Refactor code

.gitignore

@@ -1,2 +1,3 @@
 .idea/*
-data.json
+data.json
+__pycache__/

app.py

@@ -1,208 +1,7 @@
-import os
-import time
-import json
-import openai
-import gradio as gr
-from datetime import datetime
-from openai.error import RateLimitError, APIConnectionError, Timeout, APIError, \
-    ServiceUnavailableError, InvalidRequestError
-from huggingface_hub import hf_hub_download, HfApi
+from functions import *
 
-openai.api_key = os.environ.get('API_KEY')
-
-score_parameters = [
-    'Personalidad', 'Intereses', 'Lenguaje/Estilo',
-    'Autenticidad', 'Habilidad de conversación',
-    'Marca/Producto', 'Identificación', 'Experiencia de uso',
-    'Recomendacion', 'Conversación organica'
-]
-
-authors = ['Sofia', 'Eliza', 'Sindy', 'Carlos', 'Andres', 'Adriana', 'Carolina', 'Valeria']
-
-models = ["gpt-4"]
-
-temperature_values = [0.2, 0.8, 1.0]
-
-
-def innit_bot():
-    """
-    Initialize the bot by adding the prompt from the txt file to the messages history
-    """
-    with open('prompt.txt', encoding='utf-8') as file:
-        prompt = file.read()
-    message_history = [{"role": "system", "content": prompt}]
-
-    return message_history
-
-
-def make_visible():
-    """
-    Makes visible the returned elements
-    """
-    return (
-        gr.Chatbot.update(visible=True),
-        gr.Textbox.update(visible=True),
-        gr.Row.update(visible=True))
-
-def make_noninteractive():
-    """
-    Makes no interactive the returned elements
-    """
-    return (
-        gr.Dropdown.update(interactive=False),
-        gr.Radio.update(interactive=False))
-
-
-def call_api(model: gr.Dropdown, msg_history: gr.State, temperature: gr.State):
-    """
-    Returns the API's response
-    """
-    response = openai.ChatCompletion.create(
-        model=model,
-        messages=msg_history,
-        temperature=temperature
-    )
-    return response
-
-
-def handle_call(model: gr.Dropdown, msg_history: gr.State, temperature: gr.State):
-    """
-    Returns the response and waiting time of the AI. It also handles the possible errors
-    """
-    tries = 0
-    max_tries = 3
-    while True:
-        try:
-            start_time = time.time()
-            response = call_api(model, msg_history, temperature)
-            end_time = time.time()
-            break
-
-        except InvalidRequestError as e:
-            print(e)
-            response = 'Ya no tienes mas tokens disponibles. Envia lo que tengas hasta el momento e inicia otro chat'
-            raise gr.Error(response)
-
-        except (RateLimitError, APIError, Timeout, APIConnectionError, ServiceUnavailableError) as e:
-            print(e)
-
-            if tries == max_tries:
-                response = "Despues de muchos intentos, no se pudo completar la comunicacion con OpenAI. " \
-                           "Envia lo que tengas hasta el momento e inicia un chat nuevo dentro de unos minutos."
-                raise gr.Error(response)
-
-            tries += 1
-            time.sleep(60)
-
-    needed_time = end_time - start_time
-    return response, needed_time
-
-
-def get_ai_answer(msg: str, model: gr.Dropdown, msg_history: gr.State, temperature: gr.State):
-    """
-    Returns the response given by the model, all the message history so far and the seconds
-    the api took to retrieve such response. Both depend on the model
-    """
-    msg_history.append({"role": "user", "content": msg})
-    response, needed_time = handle_call(model, msg_history, temperature)
-    AI_response = response["choices"][0]["message"]["content"]
-    msg_history.append({'role': 'assistant', 'content': AI_response})
-
-    return AI_response, msg_history, needed_time
-
-
-def get_answer(
-        msg: str, msg_history: gr.State,
-        chatbot_history: gr.Chatbot, waiting_time: gr.State,
-        temperature: gr.State, model: gr.Dropdown):
-    """
-    Cleans msg box, adds the new message to the message history,
-    gets the answer from the bot and adds it to the chatbot history
-    and gets the time needed to get such answer and saves it
-    """
-
-    # Get bot answer (output), messages history and waiting time
-    AI_response, msg_history, needed_time = get_ai_answer(msg, model, msg_history, temperature)
-
-    # Save waiting time
-    waiting_time.append(needed_time)
-
-    # Save output in the chat
-    chatbot_history.append((msg, AI_response))
-
-    return "", msg_history, chatbot_history, waiting_time
-
-
-def save_scores(
-        author: gr.Dropdown, temperature: gr.State,
-        history: gr.Chatbot, waiting_time: gr.State,
-        model: gr.Dropdown, opinion: gr.Textbox, *score_values):
-    """
-    Saves the scores and chat's info into the json file
-    """
-    # Get the score of each parameter
-    scores = dict()
-    for parameter, score in zip(score_parameters, score_values):
-
-        # Check the score is a valid value if not, raise Error
-        if score is None:
-            raise gr.Error('Asegurese de haber seleccionado al menos 1 opcion en cada categoria')
-
-        scores[parameter] = score
-
-    # Get all the messages including their reaction
-    chat = []
-    for conversation in history:
-        info = {
-            'message': conversation[0],
-            'answer': conversation[1],
-            'waiting': waiting_time.pop(0)
-        }
-        chat.append(info)
-
-    date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-
-    with open('prompt.txt', encoding='utf-8') as file:
-        prompt = file.read()
-
-    # Save the info
-    session = dict(
-        prompt=prompt,
-        temperature=temperature,
-        scores=scores,
-        opinion=opinion,
-        chat=chat,
-        author=author,
-        model=model,
-        date=date
-    )
-
-    # Open the file, add the new info and save it
-    hf_hub_download(
-        repo_id=os.environ.get('DATA'), repo_type='dataset', filename="data.json", token=os.environ.get('HUB_TOKEN'),
-        local_dir="./"
-    )
-
-    with open('data.json', 'r') as infile:
-        past_sessions = json.load(infile)
-
-    # Add the new info
-    past_sessions['sessions'].append(session)
-    with open('data.json', 'w', encoding='utf-8') as outfile:
-        json.dump(past_sessions, outfile, indent=4, ensure_ascii=False)
-
-    # Save the updated file
-    api = HfApi(token=os.environ.get('HUB_TOKEN'))
-    api.upload_file(
-        path_or_fileobj="data.json",
-        path_in_repo="data.json",
-        repo_id=os.environ.get('DATA'),
-        repo_type='dataset'
-    )
-
-    # Return a confirmation message
-    return 'Done'
 
