from fastapi import FastAPI, Request, Form
from fastapi.responses import HTMLResponse
from fastapi.templating import Jinja2Templates
from fastapi.staticfiles import StaticFiles
import numpy as np
import pickle
import uvicorn
import pickle
import pandas as pd


app = FastAPI()

templates = Jinja2Templates(directory="templates")


try:
    with open('final_3dprint_model.pkl', 'rb') as file:
        model = pickle.load(file)
    print("Model loaded successfully.")
except Exception as e:
    print(f"Error loading model: {e}")
    model = None


MODEL_FEATURES = [
    'layer_height', 'wall_thickness', 'infill_density', 'nozzle_temperature',
    'bed_temperature', 'print_speed', 'fan_speed', 'infill_pattern_grid',
    'infill_pattern_honeycomb', 'material_abs', 'material_pla'
]


@app.get("/")
def home(request: Request):
    """
    Renders the main input form page.
    """
    return templates.TemplateResponse("index.html", {"request": request})


@app.post("/predict")
async def predict(request: Request):
    """
    Receives form data, preprocesses it, makes a prediction,
    and re-renders the page with the results.
    """
    if model is None:
        return templates.TemplateResponse("index.html", {
            "request": request,
            "prediction_text": "Error: Model could not be loaded."
        })
        
    try:

        form_data = await request.form()

        input_data = {feature: 0 for feature in MODEL_FEATURES}


        input_data['layer_height'] = float(form_data['layer_height'])
        input_data['wall_thickness'] = int(form_data['wall_thickness'])
        input_data['infill_density'] = int(form_data['infill_density'])
        input_data['nozzle_temperature'] = int(form_data['nozzle_temperature'])
        input_data['bed_temperature'] = int(form_data['bed_temperature'])
        input_data['print_speed'] = int(form_data['print_speed'])
        input_data['fan_speed'] = int(form_data['fan_speed'])

        if form_data['infill_pattern'] == 'grid':
            input_data['infill_pattern_grid'] = 1
        elif form_data['infill_pattern'] == 'honeycomb':
            input_data['infill_pattern_honeycomb'] = 1
            
        if form_data['material'] == 'abs':
            input_data['material_abs'] = 1
        elif form_data['material'] == 'pla':
            input_data['material_pla'] = 1


        input_df = pd.DataFrame([input_data], columns=MODEL_FEATURES)


        prediction_array = model.predict(input_df)

        prediction_results = {
            'roughness': prediction_array[0, 0],
            'tension_strenght': prediction_array[0, 1],
            'elongation': prediction_array[0, 2]
        }

        return templates.TemplateResponse("index.html", {
            "request": request,
            "prediction": prediction_results
        })

    except Exception as e:

        return templates.TemplateResponse("index.html", {
            "request": request,
            "prediction": {"error": f"An error occurred: {e}"}
        })
if __name__ == "__main__":
    import uvicorn
    uvicorn.run("main:app", host="0.0.0.0", port=7860)