app.py

from transformers import pipeline
import gradio as gr
import os



# Initialize the question-answering pipeline
def initialize_pipeline():
    model_name = "deepset/roberta-base-squad2"  # Example question-answering model
    try:
        qa_pipeline = pipeline("question-answering", model=model_name)
        return qa_pipeline
    except Exception as e:
        print(f"Error initializing the pipeline: {e}")
        return None

qa_pipeline = initialize_pipeline()

# Define your custom context
custom_context = """
George sami is a well known technical lead who can work on frontend, backend, mobile and ai development. 
he started his career on 2019. have a good problem solving skills and technical. he was born in december 1995
"""
def query_with_context(question):
    """Queries the QA model with context and a question."""
    if qa_pipeline is None:
        return "Error: Pipeline not initialized."
    try:
        result = qa_pipeline(context=custom_context, question=question)
        return result['answer']
    except Exception as e:
        return f"Error during inference: {e}"

if __name__ == "__main__":
    if qa_pipeline is not None:
        interface = gr.Interface(
            fn=query_with_context,
            inputs=gr.Textbox(label="Your Question"),
            outputs=gr.Textbox(label="AI Response"),
            title="Custom Context AI",
            description="Ask me anything about George Sami"
        )
        interface.launch()
    else:
        print("Application could not start due to pipeline error.")