# import gradio as gr
# from transformers.utils import logging
# logging.set_verbosity_error()

# import warnings
# warnings.filterwarnings("ignore", message="Using the model-agnostic default `max_length`")

# from transformers import BlipForQuestionAnswering
# from transformers import AutoProcessor

# def qa(image, question):
#     model = BlipForQuestionAnswering.from_pretrained(
#     "./models/Salesforce/blip-vqa-base")
#     processor = AutoProcessor.from_pretrained(
#     "./models/Salesforce/blip-vqa-base")
    
#     inputs = processor(image, question, return_tensors="pt")

#     out = model.generate(image, question)

#     result  = processor.decode(out[0], skip_special_tokens=True)
#     return result

# # def greet(name):
# #     return "Hello " + name + "!!"

# iface = gr.Interface(fn=qa, inputs=["image","text"], outputs="textbox")
# iface.launch()



import gradio as gr
from transformers.utils import logging
from transformers import BlipForQuestionAnswering, AutoProcessor

logging.set_verbosity_error()
import warnings
warnings.filterwarnings("ignore", message="Using the model-agnostic default `max_length`")

def qa(image, question):
    model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base")
    processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base")
    
    inputs = processor(image=image, question=question, return_tensors="pt")
    out = model.generate(**inputs)
    result = processor.decode(out[0], skip_special_tokens=True)
    
    return result

iface = gr.Interface(fn=qa, inputs=["image", "text"], outputs="textbox")
iface.launch()