Delete app.py

app.py

@@ -1,155 +0,0 @@
-import gradio as gr
-import spaces
-from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
-from qwen_vl_utils import process_vision_info
-from PIL import Image
-from datetime import datetime
-import os
-
-# subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
-
-DESCRIPTION = "[Sparrow Qwen2-VL-7B Backend](https://github.com/katanaml/sparrow)"
-
-
-def array_to_image_path(image_filepath, max_width=1250, max_height=1750):
-    if image_filepath is None:
-        raise ValueError("No image provided. Please upload an image before submitting.")
-
-    # Open the uploaded image using its filepath
-    img = Image.open(image_filepath)
-
-    # Extract the file extension from the uploaded file
-    input_image_extension = image_filepath.split('.')[-1].lower()  # Extract extension from filepath
-
-    # Set file extension based on the original file, otherwise default to PNG
-    if input_image_extension in ['jpg', 'jpeg', 'png']:
-        file_extension = input_image_extension
-    else:
-        file_extension = 'png'  # Default to PNG if extension is unavailable or invalid
-
-    # Get the current dimensions of the image
-    width, height = img.size
-
-    # Initialize new dimensions to current size
-    new_width, new_height = width, height
-
-    # Check if the image exceeds the maximum dimensions
-    if width > max_width or height > max_height:
-        # Calculate the new size, maintaining the aspect ratio
-        aspect_ratio = width / height
-
-        if width > max_width:
-            new_width = max_width
-            new_height = int(new_width / aspect_ratio)
-
-        if new_height > max_height:
-            new_height = max_height
-            new_width = int(new_height * aspect_ratio)
-
-    # Generate a unique filename using timestamp
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    filename = f"image_{timestamp}.{file_extension}"
-
-    # Save the image
-    img.save(filename)
-
-    # Get the full path of the saved image
-    full_path = os.path.abspath(filename)
-
-    return full_path, new_width, new_height
-
-
-# Initialize the model and processor globally to optimize performance
-model = Qwen2VLForConditionalGeneration.from_pretrained(
-    "Qwen/Qwen2-VL-7B-Instruct",
-    torch_dtype="auto",
-    device_map="auto"
-)
-
-processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
-
-
-@spaces.GPU
-def run_inference(input_imgs, text_input):
-    results = []
-
-    for image in input_imgs:
-        # Convert each image to the required format
-        image_path, width, height = array_to_image_path(image)
-
-        try:
-            # Prepare messages for each image
-            messages = [
-                {
-                    "role": "user",
-                    "content": [
-                        {
-                            "type": "image",
-                            "image": image_path,
-                            "resized_height": height,
-                            "resized_width": width
-                        },
-                        {
-                            "type": "text",
-                            "text": text_input
-                        }
-                    ]
-                }
-            ]
-
-            # Prepare inputs for the model
-            text = processor.apply_chat_template(
-                messages, tokenize=False, add_generation_prompt=True
-            )
-
-            image_inputs, video_inputs = process_vision_info(messages)
-            inputs = processor(
-                text=[text],
-                images=image_inputs,
-                videos=video_inputs,
-                padding=True,
-                return_tensors="pt",
-            )
-            inputs = inputs.to("cuda")
-
-            # Generate inference output
-            generated_ids = model.generate(**inputs, max_new_tokens=4096)
-            generated_ids_trimmed = [
-                out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-            ]
-            raw_output = processor.batch_decode(
-                generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=True
-            )
-
-            results.append(raw_output[0])
-            print("Processed: " + image)
-        finally:
-            # Clean up the temporary image file
-            os.remove(image_path)
-
-    return results
-
-
-css = """
-  #output {
-    height: 500px; 
-    overflow: auto; 
-    border: 1px solid #ccc; 
-  }
-"""
-
-with gr.Blocks(css=css) as demo:
-    gr.Markdown(DESCRIPTION)
-    with gr.Tab(label="Qwen2-VL-7B Input"):
-        with gr.Row():
-            with gr.Column():
-                input_imgs = gr.Files(file_types=["image"], label="Upload Document Images")
-                text_input = gr.Textbox(label="Query")
-                submit_btn = gr.Button(value="Submit", variant="primary")
-            with gr.Column():
-                output_text = gr.Textbox(label="Response")
-
-        submit_btn.click(run_inference, [input_imgs, text_input], [output_text])
-
-demo.queue(api_open=True)
-demo.launch(debug=True)