from flask import Flask,render_template, request, jsonify, redirect, url_for
import os
import cv2
from App.dog_vision import identificationPipeline
import numpy as np

app = Flask(__name__)
UPLOAD_FOLDER ='Static/Upload'
PREDICT_FOLDER = './Static/Predict'
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER

@app.route('/', methods=['GET', 'POST'])
def index():
    if request.method == 'POST':
        file = request.files.get('image_name')
        
        # ✅ Check if file is provided
        if not file or file.filename == '':
            return jsonify({"error": "No file was uploaded"}), 400
        
        # ✅ Save Image
        path = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
        file.save(path)

        print(f"Received path in views.py: {path}, Type: {type(path)}")

        # ✅ Get Prediction (Expecting a label string)
        output = identificationPipeline(path)

        # ✅ Ensure the output is a valid string (Label name)
        if isinstance(output, np.ndarray):
            output = output.tolist()  # Convert NumPy array to Python list
        elif hasattr(output, 'numpy'):
            output = output.numpy().tolist()  # Convert TensorFlow tensor to list
        elif isinstance(output, dict):
            return jsonify(output)  # If it's a dictionary, return as JSON
        elif not isinstance(output, str):
            return jsonify({"error": "Unexpected output format"}), 500

        print(f"Output from identificationPipeline: {output}")  # Debugging line

        # ✅ Read and save the image properly
        image = cv2.imread(path)
        if image is None:
            return jsonify({"error": "Uploaded file is not a valid image format or is corrupted"}), 400

        pred_filename = 'image.jpg'
        pred_path = os.path.join(PREDICT_FOLDER, pred_filename)
        cv2.imwrite(pred_path, image)

        return redirect(url_for('breedIdentification', filename=file.filename, prediction=output))

    return render_template('index.html')


@app.route("/breedIdentification/")
def breedIdentification():
    filename = request.args.get('filename')
    prediction = request.args.get('prediction')

    if not filename or not prediction:
        return redirect(url_for('index'))  # Redirect to home if missing data

    return render_template('breedIdentification.html', filename=filename, prediction=prediction)