Create app.py

app.py

@@ -0,0 +1,57 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+import pandas as pd
+import pickle
+from fastapi.middleware.cors import CORSMiddleware
+
+
+# Load the saved Random Forest model
+with open("random_forest_pkl.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# Initialize FastAPI app
+app = FastAPI(
+    title="Soil Fertility Prediction API",
+    description="Predict soil fertility level (0=Low, 1=Medium, 2=High) using a trained Random Forest model.",
+    version="1.0.0"
+)
+
+# Enable CORS (for browser and external app access)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # or replace * with your website URL for security
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Define input data model
+class SoilInput(BaseModel):
+    N: float
+    P: float
+    K: float
+    pH: float
+    EC: float
+    OC: float
+    S: float
+    Zn: float
+    Fe: float
+    Cu: float
+    Mn: float
+    B: float
+
+# Root endpoint
+@app.get("/")
+def root():
+    return {"message": "Welcome to the Soil Fertility Prediction API"}
+
+# Prediction endpoint
+@app.post("/predict")
+def predict_fertility(data: SoilInput):
+    df = pd.DataFrame([data.model_dump()])
+    pred = model.predict(df)[0]
+    labels = {0: "Low Fertility", 1: "Medium Fertility", 2: "High Fertility"}
+    return {
+        "prediction": int(pred),
+        "class_label": labels[int(pred)]
+    }