multichat json v1

.gitignore

@@ -0,0 +1,2 @@
+venv/
+__pycache__/

app.py

@@ -1,27 +1,26 @@
 #https://discuss.huggingface.co/t/dynamical-flexible-output/18146/6
 #https://github.com/gradio-app/gradio/issues/2066
-import os
 import gradio as gr
 #from transformers import AutoModelForCausalLM, AutoTokenizer
 import pandas as pd
 from datetime import datetime, timedelta, timezone
 #import torch
-from config import groq_token, groq_model, QUESTION_PROMPT, init_google_sheets_client, huggingface_tokenizer, replicate_model,groq_model, default_model_name, user_names, google_sheets_name, MAX_INTERACTIONS
+from config import groq_token, groq_model, QUESTION_PROMPT, init_google_sheets_client, groq_model, default_model_name, user_names, google_sheets_name
 
 #from config import  hugging_face_token, replicate_token
 #import replicate
 import gspread
 from  groq import Client
-import random, string
-
-from pydrive.auth import GoogleAuth
-from pydrive.drive import GoogleDrive
-import json
+import random, string, json, io
+from googleapiclient.discovery import build
+from googleapiclient.http import MediaFileUpload, MediaIoBaseDownload
+from google.oauth2 import service_account # Import service_account module
 
 
 # Initialize Google Sheets client
 client = init_google_sheets_client()
 sheet = client.open(google_sheets_name)
+#sheet = client.open_by_key('1kA37sJps3nhki-s9S7J_mQtNoqoWOLvezV0HobHzQ4s') ID planilla chatbot test nuevo
 stories_sheet = sheet.worksheet("Stories")
 system_prompts_sheet = sheet.worksheet("System Prompts")
 
@@ -72,104 +71,119 @@ data = []
 chat_history = []
 model_history = []
 
+# Save all_answers to Google Drive
+FILE_ID = '1PwEiBxpHo0jRc6T1HixyC99UnP9iawbr'
+
+
+def save_answers(all_answers):
+    # Credenciales de la cuenta de servicio (reemplaza con tus credenciales)
+    SCOPES = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
+    SERVICE_ACCOUNT_FILE = 'polar-land-440713-c4-bbc8d89804d8.json'
+    # Autentificación
+    credentials = service_account.Credentials.from_service_account_file(SERVICE_ACCOUNT_FILE, scopes=SCOPES)
+    service = build('drive', 'v3', credentials=credentials)
+
+    # Obtener el archivo existente
+    file = service.files().get(fileId=FILE_ID).execute()
+
+    # Download the content using get_media instead of export_media
+    request = service.files().get_media(fileId=FILE_ID)
+    fh = io.BytesIO()
+    downloader = MediaIoBaseDownload(fh, request)
+    done = False
+    while done is False:
+        status, done = downloader.next_chunk()
+        print("Download %d%%." % int(status.progress() * 100))
+
+    # Cargar el contenido JSON
+    content = fh.getvalue()
+    if content:
+        existing_data = json.loads(content)
+    else:
+        existing_data = {}
+
+    # Convert sets to lists before serialization if they exist
+    def convert_sets_to_lists(obj):
+        if isinstance(obj, set):
+            return list(obj)
+        if isinstance(obj, dict):
+            return {k: convert_sets_to_lists(v) for k, v in obj.items()}
+        if isinstance(obj, list):
+            return [convert_sets_to_lists(item) for item in obj]
+        return obj
+
+    existing_data = convert_sets_to_lists(existing_data)
+
+    # Agregar los nuevos datos al arreglo
+    if 'data' in existing_data:
+        existing_data['data'].append(all_answers)
+    else:
+        existing_data['data'] = [all_answers]
+    # Convertir los datos a formato JSON
+    new_content = json.dumps(existing_data)
 
+    # Create a temporary file to store the JSON data
+    with open('temp_data.json', 'w') as temp_file: 
+        temp_file.write(new_content)
 
