Delete app.py

app.py

@@ -1,39 +0,0 @@
-from transformers import ViltProcessor, ViltForQuestionAnswering
-import torch
-import gradio as gr
-
-# Load the model and processor
-processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
-model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
-
-def answer_question(image, text):
-    # Convert the uploaded image to PIL format
-    image = Image.fromarray(image.astype('uint8'), 'RGB')
-
-    # Process the image and text
-    encoding = processor(images=image, text=text, return_tensors="pt", padding=True)
-
-    # Forward pass
-    with torch.no_grad():
-        outputs = model(**encoding)
-
-    logits = outputs.logits
-    idx = logits.argmax(-1).item()
-    predicted_answer = model.config.id2label[idx]
-
-    # Return the predicted answer
-    return predicted_answer
-
-# Define Gradio inputs and outputs
-image = gr.Image(type="numpy", label="Upload Image")
-question = gr.Textbox(lines=2, label="Question")
-answer = gr.Textbox(label="Predicted Answer")
-
-# Create Gradio Interface
-gr.Interface(
-    fn=answer_question, 
-    inputs=[image, question], 
-    outputs=answer,
-    title="Image Based Visual Question Answering",
-    description="This is a demonstration of ViLT (Vision and Language Transformer) using Gradio, which has been fine-tuned on VQAv2 to answer questions based on images. To get a predicted answer, please provide an image and type in your question, then press the submit button."
-).launch(share=True)