app.py

#https://discuss.huggingface.co/t/dynamical-flexible-output/18146/6
#https://github.com/gradio-app/gradio/issues/2066
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, 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, 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")

# Combine both model dictionaries
all_models = {**groq_model}

def randomize_key_order(aux):
    keys  = list(aux.keys())
    #Shuffle the list of keys
    random.shuffle(keys)
    #Create a new dictionary with shuffled keys
    return {key: aux[key] for key in keys}


alphabet = list(string.ascii_uppercase)

# Initialize GROQ client
groq_clinet = Client(api_key=groq_token)

# Load stories from Google Sheets
def load_stories():
    stories_data = stories_sheet.get_all_values()
    stories = [{"title": story[0], "story": story[1]} for story in stories_data if story[0] != "Title"]  # Skip header row
    return stories


# Load system prompts from Google Sheets
def load_system_prompts():
    system_prompts_data = system_prompts_sheet.get_all_values()
    system_prompts = [prompt[0] for prompt in system_prompts_data[1:]]  # Skip header row
    return system_prompts


# Load available stories and system prompts
stories = load_stories()
system_prompts = load_system_prompts()


# Initialize the selected model
selected_model = default_model_name
tokenizer, model = None, None


# Initialize the data list
data = []

# Chat history
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'))



    #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])


    # 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
def interact_groq(context, model_name):
   chat_completion = groq_clinet.chat.completions.create(
       messages=context,
       model=model_name,
       temperature=0.1,
       max_tokens=100,
   )
   #print(chat_completion)
   return chat_completion.choices[0].message.content


#i=[story_dropdown, model_dropdown, system_prompt_dropdown],
#o=[chatbot_output, chat_history_json, data_table, selected_story_textbox])
# Function to send selected story and initial message
def send_selected_story(title, model_name, system_prompt):
   global chat_history
   global selected_story
   global data  # Ensure data is reset
   data = []  # Reset data for new story
   selected_story = title
   for story in stories:
       if story["title"] == title:
           system_prompt = f"""
{system_prompt}
Here is the story:
---
{story['story']}
---
           """
           combined_message = system_prompt.strip()
           if combined_message:
               chat_history = []  # Reset chat history
               chat_history.append({"role": "system", "content": combined_message})
               chat_history.append({"role": "user", "content": QUESTION_PROMPT})

               response = interact_groq(chat_history, model_name)
               resp = {"role": "assistant", "content": response.strip()}
               return resp, chat_history, story["story"]
           else:
               print("Combined message is empty.")
       else:
           print("Story title does not match.")


#i=[story_dropdown, model_dropdown, system_prompt_dropdown],
#o=[chatbot_output, chat_history_json, data_table, selected_story_textbox])
#recibo varios respuestas las muestro nomas, agrego al contexto solo la que se
#story_dropdown, model_checkbox, system_prompt_dropdown]

def send_multiple_selected_story(title, models, system_prompt):
    global model_history
    global chatbot_answser_list
    global all_answers
    resp_list = []
    print(models)
    #iterate over words
    #shuffle_models = randomize_key_order(all_models)
    random.shuffle(models)
    print(f"models shuffled: {models}")
    for index, model in enumerate(models):
        resp, context, _ = send_selected_story(title, model, system_prompt)
        chatbot_answser_list[alphabet[index]] = {'response': resp, 'model': model}
        try:
             print(resp)
             resp_list.append(gr.Chatbot(value=[resp], visible=True, type='messages'))
        except gr.exceptions.Error:
             print(f"error for en modelo {model}")
    
    
    rest = [model for model in model_list if model not in models]
    for model in rest:
        try:
            resp_list.append(gr.Chatbot(type='messages', visible=False))
        except gr.exceptions.Error:
            print(f"error, else en modelo {model}")
    
    try:
        resp_list.insert(0, gr.Chatbot(value=context, type='messages'))
        #chat_history ya se hace en send_selected_story
    except gr.exceptions.Error:
        print(f"error en main output\n {context}")

    return resp_list

#inputs=[user_input, chatbot_main_output, model_checkbox, chat_radio, assistant_user_input, chatbot_resp[0],  chatbot_resp[1], chatbot_resp[2], chatbot_resp[3]],# interaction_count],

def remove_metadata(json_array):
    print(json_array)
    print(type(json_array))
    json_aux = []
    for json_obj in json_array:
        print(f'objeto{json_obj}')
        json_aux.append({'role':json_obj["role"], 'content':json_obj["content"]})
    return json_aux