+    media = MediaFileUpload('temp_data.json', mimetype='application/json')  
+    file = service.files().update(fileId=FILE_ID, 
+                                   media_body=media, 
+                                   fields='id').execute()
+    print('Archivo actualizado correctamente: %s' % file.get('id'))
 
 
 
-from pydrive.auth import GoogleAuth
-from pydrive.drive import GoogleDrive
-import json
-
-# Autenticación y creación del cliente de Google Drive
-gauth = GoogleAuth()
-gauth.LocalWebserverAuth()  # Esto abrirá una ventana del navegador para autenticarse
-drive = GoogleDrive(gauth)
-
+    #Function to save comment and score
 def save_comment_score(score, comment, story_name, user_name, system_prompt, models):
     print("Saving comment and score...")
     print(chat_history)
     print(model_history)
     full_chat_history = ""
 
-    # Save all_answers to Google Drive
-    file_name = 'all_answers.json'
-    
-    # Check if the file already exists in Google Drive
-    file_list = drive.ListFile({'q': f"title='{file_name}' and trashed=false"}).GetList()
-    if file_list:
-        # File exists, download it
-        gfile = file_list[0]
-        gfile.GetContentFile(file_name)
-        with open(file_name, 'r') as json_file:
-            existing_data = json.load(json_file)
-        existing_data.append(all_answers)
-    else:
-        # File does not exist, create new data
-        existing_data = [all_answers]
-    
-    # Save updated data to the file
-    with open(file_name, 'w') as json_file:
-        json.dump(existing_data, json_file)
-    
-    # Upload the file to Google Drive
-    gfile = drive.CreateFile({'title': file_name})
-    gfile.SetContentFile(file_name)
-    gfile.Upload()
-    print(f"File {file_name} uploaded to Google Drive.")
-    
-    return None
-
-
-
-
-
-
-
-
-
-# Function to save comment and score
-#def save_comment_score(score, comment, story_name, user_name, system_prompt, models):
-#    print("Saving comment and score...")
-#    print(chat_history)
-#    print(model_history)
-#    full_chat_history = ""
-#
-#    # Create formatted chat history with roles
-#    #and model in model_history
-#    for message in chat_history:
-#        print(message['role'])
-#        if message['role'] == 'user':  # User message
-#            full_chat_history += f"User: {message['content']}\n"
-#        if message['role'] == 'assistant':  # Assistant message
-#            full_chat_history += f"Model:{model_history.pop(0)} Assistant: {message['content']}\n"
-#
-#    timestamp = datetime.now(timezone.utc) - timedelta(hours=3)  # Adjust to GMT-3
-#    timestamp_str = timestamp.strftime("%Y-%m-%d %H:%M:%S")
-#    model_name = (' ').join(models)
-#    # Append data to local data storage
-#    print(full_chat_history)
-#    data.append([
-#        timestamp_str,
-#        user_name,
-#        model_name,
-#        system_prompt,
-#        story_name,
-#        full_chat_history,
-#        score,
-#        comment
-#    ])
-#
-#    # Append data to Google Sheets
-#    try:
-#        user_sheet = client.open(google_sheets_name).worksheet(user_name)
-#    except gspread.exceptions.WorksheetNotFound:
-#        user_sheet = client.open(google_sheets_name).add_worksheet(title=user_name, rows="100", cols="20")
-#    
-#    user_sheet.append_row([timestamp_str, user_name, model_name, system_prompt, story_name, full_chat_history, score, comment])
-#
-#    df = pd.DataFrame(data, columns=["Timestamp", "User Name", "Model Name", "System Prompt", "Story Name", "Chat History", "Score", "Comment"])
-#    return df[["Chat History", "Score", "Comment"]], gr.update(value="")  # Show only the required columns and clear the comment input box
+    # Create formatted chat history with roles
+    #and model in model_history
+    for message in chat_history:
+        print(message['role'])
+        if message['role'] == 'user':  # User message
+            full_chat_history += f"User: {message['content']}\n"
+        if message['role'] == 'assistant':  # Assistant message
+            full_chat_history += f"Model:{model_history.pop(0)} Assistant: {message['content']}\n"
+
+    timestamp = datetime.now(timezone.utc) - timedelta(hours=3)  # Adjust to GMT-3
+    timestamp_str = timestamp.strftime("%Y-%m-%d %H:%M:%S")
+    model_name = (' ').join(models)
+    # Append data to local data storage
+    print(full_chat_history)
+    data.append([
+        timestamp_str,
+        user_name,
+        model_name,
+        system_prompt,
+        story_name,
+        full_chat_history,
+        score,
+        comment
+    ])
+
+    # Append data to Google Sheets
+    try:
+        user_sheet = client.open(google_sheets_name).worksheet(user_name)
+    except gspread.exceptions.WorksheetNotFound:
+        user_sheet = client.open(google_sheets_name).add_worksheet(title=user_name, rows="100", cols="20")
+
+    user_sheet.append_row([timestamp_str, user_name, model_name, system_prompt, story_name, full_chat_history, score, comment])
+
+
+    # Save all answers to Google Drive as a JSON file
+    print(f"all answers...\n{all_answers}")
+    save_answers(all_answers)
+
+    #Append data and render the data table
+    df = pd.DataFrame(data, columns=["Timestamp", "User Name", "Model Name", "System Prompt", "Story Name", "Chat History", "Score", "Comment"])
+    return df[["Chat History", "Score", "Comment"]], gr.update(value="")  # Show only the required columns and clear the comment input box
+
+
 