+scores_parameters, authors, models, temperature_values = get_main_data()
 
 with gr.Blocks() as app:
     msg_history = gr.State()  # Messages with the format used by OpenAI
@@ -226,8 +25,9 @@ with gr.Blocks() as app:
                 with gr.Row():
                     scores = [
                         gr.Radio(choices=['Aprovado', 'No aprovado'], label=parameter)
-                        for parameter in score_parameters
+                        for parameter in scores_parameters
                     ]
+
             with gr.Column(scale=25):
                 opinion_box = gr.Textbox(label='Opinion')
                 scores_btn = gr.Button(value='Send scores')
@@ -239,8 +39,7 @@ with gr.Blocks() as app:
     ).then(
         make_noninteractive, None, [author, temperature]
     ).then(
-        make_visible, None, [
-            chatbot, message, scores_row]
+        make_visible, None, [chatbot, message, scores_row]
     )
 
     message.submit(

functions.py

@@ -0,0 +1,212 @@
+import os
+import time
+import json
+import openai
+import gradio as gr
+from datetime import datetime
+from openai.error import RateLimitError, APIConnectionError, Timeout, APIError, \
+    ServiceUnavailableError, InvalidRequestError
+from huggingface_hub import hf_hub_download, HfApi
+
+
+def get_main_data():
+    """
+    Initializes the key for the api and returns the parameters for the scores, name of the possible authors,
+    models and possible temperature values
+    """
+    openai.api_key = os.environ.get('API_KEY')
+
+    scores_parameters = [
+        'Personalidad', 'Intereses', 'Lenguaje/Estilo', 'Autenticidad', 'Habilidad de conversación',
+        'Marca/Producto', 'Identificación', 'Experiencia de uso', 'Recomendacion', 'Conversación organica'
+    ]
+
+    authors = ['Sofia', 'Eliza', 'Sindy', 'Carlos', 'Andres', 'Adriana', 'Carolina', 'Valeria']
+    models = ["gpt-4"]
+    temperature_values = [0.2, 0.8, 1.0]
+
+    return scores_parameters, authors, models, temperature_values
+
+
+def innit_bot():
+    """
+    Initialize the bot by adding the prompt from the txt file to the messages history
+    """
+    with open('prompt.txt', encoding='utf-8') as file:
+        prompt = file.read()
+    message_history = [{"role": "system", "content": prompt}]
+
+    return message_history
+
+
+def make_visible():
+    """
+    Makes visible the returned elements
+    """
+    return (
+        gr.Chatbot.update(visible=True),
+        gr.Textbox.update(visible=True),
+        gr.Row.update(visible=True))
+
+
+def make_noninteractive():
+    """
+    Makes no interactive the returned elements
+    """
+    return (
+        gr.Dropdown.update(interactive=False),
+        gr.Radio.update(interactive=False))
+
+
+def call_api(model: gr.Dropdown, msg_history: gr.State, temperature: gr.State):
+    """
+    Returns the API's response
+    """
+    response = openai.ChatCompletion.create(
+        model=model,
+        messages=msg_history,
+        temperature=temperature
+    )
+    return response
+
+
+def handle_call(model: gr.Dropdown, msg_history: gr.State, temperature: gr.State):
+    """
+    Returns the response and waiting time of the AI. It also handles the possible errors
+    """
+    tries = 0
+    max_tries = 3
+    while True:
+        try:
+            start_time = time.time()
+            response = call_api(model, msg_history, temperature)
+            end_time = time.time()
+            break
+
+        except InvalidRequestError as e:
+            print(e)
+            response = 'Ya no tienes mas tokens disponibles. Envia lo que tengas hasta el momento e inicia otro chat'
+            raise gr.Error(response)
+
+        except (RateLimitError, APIError, Timeout, APIConnectionError, ServiceUnavailableError) as e:
+            print(e)
+
+            if tries == max_tries:
+                response = "Despues de muchos intentos, no se pudo completar la comunicacion con OpenAI. " \
