Upload 4 files

Dockerfile

@@ -0,0 +1,13 @@
+FROM python:3.10
+
+WORKDIR /code
+
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

diabetes_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f697fe210ee5bc5907e10f03ec13a084a6e69187ac15d82e6a455e0343d68b6
+size 1127

main.py

@@ -0,0 +1,97 @@
+# ====================================================
+# main.py - Diabetes Prediction API (Production)
+# ====================================================
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import numpy as np
+import joblib
+import os
+
+# -----------------------------
+# Load Trained Model & Scaler
+# -----------------------------
+MODEL_FILE = "diabetes_model.pkl"
+SCALER_FILE = "scaler.pkl"
+
+if not os.path.exists(MODEL_FILE) or not os.path.exists(SCALER_FILE):
+    raise FileNotFoundError("Model or scaler file not found. Make sure both exist in the same directory.")
+
+scaler = joblib.load(SCALER_FILE)
+best_model = joblib.load(MODEL_FILE)
+
+# -----------------------------
+# Initialize FastAPI app
+# -----------------------------
+app = FastAPI(
+    title="Diabetes Prediction API",
+    description="Predicts diabetes based on patient data using trained ML model",
+    version="1.0"
+)
+
+# -----------------------------
+# Enable CORS for all origins
+# -----------------------------
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # For production, replace '*' with allowed domains
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# -----------------------------
+# Define Input Data Model
+# -----------------------------
+class InputData(BaseModel):
+    Age: float
+    Sex: float
+    BMI: float
+    Glucose: float
+    BloodPressure: float
+    Insulin: float
+    Increased_Thirst: float
+    Increased_Hunger: float
+    Fatigue_Tiredness: float
+    Blurred_Vision: float
+    Unexplained_Weight_Loss: float
+
+# -----------------------------
+# Prediction Endpoint
+# -----------------------------
+@app.post("/predict")
+def predict(data: InputData):
+    try:
+        # Convert input to numpy array
+        features = np.array([[ 
+            data.Age,
+            data.Sex,
+            data.BMI,
+            data.Glucose,
+            data.BloodPressure,
+            data.Insulin,
+            data.Increased_Thirst,
+            data.Increased_Hunger,
+            data.Fatigue_Tiredness,
+            data.Blurred_Vision,
+            data.Unexplained_Weight_Loss
+        ]])
+
+        # Scale features
+        features_scaled = scaler.transform(features)
+
+        # Predict
+        prediction = best_model.predict(features_scaled)[0]
+        result = "Diabetes" if prediction == 1 else "No Diabetes"
+
+        return {"prediction": result}
+    
+    except Exception as e:
+        return {"error": str(e)}
+
+# -----------------------------
+# Run the API locally
+# -----------------------------
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("main:app", host="0.0.0.0", port=7860, reload=True)

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+numpy
+scikit-learn
+joblib