 
 # Function to handle interaction with model
@@ -272,26 +286,24 @@ def remove_metadata(json_array):
 # dont know the correct model beacuse it shuffles each time
 #selected model it's only the index in radio input
 def multiple_interact(query, models, selected_model, assistant_user_input): #, interaction_count)
-    print(f'chat_checkbox: {selected_model}')
+    #print(f'chat_checkbox: {selected_model}')
     resp_list = []
-    print(model_history)
-    #quito history ahora es una variable global
+    #print(model_history)
         
-
     if selected_model == "user_input":
-        chat_history.append({"role": "assistant", "content": assistant_user_input})
-        chat_history.append({"role": "user", "content": query})
-        
+        user_dialog = [{'response': {'role': 'assistant', 'content': assistant_user_input}, 'model': 'user_input'}]
         dialog = {
             "context":  remove_metadata(chat_history),
-            "assistant": assistant_user_input + chatbot_answser_list.values(),
+            "assistant": user_dialog + list(chatbot_answser_list.values()),
             "selected": "user_input",
             }
+        chat_history.append({"role": "assistant", "content": assistant_user_input})
+        chat_history.append({"role": "user", "content": query})
   
     else:
         dialog = {
             "context":  remove_metadata(chat_history),
-            "assistant": chatbot_answser_list.values(),
+            "assistant": list(chatbot_answser_list.values()),
             "selected": None,
             }
         
@@ -384,7 +396,7 @@ model_list = list(all_models.keys())
 active_models = []
 #chatbot_answer_list['model'] = "respuesta aqui"
 chatbot_answser_list = {}
-all_answers = {} #save all answers of all chatbots
+all_answers = [] #save all answers of all chatbots
 # Create the chat interface using Gradio Blocks
 active_models = []
 with gr.Blocks() as demo:

polar-land-440713-c4-bbc8d89804d8.json

@@ -0,0 +1,13 @@
+{
+  "type": "service_account",
+  "project_id": "polar-land-440713-c4",
+  "private_key_id": "bbc8d89804d81d789130b20ef8a78fc48e1adecb",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nKN8C9b4Vic/jM76HbFSJ/TK4\n-----END PRIVATE KEY-----\n",
+  "client_email": "[email protected]",
+  "client_id": "108705807236254785411",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/robot-481%40polar-land-440713-c4.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}

requirements.txt

@@ -8,4 +8,5 @@ oauth2client
 #accelerate
 #bitsandbytes
 replicate
-groq
+groq
+gradio