main.py

main.py

+from fastapi import FastAPI, Request, Form
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import HTMLResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from uvicorn import run
+import numpy as np
+import os
+import joblib
+import json
+
+app = FastAPI()
+
+origins = ["*"]
+methods = ["*"]
+headers = ["*"]
+
+app.add_middleware(
+    CORSMiddleware, 
+    allow_origins = origins,
+    allow_credentials = True,
+    allow_methods = methods,
+    allow_headers = headers    
+)
+
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+
+templates = Jinja2Templates(directory="templates")
+
+
+def getPredict(topredict):
+    kn_classifier2 = joblib.load('fishRecModel.pkl' , mmap_mode ='r')
+
+    prediction = kn_classifier2.predict((topredict))
+
+    return prediction
+
+@app.get("/home",response_class=HTMLResponse)
+async def homePage(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
+@app.post("/result",response_class=HTMLResponse)
+async def resultPage(request: Request, NH3: str= Form(...),NO2: str= Form(...),NO3: str= Form(...),Temprature: str= Form(...),Oxygen: str= Form(...),PH: str= Form(...)):
+
+        to_predict_list =np.array([[NH3,NO2,NO3,Temprature,Oxygen,PH]])
+
+        prediction_r = getPredict(to_predict_list)
+        resultlist = prediction_r.tolist()
+        json_str = json.dumps(resultlist)
+        outcome = json_str.replace("[", "").replace("]", "")
+
+        return templates.TemplateResponse("result.html", {"request": request, "prediction":outcome})
+
+if __name__ == "__main__":
+    port = int(os.environ.get('PORT', 5000))
+    run(app, host="0.0.0.0", port=port)
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