app.py

import io
import os
import json
import streamlit as st
import pandas as pd
from google.oauth2 import service_account
from googleapiclient.discovery import build
from googleapiclient.http import MediaIoBaseDownload, MediaIoBaseUpload

# Environment variables
FOLDER_ID = os.getenv("FOLDER_ID")
GOOGLE_CREDENTIALS = os.environ.get("GOOGLE_CREDENTIALS")
DATASET_NAME = os.environ.get("DATASET_NAME")

def create_new_file_in_drive(username, dataframe_to_upload, credentials_json, folder_id):
    """Crée un nouveau fichier CSV dans Google Drive à partir d'un DataFrame Pandas."""

    creds_dict = json.loads(credentials_json)

    # Charger les informations d'identification du compte de service
    creds = service_account.Credentials.from_service_account_info(creds_dict)

    # Construire le service API Drive
    service = build('drive', 'v3', credentials=creds)

    # Convertir le DataFrame en fichier CSV en mémoire
    csv_buffer = io.BytesIO()
    dataframe_to_upload.to_csv(csv_buffer, index=False, sep=';', encoding='utf-8')
    csv_buffer.seek(0)

    # Créer les métadonnées du fichier
    filename = f"rating-results-{username}.csv"
    file_metadata = {
        'name': filename,
        'parents': [folder_id]
    }

    # Télécharger le fichier CSV sur Google Drive
    media = MediaIoBaseUpload(csv_buffer, mimetype='text/csv', resumable=True)
    file = service.files().create(body=file_metadata, media_body=media, fields='id').execute()

    print(f"File '{filename}' created successfully.")

def main():
    st.title("Problematic Rating App")

    # Initialiser st.session_state.user et st.session_state.page si ce n'est pas déjà fait
    if 'user' not in st.session_state:
        st.session_state.user = None
    if 'page' not in st.session_state:
        st.session_state.page = 0 # 0 = page accueil, 1 = page notation

    # Page d'accueil pour saisir le nom de l'utilisateur
    if st.session_state.page == 0:
        st.session_state.user = st.text_input("Enter your name:")
        if st.session_state.user:
            st.session_state.page = 1
            st.rerun() # important
        else:
            st.stop()

    # Charger le fichier CSV
    try:
        df = pd.read_csv(DATASET_NAME, sep=';')
    except FileNotFoundError:
        st.error(f"{DATASET_NAME} not found. Please upload the file.")
        return

    # Initialisation des variables de session
    if 'problem_index' not in st.session_state:
        st.session_state.problem_index = 0
    if 'formulation_index' not in st.session_state:
        st.session_state.formulation_index = 0
    if 'results' not in st.session_state:
        st.session_state.results = []
    if 'specificity' not in st.session_state:
        st.session_state.specificity = None
    if 'clarity' not in st.session_state:
        st.session_state.clarity = None
    if 'priority' not in st.session_state:
        st.session_state.priority = None
        

    # Afficher la problématique à noter
    if st.session_state.problem_index < len(df):
        problem = df.iloc[st.session_state.problem_index]
        formulation_types = ['original_form', 'perspective_shift', 'paraphrase', 'structural_transformation']
        current_formulation = formulation_types[st.session_state.formulation_index]
        st.write(f"Technical problem {st.session_state.problem_index + 1}/{len(df)}")
        st.write(f"Formulation {st.session_state.formulation_index + 1}/4")
        st.write(problem[current_formulation])

        # Menu déroulant pour la notation
        st.write("Rate the problem on the following three criteria:")
        col1, col2, col3 = st.columns(3)

        with col1:
            st.session_state.specificity = st.radio("Specificity", options=[0, 1, 2, 3, 4, 5, "Don't know"], index=None)

        with col2:
            st.session_state.clarity = st.radio("Clarity", options=[0, 1, 2, 3, 4, 5, "Don't know"], index=None)

        with col3:
            st.session_state.priority = st.radio("Priority Order", options=[0, 1, 2, 3, 4, 5, "Don't know"], index=None)

        # Bouton pour passer à la formulation suivante ou à la problématique suivante
        if st.button("Next"):
            # Vérifier si le nom d'utilisateur est défini
            if st.session_state.user:
                st.session_state.results.append({
                    'user': st.session_state.user,
                    'formulation': current_formulation,
                    'problematic': problem[current_formulation],
                    'specificity': st.session_state.specificity,
                    'clarity': st.session_state.clarity,
                    'priority': st.session_state.priority
                })
                st.session_state.formulation_index += 1
                if st.session_state.formulation_index == 4:
                    st.session_state.formulation_index = 0
                    st.session_state.problem_index += 1
                st.rerun()
            else:
                st.warning("Please enter your name before proceeding.")
    else:
        # Afficher un message de remerciement et générer le fichier CSV
        st.write("Thanks for rating the problems!")
        st.session_state.results_df = pd.DataFrame(st.session_state.results)        
        create_new_file_in_drive(st.session_state.user, st.session_state.results_df, GOOGLE_CREDENTIALS, FOLDER_ID)
        

if __name__ == "__main__":
    main()