Create app.py

app.py

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+# Standard library imports
+import os
+from datetime import datetime
+import subprocess
+import time
+
+# Third-party imports
+import numpy as np
+import torch
+from PIL import Image
+import accelerate
+import gradio as gr
+import spaces
+from transformers import (
+    Qwen2_5_VLForConditionalGeneration,
+    AutoTokenizer,
+    AutoProcessor
+)
+
+# Local imports
+from qwen_vl_utils import process_vision_info
+
+
+def array_to_image_path(image_array):
+    if image_array is None:
+        raise ValueError("No image provided. Please upload an image before submitting.")
+    # Convert numpy array to PIL Image
+    img = Image.fromarray(np.uint8(image_array))
+    
+    # Generate a unique filename using timestamp
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = f"image_{timestamp}.png"
+    
+    # Save the image
+    img.save(filename)
+    
+    # Get the full path of the saved image
+    full_path = os.path.abspath(filename)
+    
+    return full_path
+    
+models = {
+    "Qwen/Qwen2.5-VL-7B-Instruct": Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", 
+                                                                                      trust_remote_code=True, 
+                                                                                      torch_dtype="auto",
+                                                                                      device_map="auto").eval()
+}
+
+processors = {
+    "Qwen/Qwen2.5-VL-7B-Instruct": AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_code=True)
+}
+
+DESCRIPTION = "[Qwen2.5-VL Demo](https://huggingface.co/collections/Qwen/qwen25-vl-6795ffac22b334a837c0f9a5)"
+
+kwargs = {}
+kwargs['torch_dtype'] = torch.bfloat16
+
+user_prompt = '<|user|>\n'
+assistant_prompt = '<|assistant|>\n'
+prompt_suffix = "<|end|>\n"
+
+@spaces.GPU
+def run_example(image, text_input=None, model_id=None):
+    start_time = time.time()
+    image_path = array_to_image_path(image)
+    
+    print(image_path)
+    model = models[model_id]
+    processor = processors[model_id]
+
+    image = Image.fromarray(image).convert("RGB")
+    messages = [
+    {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image",
+                    "image": image_path,
+                },
+                {"type": "text", "text": text_input},
+            ],
+        }
+    ]
+    
+    # Preparation for inference
+    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")
+    
+    # Inference: Generation of the output
+    generated_ids = model.generate(**inputs, max_new_tokens=1024)
+    generated_ids_trimmed = [
+        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+    ]
+    output_text = processor.batch_decode(
+        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    )
+
+    end_time = time.time()
+    total_time = round(end_time - start_time, 2)
+    
+    return output_text[0], total_time
+
+css = """
+  #output {
+    height: 500px; 
+    overflow: auto; 
+    border: 1px solid #ccc; 
+  }
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Tab(label="Qwen2.5-VL Input"):
+        with gr.Row():
+            with gr.Column():
+                input_img = gr.Image(label="Input Picture")
+                model_selector = gr.Dropdown(choices=list(models.keys()), 
+                                             label="Model", 
+                                             value="Qwen/Qwen2.5-VL-7B-Instruct")
+                text_input = gr.Textbox(label="Question")
+                submit_btn = gr.Button(value="Submit")
+            with gr.Column():
+                output_text = gr.Textbox(label="Output Text")
+                time_taken = gr.Textbox(label="Time taken for processing + inference")
+
+        submit_btn.click(run_example, [input_img, text_input, model_selector], [output_text, time_taken])
+
+demo.queue(api_open=False)
+demo.launch(debug=True)