# 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}')
    resp_list = []
    #print(model_history)
        
    if selected_model == "user_input":
        user_dialog = [{'response': {'role': 'assistant', 'content': assistant_user_input}, 'model': 'user_input'}]
        dialog = {
            "context":  remove_metadata(chat_history),
            "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": list(chatbot_answser_list.values()),
            "selected": None,
            }
        
        #chatbot_answser_list
        #get the previous answer of the selected model
        for index, model in enumerate(models):
            if alphabet[index] == selected_model:
                selected_model_history = chatbot_answser_list[selected_model]['response']
                print(f"selected_model_history: {selected_model_history}")
                chat_history.append(selected_model_history)
                chat_history.append({"role": "user","content": query.strip()})
                #si es la correcta guardarla
                dialog["selected"] = chatbot_answser_list[selected_model]['model']
                break
    #APPE
    all_answers.append(dialog)
    #save to csv
    selected_model_history = {} #reset history 
    
    #creo que no precisa
    aux_history = remove_metadata(chat_history)
    #print(aux_history)
    

    #no es models es....
    random.shuffle(active_models)
    for index, model in enumerate(active_models):
        resp = interact_groq(aux_history, model)
        resp = {"role": "assistant", "content": resp.strip()}
        chatbot_answser_list[alphabet[index]] = {'response': resp, 'model': model}
        try:
             print(resp)
             resp_list.append(gr.Chatbot(value=[resp], visible=True, type='messages'))
        except gr.exceptions.Error:
             print(f"error for en modelo {model}")
    
    rest = [model for model in model_list if model not in active_models]
    for model in rest:
        try:
            resp_list.append(gr.Chatbot(type='messages', visible=False))
        except gr.exceptions.Error:
            print(f"error, else en modelo {model}")

    resp_list.insert(0, gr.Chatbot(value=aux_history, type='messages'))
    model_history.append(selected_model)
    print(model_history)

    return resp_list



# Function to load user guide from a file
def load_user_guide():
    with open('user_guide.txt', 'r') as file:
        return file.read()
  
def change_textbox(checkbox):
    if checkbox == "user_input":
         return  gr.Textbox(placeholder="Type your message here...", label="Assistant input", visible=True)
    else:
        return gr.Textbox(value="", visible=False)

def change_checkbox(checkbox):
    print(f'checkbox: {checkbox}')

    #luego cuando sean variables
    global active_models
    active_models = checkbox
    quant_models = len(checkbox)
    words = [alphabet[i] for i in range(quant_models)]
    checkbox = gr.Radio(label="Select Model to respond...", choices=words+["user_input"])
    #checkbox = gr.Radio(label="Select Model to respond...", choices=checkbox+["user_input"])
    return checkbox

def change_story(story_title, ret="gradio"):
    for story in stories:
          if story["title"] == story_title:
                if ret== "gradio":
                    return gr.Textbox(label="Selected Story", lines=10, interactive=False, value=story["story"])
                else: #"string"
                    return story["story"]
    return gr.Textbox(label="Error", lines=10, interactive=False, value="Story title does not match.")



    

chatbot_list = []
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
# Create the chat interface using Gradio Blocks
active_models = []
with gr.Blocks() as demo:
    with gr.Tabs():
        with gr.TabItem("Chat"):
            gr.Markdown("# Demo Chatbot V3")

            gr.Markdown("## Context")
            with gr.Group():
               model_dropdown = gr.Dropdown(choices=list(all_models.keys()), label="Select Models", value=model_list[0])
               user_dropdown = gr.Dropdown(choices=user_names, label="Select User Name")
               initial_story = stories[0]["title"] if stories else None
               story_dropdown = gr.Dropdown(choices=[story["title"] for story in stories], label="Select Story", value=initial_story)
               system_prompt_dropdown = gr.Dropdown(choices=system_prompts, label="Select System Prompt", value=system_prompts[0])
               send_story_button = gr.Button("Send Story")

            gr.Markdown("## Chat")
            with gr.Group():
               selected_story_textbox = gr.Textbox(label="Selected Story", lines=10, interactive=False)
               chatbot_output = gr.Chatbot(label="Chat History", type='messages')
               chatbot_input = gr.Textbox(placeholder="Type your message here...", label="User Input")
               send_message_button = gr.Button("Send")

            gr.Markdown("## Evaluation")
            with gr.Group():
               score_input = gr.Slider(minimum=0, maximum=5, step=1, label="Score")
               comment_input = gr.Textbox(placeholder="Add a comment...", label="Comment")
               save_button = gr.Button("Save Score and Comment")
               data_table = gr.DataFrame(headers=["Chat History", "Score", "Comment"])
    
