Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from PIL import Image
+import requests
+from io import BytesIO
+
+# Load the Qwen-VL model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-VL", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="cuda", trust_remote_code=True).eval()
+
+def generate_predictions(image_input, text_input):
+    # Save the image locally to match the original example
+    user_image_path = "/tmp/user_input_test_image.jpg"
+    image_input.save(user_image_path)
+    image_input = Image.open(user_image_path)
+
+    # Prepare the inputs
+    query = tokenizer.from_list_format([
+        {'image': user_image_path},
+        {'text': text_input},
+    ])
+    inputs = tokenizer(query, return_tensors='pt')
+    inputs = inputs.to(model.device)
+    
+    # Generate the caption
+    pred = model.generate(**inputs)
+    response = tokenizer.decode(pred.cpu()[0], skip_special_tokens=False)
+    
+    # Draw bounding boxes if any
+    image_with_boxes = tokenizer.draw_bbox_on_latest_picture(response)
+    
+    return image_with_boxes, response
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=generate_predictions, 
+    inputs=["image", "text"], 
+    outputs=["image", "text"]
+)
+
+iface.launch()