fixed clip import and deleted old files

MED_VQA_Huggyface_Gradio.py

@@ -5,7 +5,7 @@ from transformers import ViltProcessor, ViltForQuestionAnswering
 import torch
 import torch.nn as nn
 from transformers import CLIPTokenizer
-from CLIP import clip
+import clip
 from Transformers_for_Caption import Transformer_Caption
 import numpy as np
 import torchvision.transforms as transforms

app.py

@@ -1,46 +0,0 @@
-##### VQA MED Demo
-
-import gradio as gr
-from transformers import ViltProcessor, ViltForQuestionAnswering
-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 = ViltForQuestionAnswering.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.Image(type="pil")
-question = gr.Textbox(label="Question")
-answer = gr.Textbox(label="Predicted answer")
-examples = [["cats.jpg", "How many cats are there?"]]
-
-title = "Interactive Visual Question Answering demo (BigMed@ai: Artificial Intelligence for Large-Scale Medical Image Analysis)"
-description = "<div style='display: flex;align-items: center;justify-content: space-between;'><p style='width:60vw;'>Gradio Demo for VQA medical model trained on PathVQA dataset, To use it, upload your image and type a question and click 'submit', or click one of the examples to load them.</p><a href='https://github.com/dandelin/ViLT' target='_blank' class='link'><img src='file/GitHub.png' style='justify-self:margin-top:0.5em;center; width:calc(200px + 5vw);'></a></div>"
-
-### link to paper and github code
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2102.03334' target='_blank'>BigMed@ai</a> | <a href='https://github.com/dandelin/ViLT' target='_blank'>Github Repo</a></p>"
-
-interface = gr.Interface(fn=answer_question,
-                         inputs=[image, question],
-                         outputs=answer,
-                         examples=examples,
-                         title=title,
-                         description=description,
-                         article=article,
-                         )
-interface.launch(debug=True, enable_queue=True)

flagged/log.csv

@@ -1,2 +0,0 @@
-image,Question,Predicted answer,flag,username,timestamp
-D:\2023\BigMed_Demos\VQA_Demo\flagged\image\tmp6px7agq4.jpg,How many cats are there?,2,,,2022-12-26 01:49:33.791750