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import gradio as gr
from transformers import pipeline
from Gradio_UI import GradioUI
# Load your fine-tuned model from Hugging Face Hub
model = pipeline("text2text-generation", model='unica/CLiMA') # Replace with your actual model repo name
# Define your prompt template (customize as needed)
def format_prompt(user_input):
return f"Identify causal relations in the following clinical narrative:\n\n{user_input}\n\nCausal relations:" # Modify if your model uses a different template
# Define prediction function
def generate_relations(text):
prompt = format_prompt(text)
result = model(prompt, max_length=512, do_sample=False)
return result[0]['generated_text']
# Gradio interface
demo = gr.Interface(
fn=generate_relations,
inputs=gr.Textbox(lines=10, label="Clinical Note or Drug Review Text"),
outputs=gr.Textbox(label="Extracted Causal Relations"),
title="Causal Relation Extractor with MedLlama",
description="Paste your clinical note or drug review. This AI agent extracts drug-condition or symptom causal relations using a fine-tuned LLM.",
examples=[
["Patient reported severe headaches after starting amitriptyline."],
["Lisinopril helped reduce the patient's blood pressure but caused persistent cough."],
["After using Metformin, the patient experienced gastrointestinal discomfort."]
]
)
# Launch the app
demo.launch() |