app.py

from flask import Flask, request, jsonify
from flask_cors import CORS
import os
import pandas as pd
# from utils.analytics import get_summary_stats
from model.mood_predict import mood_predict
from utils.mapping import map_to_mood
import datetime

app = Flask(__name__)

CORS(app) 

# --- Data File ---
DATA_DIR = "/tmp/data"
LOGS_CSV = os.path.join(DATA_DIR, "logs.csv")
LOG_COLUMNS = ['date', 'exercise', 'water', 'reading', 'meditation', 'mood', 'journal_text']

# --- Helper Function ---
def init_log_file():
    """Creates the log file with headers if it doesn't exist."""
    if not os.path.exists(DATA_DIR):
        os.makedirs(DATA_DIR, exist_ok=True)
    if not os.path.exists(LOGS_CSV):
        df = pd.DataFrame(columns=LOG_COLUMNS)
        df.to_csv(LOGS_CSV, index=False)


# --- API Endpoints ---

@app.route("/")
def home():
    return jsonify({'message': "MindTrack Backend is running!"}), 201

@app.route("/log", methods=["POST"])
def log_habit():
    """
    Saves a new log entry.
    This is now a "read-modify-write" operation to handle
    duplicate dates (overwrite) and file creation.
    """
    new_log_data = request.json
    
    if not new_log_data or 'date' not in new_log_data:
        return jsonify({"error": "No data or date provided"}), 400
        
    try:
        # 1. Ensure the data directory exists
        os.makedirs(DATA_DIR, exist_ok=True)
        
        # 2. Load existing data if file exists
        if os.path.exists(LOGS_CSV):
            df = pd.read_csv(LOGS_CSV)
        else:
            # Create an empty DataFrame WITH the correct columns
            df = pd.DataFrame(columns=LOG_COLUMNS)

        # 3. Check for and remove duplicate date (for overwrite)
        new_date = new_log_data['date']
        if not df.empty and new_date in df['date'].values:
            print(f"Duplicate date found: {new_date}. Overwriting old entry.")
            df = df[df['date'] != new_date] # Keep all rows *except* the one with the duplicate date
        
        # 4. Create a DataFrame for the new entry, ensuring it also has all columns
        df_entry = pd.DataFrame([new_log_data], columns=LOG_COLUMNS)
        
        # 5. Append new entry to the (potentially filtered) DataFrame
        df_updated = pd.concat([df, df_entry], ignore_index=True)
        
        # 6. Sort by date for consistency (optional but good practice)
        df_updated = df_updated.sort_values(by='date')
        
        # 7. Save the entire updated DataFrame back to the CSV
        # header=True is the default and correct
        df_updated.to_csv(LOGS_CSV, index=False)
        
        return jsonify({"message": "Log saved successfully"}), 201
        
    except Exception as e:
        print(f"Error saving log: {e}")
        return jsonify({"error": "Failed to save log"}), 500


@app.route('/get_all_logs', methods=['GET'])
def get_all_logs():
    """
    Reads all log entries from the CSV and returns them as JSON.
    This is the "Single Source of Truth" endpoint for the dashboard.
    """
    if not os.path.exists(LOGS_CSV):
        # If the file doesn't exist yet, just return an empty list
        return jsonify([]) 
        
    try:
        df = pd.read_csv(LOGS_CSV)
        
        # Convert DataFrame to JSON (orient='records' gives a list of dicts)
        logs_json = df.to_dict(orient='records')
        
        return jsonify(logs_json)
        
    except Exception as e:
        print(f"Error reading logs: {e}")
        return jsonify({"error": "Failed to retrieve logs"}), 500


@app.route("/predict_mood", methods=["POST"])
def predict_mood():
    """
    Predicts the sentiment of a given journal text.
    """
    try:
        data = request.json
        text = data.get("text")
        
        if not text or text.strip() == "":
            return jsonify({"error": "No text provided"}), 400
        
        pred_moods = mood_predict(text)
        mood = pred_moods.get('label', 'Neutral')
        score = pred_moods.get('score', 0.0)

        mapped_mood = map_to_mood(mood)
        
        return jsonify({
            "mood": mapped_mood,
            "score": score
        }), 200

    except Exception as e:
        app.logger.error(f"Error in /predict_mood: {e}")
        return jsonify({"error": str(e)}), 500


@app.route('/reset_logs', methods=['POST'])
def reset_logs():
    """
    Deletes the logs.csv file to reset the dashboard to sample data.
    This is for demo purposes.
    """
    try:
        if os.path.exists(LOGS_CSV):
            os.remove(LOGS_CSV)
            print("logs.csv has been deleted.")
            return jsonify({"message": "Log file deleted successfully. Dashboard will reset to sample data."}), 200
        else:
            print("logs.csv not found, no action needed.")
            return jsonify({"message": "No log file to delete."}), 200
    except Exception as e:
        print(f"Error deleting log file: {e}")
        return jsonify({"error": f"Failed to delete log file: {e}"}), 500


# --- Main execution ---
if __name__ == "__main__":
    init_log_file() # Ensure log file exists on startup
    # For Render deployment, Render sets the PORT env variable.
    # port = int(os.environ.get("PORT", 5000))
    app.run(host="0.0.0.0", port=7860, debug=True) # Debug=True is fine for hackathon