks and limitations](https://huggingface.co/bmi-labmedinfo/Igea-1B-v0.0.1#%F0%9F%9A%A8%E2%9A%A0%EF%B8%8F%F0%9F%9A%A8-bias-risks-and-limitations-%F0%9F%9A%A8%E2%9A%A0%EF%B8%8F%F0%9F%9A%A8)** of Igea before use!")
    with gr.Group():
        question.render()
        with gr.Row():
            opa.render()
            opb.render()
            opc.render()
            opd.render()
    context.render()
    with gr.Accordion("Advanced Options", open=False):
        temperature.render()
    output = gr.HTML(label="Answer",elem_id="outbox")

    btn = gr.Button("Answer")
    btn.click(generate_text, [question, opa, opb, opc, opd, context, temperature], output)

# Launch the interface
if __name__ == "__main__":
    iface.launch(inline=True)