+                           "Envia lo que tengas hasta el momento e inicia un chat nuevo dentro de unos minutos."
+                raise gr.Error(response)
+
+            tries += 1
+            time.sleep(60)
+
+    needed_time = end_time - start_time
+    return response, needed_time
+
+
+def get_ai_answer(msg: str, model: gr.Dropdown, msg_history: gr.State, temperature: gr.State):
+    """
+    Returns the response given by the model, all the message history so far and the seconds
+    the api took to retrieve such response. Both depend on the model
+    """
+    msg_history.append({"role": "user", "content": msg})
+    response, needed_time = handle_call(model, msg_history, temperature)
+    AI_response = response["choices"][0]["message"]["content"]
+    msg_history.append({'role': 'assistant', 'content': AI_response})
+
+    return AI_response, msg_history, needed_time
+
+
+def get_answer(
+        msg: str, msg_history: gr.State,
+        chatbot_history: gr.Chatbot, waiting_time: gr.State,
+        temperature: gr.State, model: gr.Dropdown):
+    """
+    Cleans msg box, adds the new message to the message history,
+    gets the answer from the bot and adds it to the chatbot history
+    and gets the time needed to get such answer and saves it
+    """
+
+    # Get bot answer (output), messages history and waiting time
+    AI_response, msg_history, needed_time = get_ai_answer(msg, model, msg_history, temperature)
+
+    # Save waiting time
+    waiting_time.append(needed_time)
+
+    # Save output in the chat
+    chatbot_history.append((msg, AI_response))
+
+    return "", msg_history, chatbot_history, waiting_time
+
+
+def save_scores(
+        author: gr.Dropdown, temperature: gr.State,
+        history: gr.Chatbot, waiting_time: gr.State,
+        model: gr.Dropdown, opinion: gr.Textbox, *score_values):
+    """
+    Saves the scores and chat's info into the json file
+    """
+    # Get the parameters for each score
+    score_parameters, _, _, _ = get_main_data()
+
+    # Get the score of each parameter
+    scores = dict()
+    for parameter, score in zip(score_parameters, score_values):
+
+        # Check the score is a valid value if not, raise Error
+        if score is None:
+            raise gr.Error('Asegurese de haber seleccionado al menos 1 opcion en cada categoria')
+
+        scores[parameter] = score
+
+    # Get all the messages including their reaction
+    chat = []
+    for conversation in history:
+        info = {
+            'message': conversation[0],
+            'answer': conversation[1],
+            'waiting': waiting_time.pop(0)
+        }
+        chat.append(info)
+
+    date = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+    with open('prompt.txt', encoding='utf-8') as file:
+        prompt = file.read()
+
+    # Save the info
+    session = dict(
+        prompt=prompt,
+        temperature=temperature,
+        scores=scores,
+        opinion=opinion,
+        chat=chat,
+        author=author,
+        model=model,
+        date=date
+    )
+
+    # Open the file, add the new info and save it
+    hf_hub_download(
+        repo_id=os.environ.get('DATA'), repo_type='dataset', filename="data.json", token=os.environ.get('HUB_TOKEN'),
+        local_dir="./"
+    )
+
+    with open('data.json', 'r') as infile:
+        past_sessions = json.load(infile)
+
+    # Add the new info
+    past_sessions['sessions'].append(session)
+    with open('data.json', 'w', encoding='utf-8') as outfile:
+        json.dump(past_sessions, outfile, indent=4, ensure_ascii=False)
+
+    # Save the updated file
+    api = HfApi(token=os.environ.get('HUB_TOKEN'))
+    api.upload_file(
+        path_or_fileobj="data.json",
+        path_in_repo="data.json",
+        repo_id=os.environ.get('DATA'),
+        repo_type='dataset'
+    )
+
+    # Return a confirmation message
+    return 'Done'