heart stroke project is added

README.md

@@ -1 +1,3 @@
-# heart_doc
+# heart_doc
+
+# Data is extracted from Kaggle

backend/main.py

@@ -0,0 +1,58 @@
+from fastapi import FastAPI
+import onnxruntime as ort 
+import numpy as np
+from pydantic import BaseModel
+import uvicorn
+from fastapi.middleware.cors import CORSMiddleware
+
+class HeartStrokeBase(BaseModel):
+    ST_Slope: float
+    ChestPainType: float 
+    ExerciseAngina: float
+    Cholesterol: float 
+    MaxHR: float 
+    Oldpeak: float
+    Sex: int 
+    FastingBS: float 
+    Age: int 
+    RestingBP: float  
+
+origins = ["*"]
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.post("/predict")
+async def heart_stroke_inference(data: HeartStrokeBase = None):
+
+    print(data.dict())
+    def sigmoid(x):
+        return 1 / (1 + np.exp(-x))
+
+    try:
+        data = data.dict().values()
+        data = [x for x in data]
+        data = np.array(data).astype(np.float32)
+        data = [list(data)]
+        model_path = 'HeartNet.onnx'
+        session = ort.InferenceSession(model_path)
+        input_name = session.get_inputs()[0].name
+        output_name = session.get_outputs()[0].name
+        output = session.run([output_name], {input_name: data})[0]
+
+        bin_data = sigmoid(output)
+        res = np.round(bin_data)
+        predict_ = "yes" if int(res[0][0]) == 1 else "no"
+        return {'type': "success", "value": predict_} 
+    except Exception as e:
+        return {'type': 'error', 'value': str(e)}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)