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Update app.py
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app.py
CHANGED
@@ -23,19 +23,17 @@ dtype = torch.float16
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processor = AutoProcessor.from_pretrained("StanfordAIMI/CheXagent-8b", trust_remote_code=True)
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generation_config = GenerationConfig.from_pretrained("StanfordAIMI/CheXagent-8b")
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model = AutoModelForCausalLM.from_pretrained("StanfordAIMI/CheXagent-8b", torch_dtype=dtype, trust_remote_code=True)
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@spaces.GPU
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def generate(image, prompt):
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if hasattr(image, "read"):
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image = Image.open(io.BytesIO(image.read())).convert("RGB")
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else:
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image = image
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-
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images = [image]
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inputs = processor(images=images, text=f" USER: <s>{prompt} ASSISTANT: <s>", return_tensors="pt").to(device=device, dtype=dtype)
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output = model.generate(**inputs, generation_config=generation_config)[0]
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response = processor.tokenizer.decode(output, skip_special_tokens=True)
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return response
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processor = AutoProcessor.from_pretrained("StanfordAIMI/CheXagent-8b", trust_remote_code=True)
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generation_config = GenerationConfig.from_pretrained("StanfordAIMI/CheXagent-8b")
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# model = AutoModelForCausalLM.from_pretrained("StanfordAIMI/CheXagent-8b", torch_dtype=dtype, trust_remote_code=True)
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@spaces.GPU
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def generate(image, prompt):
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model = AutoModelForCausalLM.from_pretrained("StanfordAIMI/CheXagent-8b", torch_dtype=dtype, trust_remote_code=True).to(device)
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if hasattr(image, "read"):
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image = Image.open(io.BytesIO(image.read())).convert("RGB")
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else:
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image = image
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images = [image]
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inputs = processor(images=images, text=f" USER: <s>{prompt} ASSISTANT: <s>", return_tensors="pt").to(device=device, dtype=dtype)
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output = model.generate(**inputs, generation_config=generation_config)[0]
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response = processor.tokenizer.decode(output, skip_special_tokens=True)
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return response
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