Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+from PIL import Image
+from datetime import datetime
+import numpy as np
+import os
+
+# Function to save image array as a file and return the path
+def array_to_image_path(image_array):
+    img = Image.fromarray(np.uint8(image_array))
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = f"image_{timestamp}.png"
+    img.save(filename)
+    return os.path.abspath(filename)
+
+# Load model and processor
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "Qwen/Qwen2-VL-2B-Instruct", 
+    trust_remote_code=True, 
+    torch_dtype=torch.float32,
+    device_map="cpu"
+).eval()
+
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", trust_remote_code=True)
+
+DESCRIPTION = "[Qwen2-VL-2B Demo (CPU Version)](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct)"
+
+def run_example(image, text_input):
+    image_path = array_to_image_path(image)
+    
+    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",
+    )
+    
+    # Inference: Generation of the output
+    with torch.no_grad():
+        generated_ids = model.generate(**inputs, max_new_tokens=128)
+    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
+    )
+    
+    return output_text[0]
+
+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-2B Input (CPU)"):
+        with gr.Row():
+            with gr.Column():
+                input_img = gr.Image(label="Input Picture")
+                text_input = gr.Textbox(label="Question")
+                submit_btn = gr.Button(value="Submit")
+            with gr.Column():
+                output_text = gr.Textbox(label="Output Text")
+
+        submit_btn.click(run_example, [input_img, text_input], [output_text])
+
+demo.queue(api_open=False)
+demo.launch()