Upload 4 files

.huggingface.yaml

@@ -0,0 +1 @@
+sdk: docker

Dockerfile

@@ -0,0 +1,10 @@
+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -0,0 +1,120 @@
+from fastapi import FastAPI, Query
+from pydantic import BaseModel
+from typing import List, Tuple
+from fastapi import Body
+
+import torch
+import torchxrayvision as xrv
+import torchvision
+import skimage.io
+import numpy as np
+import requests
+import cv2
+
+from io import BytesIO
+import matplotlib.pyplot as plt
+from pytorch_grad_cam import GradCAM
+from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
+from pytorch_grad_cam.utils.image import show_cam_on_image
+
+app = FastAPI()
+
+model = xrv.models.DenseNet(weights="densenet121-res224-all")
+model.eval()
+
+
+def preprocess_image_from_url(image_url: str) -> torch.Tensor:
+    response = requests.get(image_url)
+    img = skimage.io.imread(BytesIO(response.content))
+    img = xrv.datasets.normalize(img, 255)
+
+    if img.ndim == 3:
+        img = img.mean(2)[None, ...]
+    elif img.ndim == 2:
+        img = img[None, ...]
+
+    transform = torchvision.transforms.Compose([
+        xrv.datasets.XRayCenterCrop(),
+        xrv.datasets.XRayResizer(224)
+    ])
+    img = transform(img)
+    img_tensor = torch.from_numpy(img)
+    return img_tensor
+
+
+def get_predictions_and_bounding_box(img_tensor: torch.Tensor):
+    with torch.no_grad():
+        output = model(img_tensor[None, ...])[0]
+
+    predictions = dict(zip(model.pathologies, output.numpy()))
+    sorted_preds = sorted(predictions.items(), key=lambda x: -x[1])
+
+    top_pred_label, top_conf = sorted_preds[0]
+    top_pred_index = list(model.pathologies).index(top_pred_label)
+
+    target_layer = model.features[-1]
+    cam = GradCAM(model=model, target_layers=[target_layer])
+    grayscale_cam = cam(input_tensor=img_tensor[None, ...],
+                        targets=[ClassifierOutputTarget(top_pred_index)])[0, :]
+
+    input_img = img_tensor.numpy()[0]
+    input_img_norm = (input_img - input_img.min()) / (input_img.max() - input_img.min())
+    input_img_rgb = cv2.cvtColor((input_img_norm * 255).astype(np.uint8), cv2.COLOR_GRAY2RGB)
+
+    cam_resized = cv2.resize(grayscale_cam, (224, 224))
+    cam_uint8 = (cam_resized * 255).astype(np.uint8)
+    _, thresh = cv2.threshold(cam_uint8, 100, 255, cv2.THRESH_BINARY)
+    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    bounding_boxes = []
+    for cnt in contours:
+        x, y, w, h = cv2.boundingRect(cnt)
+        bounding_boxes.append((int(x), int(y), int(x + w), int(y + h)))  
+
+    return sorted_preds, bounding_boxes
+
+
+class Prediction(BaseModel):
+    label: str
+    confidence: float
+
+
+class PredictionResponse(BaseModel):
+    predictions: List[Prediction]
+    top_prediction_bounding_boxes: List[Tuple[int, int, int, int]]
+
+
+@app.get("/predict", response_model=PredictionResponse)
+def predict(image_url: str = Query(..., description="URL of chest X-ray image")):
+    try:
+        img_tensor = preprocess_image_from_url(image_url)
+        preds, bboxes = get_predictions_and_bounding_box(img_tensor)
+        prediction_list = [Prediction(label=label, confidence=float(conf)) for label, conf in preds]
+
+        return PredictionResponse(
+            predictions=prediction_list,
+            top_prediction_bounding_boxes=bboxes
+        )
+    except Exception as e:
+        return {"error": str(e)}
+
+class URLRequest(BaseModel):
+    url: str
+
+@app.post("/predict", response_model=PredictionResponse)
+def predict_from_url(body: URLRequest):
+    try:
+        img_tensor = preprocess_image_from_url(body.url)
+        preds, bboxes = get_predictions_and_bounding_box(img_tensor)
+        prediction_list = [Prediction(label=label, confidence=float(conf)) for label, conf in preds]
+
+        return PredictionResponse(
+            predictions=prediction_list,
+            top_prediction_bounding_boxes=bboxes
+        )
+    except Exception as e:
+        return {"error": str(e)}
+    
+
+
+# uvicorn app:app --reload

requirements.txt

@@ -0,0 +1,9 @@
+fastapi 
+uvicorn 
+torch 
+torchvision 
+scikit-image 
+opencv-python 
+requests 
+torchxrayvision
+grad-cam