Delete app.py

app.py

@@ -1,41 +0,0 @@
-import streamlit as st
-from PIL import Image
-import torch
-from transformers import AutoProcessor, AutoModelForCausalLM
-import io
-
-# Load the model and processor from Hugging Face
-model_id = "Qwen/Qwen2-VL-7B-Instruct"
-st.title("Qwen2-VL-7B OCR with Streamlit")
-
-# Load processor and model
-st.write("Loading the model, please wait...")
-processor = AutoProcessor.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id)
-
-st.write("Model loaded successfully!")
-
-# Streamlit UI for image upload
-uploaded_image = st.file_uploader("Upload an image for OCR", type=["jpg", "jpeg", "png"])
-
-if uploaded_image is not None:
-    # Display the uploaded image
-    image = Image.open(uploaded_image)
-    st.image(image, caption="Uploaded Image", use_column_width=True)
-
-    # Process the image using the model
-    st.write("Processing the image...")
-
-    # Convert image to tensor for model input
-    inputs = processor(images=image, return_tensors="pt")
-
-    # Run inference
-    with torch.no_grad():
-        generated_text = model.generate(**inputs, max_new_tokens=200)
-
-    # Decode the generated text
-    result_text = processor.decode(generated_text[0], skip_special_tokens=True)
-
-    # Display the result
-    st.write("Extracted Text:")
-    st.text(result_text)