flask_app.py

from flask import Flask, render_template, request
import requests
import json

app = Flask(__name__)

# API URL
API_URL = "https://ak0601-et-alzheimer.hf.space/predict"

# Feature names in order
FEATURE_NAMES = [
    "ROI",
    "nFixations",
    "nTobiiSaccades",
    "regSaccades",
    "longSaccades",
    "tinySaccades",
    "saccadeTotLength",
    "totalFixTime",
    "totalSpokenTime",
    "speechDelay",
    "endSpeechDelay",
    "startPupL",
    "startPupR",
    "endPupL",
    "endPupR",
    "diffPupL",
    "diffPupR"
]

@app.route('/', methods=['GET', 'POST'])
def index():
    prediction_result = None
    input_values = {}
    error_message = None

    if request.method == 'POST' and 'predict_btn' in request.form:
        try:
            # Collect and convert features
            features = []
            for name in FEATURE_NAMES:
                val = request.form.get(name)
                input_values[name] = val # Keep for re-populating form
                features.append(float(val))

            # Prepare payload
            payload = {"features": features}

            # Call API
            response = requests.post(API_URL, json=payload)
            
            if response.status_code == 200:
                result = response.json()
                # Determine class label (optional mapping)
                class_map = {0: "Control/Healthy", 1: "MCI (Mild Cognitive Impairment)", 2: "Alzheimer's Disease"}
                predicted_class_idx = result.get("predicted_class")
                predicted_label = class_map.get(predicted_class_idx, f"Class {predicted_class_idx}")
                
                prediction_result = {
                    "class_index": predicted_class_idx,
                    "label": predicted_label,
                    "confidence": f"{result.get('confidence', 0):.2%}",
                    "probabilities": result.get("probabilities")
                }
            else:
                error_message = f"API Error: {response.status_code} - {response.text}"

        except ValueError:
            error_message = "Invalid input: Please ensure all fields contain numeric values."
        except requests.exceptions.ConnectionError:
            error_message = "Connection Error: Could not connect to the prediction API."
        except Exception as e:
            error_message = f"An error occurred: {str(e)}"

    return render_template('index.html', feature_names=FEATURE_NAMES, result=prediction_result, inputs=input_values, error=error_message)

if __name__ == '__main__':
    app.run(debug=True, port=7860) # Use 8080 to avoid conflict with default 5000 if needed