        with gr.TabItem("User Guide"):
            gr.Textbox(label="User Guide", value=load_user_guide(), lines=20)



        with gr.TabItem("Multiple Evaluation"):
            with gr.Group():
               #model_dropdown = gr.Dropdown(choices=list(all_models.keys()), label="Select Model", value=default_model_name)
               model_checkbox = gr.CheckboxGroup(choices=list(all_models.keys()), label="Select Model", value=None) #value=[default_model_name])
               user_dropdown = gr.Dropdown(choices=user_names, label="Select User Name")
               story_dropdown = gr.Dropdown(choices=[story["title"] for story in stories], label="Select Story", value=initial_story)
               system_prompt_dropdown = gr.Dropdown(choices=system_prompts, label="Select System Prompt", value=system_prompts[0])
               send_multiple_story_button = gr.Button("Send Story")

            gr.Markdown("## Chat")
            with gr.Group():
                selected_story_textbox = gr.Textbox(label="Selected Story", lines=10, interactive=False, value=change_story(initial_story, "string"))
                #aqui armar una ventana x cada modelo seleccionado
                chatbot_list.append(gr.Chatbot(label="Chat History", type='messages'))
                with gr.Row():
                    for i, model in enumerate(model_list):
                        label = f"Model {alphabet[i % len(alphabet)]}"
                        aux = gr.Chatbot(label=label, visible=False, type='messages')
                        chatbot_list.append(aux)   
                        
                user_input = gr.Textbox(placeholder="Type your message here...", label="User Input")
                #chat_radio = gr.Radio(choices=list(model_list)+["user_input"], label="Sent something to continue...", value=[model_list[0]])
                chat_radio = gr.Radio(label="Select Model to respond...")
                #elegir respuesta primero, luego enviar mensaje
                assistant_user_input = gr.Textbox(interactive=True, show_copy_button=True, visible=False)     
                send_multiple_message_button = gr.Button("Send")
            
            gr.Markdown("## Evaluation")
            with gr.Group():
                score_input = gr.Slider(minimum=0, maximum=5, step=1, label="Score")
                comment_input = gr.Textbox(placeholder="Add a comment...", label="Comment")
                save_button_multievaluation = gr.Button("Save Score and Comment")
                data_table = gr.DataFrame(headers=["Chat History", "Score", "Comment"])

      
    interaction_count = gr.Number(value=0, visible=False)
    selected_model_array = gr.List(value=None, visible=False)

   #input es las entradas a la funcion
   #output es las salidas de la funcion? puede ser lo que se creo si
   #send_story_button.click(fn=send_selected_story, inputs=[story_dropdown, model_dropdown, system_prompt_dropdown], outputs=[chatbot_output, chat_history_json, data_table, selected_story_textbox])
   #send_message_button.click(fn=interact, inputs=[chatbot_input, chat_history_json, interaction_count, model_dropdown], outputs=[chatbot_input, chatbot_output, chat_history_json, interaction_count])
   #save_button.click(fn=save_comment_score, inputs=[chatbot_output, score_input, comment_input, story_dropdown, user_dropdown, system_prompt_dropdown], outputs=[data_table, comment_input])

    chat_radio.change(fn=change_textbox, inputs=chat_radio, outputs=assistant_user_input)
    #al elegir modelo cambia el chat radio, setea los modelos elegidos
    model_checkbox.input(fn=change_checkbox, inputs=model_checkbox, outputs=chat_radio)
    story_dropdown.input(fn=change_story, inputs=[story_dropdown], outputs=selected_story_textbox)

    send_multiple_story_button.click(
       fn=send_multiple_selected_story,
       inputs=[story_dropdown, model_checkbox, system_prompt_dropdown],
       outputs=chatbot_list,
       )
    
   #Tengo que cambiar para que los modelos responan solo las respuestas y no todo el historial
   #preciso las historias previas de cada una
   #el modelo que se haya elegido
   #aqui mando a solicitar...
   #luego retorno:
   #en

    send_multiple_message_button.click(
       fn=multiple_interact,
       inputs=[user_input, model_checkbox, chat_radio, assistant_user_input],# interaction_count],
       outputs=chatbot_list,
       )

#quiza tenga que guardar una variable con los valores de los checkbox
    save_button_multievaluation.click(
        fn=save_comment_score,
        inputs=[score_input, comment_input, story_dropdown, user_dropdown, system_prompt_dropdown,  model_checkbox],
        outputs=[data_table, comment_input])

demo.launch()