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import gradio as gr |
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from transformers import AutoProcessor, AutoModelForVision2Seq |
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from PIL import Image |
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import torch |
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MODEL_ID = "Qwen/Qwen2-VL-7B-Instruct" |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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print(f"Loading model: {MODEL_ID}") |
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processor = AutoProcessor.from_pretrained(MODEL_ID) |
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model = AutoModelForVision2Seq.from_pretrained( |
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MODEL_ID, |
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, |
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device_map="auto" |
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) |
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def describe_ingredients(image): |
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"""Ask the model to identify ingredients in an image.""" |
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prompt = "Describe the food in this image and list its likely ingredients." |
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inputs = processor(images=image, text=prompt, return_tensors="pt").to(device) |
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output = model.generate(**inputs, max_new_tokens=128) |
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result = processor.batch_decode(output, skip_special_tokens=True)[0] |
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return result.strip() |
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demo = gr.Interface( |
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fn=describe_ingredients, |
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inputs=gr.Image(type="pil"), |
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outputs="text", |
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title="🍱 Food Ingredient Analyzer (Qwen2-VL)", |
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description="Upload a food image — the model will describe it and list possible ingredients." |
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) |
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if __name__ == "__main__": |
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demo.launch() |
