| import gradio as gr |
| from transformers import CLIPProcessor, CLIPModel |
| from PIL import Image |
| import torch |
|
|
| MODEL_ID = "KhanyiTapiwa00/medsiglip-diagnosis" |
|
|
| processor = CLIPProcessor.from_pretrained(MODEL_ID) |
| model = CLIPModel.from_pretrained(MODEL_ID) |
| model.eval() |
|
|
| def predict(image: Image.Image, text: str): |
| if image is None or text.strip() == "": |
| return "Please provide both an image and a text description." |
| |
| inputs = processor(images=image, text=text, return_tensors="pt", padding=True) |
| with torch.no_grad(): |
| outputs = model(**inputs) |
| |
| logits = outputs.logits_per_image |
| probs = torch.softmax(logits, dim=1) |
| return str(probs.cpu().numpy()) |
|
|
| demo = gr.Interface( |
| fn=predict, |
| inputs=[gr.Image(type="pil", label="Upload Medical Image"), |
| gr.Textbox(label="Enter Description")], |
| outputs=gr.Textbox(label="Similarity Score"), |
| title="MedSigLIP AI Demo", |
| description="Upload a medical image and compare it with a text description." |
| ) |
|
|
| demo.launch() |
|
|
