initial commit

app.py

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+import io
+
+import requests
+import torch
+from PIL import Image
+from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig
+
+# step 1: Setup constant
+device = "cuda"
+dtype = torch.float16
+
+# step 2: Load Processor and Model
+processor = AutoProcessor.from_pretrained("StanfordAIMI/CheXagent-8b", trust_remote_code=True)
+generation_config = GenerationConfig.from_pretrained("StanfordAIMI/CheXagent-8b")
+model = AutoModelForCausalLM.from_pretrained("StanfordAIMI/CheXagent-8b", torch_dtype=dtype, trust_remote_code=True)
+
+# step 3: Fetch the images
+image_path = "https://upload.wikimedia.org/wikipedia/commons/3/3b/Pleural_effusion-Metastatic_breast_carcinoma_Case_166_%285477628658%29.jpg"
+images = [Image.open(io.BytesIO(requests.get(image_path).content)).convert("RGB")]
+
+# step 4: Generate the Findings section
+prompt = f'Describe "Airway"'
+inputs = processor(images=images, text=f" USER: <s>{prompt} ASSISTANT: <s>", return_tensors="pt").to(device=device, dtype=dtype)
+output = model.generate(**inputs, generation_config=generation_config)[0]
+response = processor.tokenizer.decode(output, skip_special_tokens=True)