# import gradio as gr # from transformers import pipeline # # Load the pre-trained model # generator = pipeline("question-answering", model="EleutherAI/gpt-neo-2.7B") # # Define Gradio interface # def generate_response(prompt): # # Generate response based on the prompt # response = generator(prompt, max_length=50, do_sample=True, temperature=0.9) # return response[0]['generated_text'] # # Create Gradio interface # iface = gr.Interface( # fn=generate_response, # inputs="text", # outputs="text", # title="OpenAI Text Generation Model", # description="Enter a prompt and get a generated text response.", # ) # # Deploy the Gradio interface # iface.launch(share=True) import gradio as gr from transformers import pipeline # Load the question answering pipeline qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad", tokenizer="distilbert-base-cased") # Define a function to generate answer for the given question def generate_answer(question): # Call the question answering pipeline result = qa_pipeline(question=question, context=None) return result["answer"] iface = gr.Interface( fn=generate_answer, inputs="text", outputs="text", title="Open-Domain Question Answering", description="Enter your question to get an answer.", ) iface.launch(share=True) # Deploy the interface