import gradio as gr
from transformers import ViltProcessor, ViltForVisualQuestionAnswering
import torch

torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')

processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
model = ViltForVisualQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")

def answer_question(image, text):
    encoding = processor(image, text, return_tensors="pt")
    
    # forward pass
    with torch.no_grad():
     outputs = model(**encoding)
     
    logits = outputs.logits
    idx = logits.argmax(-1).item()
    predicted_answer = model.config.id2label[idx]
   
    return predicted_answer
   
image = gr.inputs.Image(type="pil")
question = gr.inputs.Textbox(label="Question")
answer = gr.outputs.Textbox(label="Predicted answer")
examples = [["cats.jpg", "How many cats are there?"], 
            [
                "https://s3.geograph.org.uk/geophotos/06/21/24/6212487_1cca7f3f_1024x1024.jpg",
                "What is the color of the flower?",
            ],
            [
                "https://computing.ece.vt.edu/~harsh/visualAttention/ProjectWebpage/Figures/vqa_1.png",
                "What is the mustache made of?",
            ],
            [
                "https://computing.ece.vt.edu/~harsh/visualAttention/ProjectWebpage/Figures/vqa_2.png",
                "How many slices of pizza are there?",
            ],
            [
                "https://computing.ece.vt.edu/~harsh/visualAttention/ProjectWebpage/Figures/vqa_3.png",
                "Does it appear to be rainy?",
            ],
]

interface = gr.Interface(fn=answer_question, inputs=[image, question], outputs=answer, examples=examples, enable_queue=True)
interface.launch(debug=True)