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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List
import uvicorn
from medimageinsightmodel import MedImageInsight
import base64
# Initialize FastAPI app
app = FastAPI(title="Medical Image Analysis API")
# Initialize model
classifier = MedImageInsight(
model_dir="2024.09.27",
vision_model_name="medimageinsigt-v1.0.0.pt",
language_model_name="language_model.pth"
)
classifier.load_model()
class ClassificationRequest(BaseModel):
images: List[str] # Base64 encoded images
labels: List[str]
multilabel : bool = False
class EmbeddingRequest(BaseModel):
images: List[str] = None # Base64 encoded images
texts: List[str] = None
@app.post("/predict")
async def predict(request: ClassificationRequest):
try:
results = classifier.predict(
images=request.images,
labels=request.labels,
multilabel = request.multilabel
)
return {"predictions": results}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/encode")
async def encode(request: EmbeddingRequest):
try:
results = classifier.encode(images=request.images, texts= request.texts)
results["image_embeddings"] = results["image_embeddings"].tolist() if results["image_embeddings"] is not None else None
results["text_embeddings"] = results["text_embeddings"].tolist() if results["text_embeddings"] is not None else None
return results
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/health")
async def health():
return {"status": "healthy"}
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8000) |