from fastapi import FastAPI, File, UploadFile, Request, Form
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from transformers import pipeline
import uvicorn
from PIL import Image
import io
import numpy as np
from projects.DL_CatDog.DL_CatDog import preprocess_image, read_image, model_DL_CatDog
from projects.ML_StudentPerformance.ML_StudentPerformace import predict_student_performance, create_custom_data, form1
from projects.ML_DiabetesPrediction.ML_DiabetesPrediction import model_ML_DiabetesPrediction, form2

app = FastAPI()

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # You can restrict this to specific origins if needed
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Health check route
@app.get("/api/working")
def home():
    return {"message": "FastAPI server is running on Hugging Face Spaces!"}

# Prediction route for DL_CatDog
@app.post("/api/predict1")
async def predict_DL_CatDog(file: UploadFile = File(...)):
    try:
        image = read_image(file)
        preprocessed_image = preprocess_image(image)
        prediction = model_DL_CatDog.predict(preprocessed_image)
        predicted_class = "Dog" if np.round(prediction[0][0]) == 1 else "Cat"
        return JSONResponse(content={"ok": 1, "prediction": predicted_class})
    except Exception as e:
        return JSONResponse(content={"ok": -1, "message": f"Something went wrong! {str(e)}"}, status_code=500)

# Classification route for DL_PlantDisease
pipe = pipeline("image-classification", model="wambugu71/crop_leaf_diseases_vit")
@app.post("/api/classify")
async def classify_image(file: UploadFile = File(...)):
    try:
        # Read the uploaded image file
        image = Image.open(io.BytesIO(await file.read()))

        # Run the image through the Hugging Face model
        predictions = pipe(image)

        return JSONResponse(content={"ok": 1, "predictions": predictions})
    except Exception as e:
        return JSONResponse(content={"ok": -1, "message": f"Something went wrong! {str(e)}"}, status_code=500)


# Prediction route for ML_StudentPerformance
@app.post("/api/predict2")
async def predict_student_performance_api(request: form1):
    print(request, end='\n\n\n\n')
    try:
        # Create the CustomData object
        custom_data = create_custom_data(
            gender= request.gender,
            ethnicity= request.ethnicity,
            parental_level_of_education= request.parental_level_of_education,
            lunch= request.lunch,
            test_preparation_course= request.test_preparation_course,
            reading_score= request.reading_score,
            writing_score= request.writing_score
        )
        # Perform the prediction
        result = predict_student_performance(custom_data)
        return JSONResponse(content={"ok": 1, "prediction": result})
    except Exception as e:
        return JSONResponse(content={"ok": -1, "message": f"Something went wrong! {str(e)}"}, status_code=500)

# Prediction route for ML_DiabetesPrediction
@app.post("/api/predict3")
async def predict_student_performance_api(req: form2):
    try:
        input_data = (req.Pregnancies, req.Glucose, req.BloodPressure, req.SkinThickness, req.Insulin, req.BMI, req.DiabetesPedigreeFunction, req.Age)

        # changing the input_data to numpy array
        input_data_as_numpy_array = np.asarray(input_data)

        # reshape the array as we are predicting for one instance
        input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

        # Perform the prediction
        prediction = model_ML_DiabetesPrediction.predict(input_data_reshaped)[0]
        prediction = int(prediction)

        return JSONResponse(content={"ok": 1, "prediction": prediction})
    except Exception as e:
        return JSONResponse(content={"ok": -1, "message": f"Something went wrong! {str(e)}"}, status_code=500)

# Main function to run the FastAPI server
if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)