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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+dense_final_finetuned.keras filter=lfs diff=lfs merge=lfs -text

app.py

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+# ==========================================
+# IMPORTS
+# ==========================================
+from fastapi import FastAPI, File, UploadFile
+from pydantic import BaseModel
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+import pandas as pd
+import numpy as np
+import joblib
+import tensorflow as tf
+from PIL import Image
+import io
+
+# ==========================================
+# INITIALIZE APP
+# ==========================================
+app = FastAPI(
+    title="Stroke Detection API (CT + Clinical Data)",
+    description="Deep Learning (DenseNet121) + ML Logistic Regression",
+    version="2.0"
+)
+
+# CORS setup
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ==========================================
+# LOAD MODELS
+# ==========================================
+logistic_model = joblib.load("E:/Data Science Study/ML Project/Arshi/Models/stroke_logistic_regression_model.pkl")
+preprocessor = joblib.load("E:/Data Science Study/ML Project/Arshi/Models/preprocessor.pkl")
+cnn_model = tf.keras.models.load_model("E:/Data Science Study/ML Project/Arshi/Models/dense_final_finetuned.keras")
+
+IMG_SIZE = (224, 224)
+
+# ==========================================
+# Pydantic Models
+# ==========================================
+class StrokeInput(BaseModel):
+    age: float
+    avg_glucose_level: float
+    bmi: float
+    hypertension: int
+    heart_disease: int
+    gender: str
+    ever_married: str
+    Residence_type: str
+    work_type: str
+    smoking_status: str
+
+class StrokeOutput(BaseModel):
+    stroke_prediction: int
+    stroke_probability: float
+
+# ==========================================
+# HELPER FUNCTIONS
+# ==========================================
+def preprocess_image(image_bytes):
+    img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    img = img.resize(IMG_SIZE)
+    img_arr = tf.keras.preprocessing.image.img_to_array(img) / 255.0
+    img_arr = np.expand_dims(img_arr, axis=0)
+    return img_arr
+
+def predict_image_cnn(img_tensor, threshold=0.5):
+    prob = cnn_model.predict(img_tensor)[0][0]
+    label = "Stroke Detected" if prob >= threshold else "Normal Brain"
+    return label, float(prob)
+
+# ==========================================
+# ENDPOINT 1: STRUCTURED DATA ML MODEL
+# ==========================================
+@app.post("/stroke-predict-struct", response_model=StrokeOutput)
+def predict_stroke_struct(data: StrokeInput):
+
+    df = pd.DataFrame([data.dict()])
+
+    # Feature Engineering
+    df['age_glu_interaction'] = df['age'] * df['avg_glucose_level']
+    df['ht_hd_score'] = df['hypertension'] + df['heart_disease']
+
+    df['work_type_simplified'] = df['work_type'].replace({
+        'children': 'No_Work',
+        'Never_worked': 'No_Work',
+        'Private': 'Private',
+        'Self-employed': 'Self_Employed',
+        'Govt_job': 'Govt'
+    })
+
+    df['smoke_simplified'] = df['smoking_status'].replace({
+        'formerly smoked': 'Former',
+        'never smoked': 'Never',
+        'smokes': 'Smoker',
+        'Unknown': 'Unknown'
+    })
+
+    df['glucose_bin'] = pd.cut(
+        df['avg_glucose_level'],
+        bins=[0, 100, 140, np.inf],
+        labels=['Normal', 'Prediabetic', 'High']
+    )
+
+    selected_features = [
+        'age','avg_glucose_level','bmi','age_glu_interaction',
+        'hypertension','heart_disease','ht_hd_score',
+        'gender','ever_married','Residence_type',
+        'work_type_simplified','smoke_simplified','glucose_bin'
+    ]
+
+    df = df[selected_features]
+
+    processed = preprocessor.transform(df)
+    prob = logistic_model.predict_proba(processed)[0][1]
+    pred = logistic_model.predict(processed)[0]
+
+    return {
+        "stroke_prediction": int(pred),
+        "stroke_probability": float(round(prob, 4))
+    }
+
+# ==========================================
+# ENDPOINT 2: MRI IMAGE CNN MODEL
+# ==========================================
+@app.post("/stroke-predict-image")
+async def predict_stroke_image(file: UploadFile = File(...)):
+    image_bytes = await file.read()
+    img_tensor = preprocess_image(image_bytes)
+
+    label, prob = predict_image_cnn(img_tensor)
+
+    return JSONResponse({
+        "filename": file.filename,
+        "prediction": label,
+        "confidence_score": float(round(prob, 4))
+    })

dense_final_finetuned.keras

@@ -0,0 +1,3 @@
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+oid sha256:d64058908f87df5502186ea7d210c0218b4df1c9ec72e3de47274d32a2b2f05b
+size 39468817

preprocessor.pkl

@@ -0,0 +1,3 @@
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+oid sha256:b0a2aee25e2edc731a241d2e3c8d5ba89dd6ad269a6619967b82bab46b0a35d8
+size 6020

stroke_logistic_regression_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c211ffd2f14ad218ade62283081290520f245322dd05c2f6748b442c4d848ba5
+size 1023