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app.py

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+import gradio as gr
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
+from PIL import Image
+import torch
+from qwen_vl_utils import process_vision_info
+
+# Load model and processor
+model_id = "DiagramAgent/Diagram_to_Code_Agent"
+model = Qwen2VLForConditionalGeneration.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
+processor = AutoProcessor.from_pretrained(model_id)
+
+def generate_code(image):
+    messages = [{"role": "user", "content": [{"type": "image", "image": image}, {"type": "text", "text": "diagram"}]}]
+    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").to(model.device)
+    generated_ids = model.generate(**inputs, max_new_tokens=8192)
+    output_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
+    return output_text[0]
+
+gr.Interface(fn=generate_code, inputs=gr.Image(type="pil"), outputs="text", title="Diagram to Code Agent").launch()

requirements.txt

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+transformers
+torch
+gradio
+Pillow
+tqdm
+git+https://github.com/QwenLM/Qwen-VL.git