Add checks to avoid long messages and messages without a question

app.py

@@ -1,7 +1,7 @@
 from functions import *
 
 
-scores_parameters, authors, models, temperature_values = get_main_data()
+scores_parameters, authors, _, temperature_values = get_main_data()
 
 with gr.Blocks() as app:
     msg_history = gr.State()  # Messages with the format used by OpenAI
@@ -9,7 +9,6 @@ with gr.Blocks() as app:
 
     with gr.Tab('Test Chats'):
         with gr.Row():
-            model = gr.Textbox(value=models[0], label='Model', interactive=False)
             author = gr.Dropdown(authors, value=authors[0], label='Author', interactive=True)
             temperature = gr.Radio(temperature_values, label="Randomness", value=0.2)
             chat_btn = gr.Button(value='Start chat')
@@ -44,12 +43,12 @@ with gr.Blocks() as app:
 
     message.submit(
         get_answer,
-        [message, msg_history, chatbot, waiting_time, temperature, model],
+        [message, msg_history, chatbot, waiting_time, temperature],
         [message, msg_history, chatbot, waiting_time])
 
     scores_btn.click(
         save_scores,
-        [author, temperature, chatbot, waiting_time, model, opinion_box] + scores,
+        [author, temperature, chatbot, waiting_time, opinion_box] + scores,
         scores_box)
 
 

functions.py

@@ -12,7 +12,7 @@ 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
+    model used and possible temperature values
     """
     openai.api_key = os.environ.get('API_KEY')
 
@@ -22,10 +22,10 @@ def get_main_data():
     ]
 
     authors = ['Sofia', 'Eliza', 'Sindy', 'Carlos', 'Andres', 'Adriana', 'Carolina', 'Valeria']
-    models = ["gpt-4"]
-    temperature_values = [0.2, 0.8, 1.0]
+    model = "gpt-4"
+    temperature_values = [0.8, 1.0, 1.2]
 
-    return scores_parameters, authors, models, temperature_values
+    return scores_parameters, authors, model, temperature_values
 
 
 def innit_bot():
@@ -58,19 +58,19 @@ def make_noninteractive():
         gr.Radio.update(interactive=False))
 
 
-def call_api(model: gr.Dropdown, msg_history: gr.State, temperature: gr.State):
+def call_api(msg_history: gr.State, temperature: gr.State):
     """
     Returns the API's response
     """
     response = openai.ChatCompletion.create(
-        model=model,
+        model="gpt-4",
         messages=msg_history,
         temperature=temperature
     )
     return response
 
 
-def handle_call(model: gr.Dropdown, msg_history: gr.State, temperature: gr.State):
+def handle_call(msg_history: gr.State, temperature: gr.State):
     """
     Returns the response and waiting time of the AI. It also handles the possible errors
     """
@@ -79,7 +79,7 @@ def handle_call(model: gr.Dropdown, msg_history: gr.State, temperature: gr.State
     while True:
         try:
             start_time = time.time()
-            response = call_api(model, msg_history, temperature)
+            response = call_api(msg_history, temperature)
             end_time = time.time()
             break
 
@@ -103,13 +103,13 @@ def handle_call(model: gr.Dropdown, msg_history: gr.State, temperature: gr.State
     return response, needed_time
 
 
-def get_ai_answer(msg: str, model: gr.Dropdown, msg_history: gr.State, temperature: gr.State):
+def get_ai_answer(msg: str, 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
+    the api took to retrieve such response.
     """
     msg_history.append({"role": "user", "content": msg})
-    response, needed_time = handle_call(model, msg_history, temperature)
+    response, needed_time = handle_call(msg_history, temperature)
     AI_response = response["choices"][0]["message"]["content"]
     msg_history.append({'role': 'assistant', 'content': AI_response})
 
@@ -117,9 +117,8 @@ def get_ai_answer(msg: str, model: gr.Dropdown, msg_history: gr.State, temperatu
 
 
 def get_answer(
-        msg: str, msg_history: gr.State,
-        chatbot_history: gr.Chatbot, waiting_time: gr.State,
-        temperature: gr.State, model: gr.Dropdown):
+        msg: str, msg_history: gr.State, chatbot_history: gr.Chatbot,
+        waiting_time: gr.State, temperature: gr.State):
     """
     Cleans msg box, adds the new message to the message history,
     gets the answer from the bot and adds it to the chatbot history
@@ -127,7 +126,18 @@ def get_answer(
     """
 
     # Get bot answer (output), messages history and waiting time
-    AI_response, msg_history, needed_time = get_ai_answer(msg, model, msg_history, temperature)
+    AI_response, msg_history, needed_time = get_ai_answer(msg, msg_history, temperature)
+
+    # Make sure the AI_response is short, if not make it shorter
+    if len(AI_response) > 260:
+        new_msg = 'El mensaje esta muy largo. Da la misma idea (mandando el link, pregunta y/o promocion que hayas ' \
+                  'dado) pero usando 40 palabras.'
+        AI_response, msg_history, needed_time = get_ai_answer(new_msg, msg_history, temperature)
+
+    # Make sure the AI_response has at least one question
+    if '?' not in AI_response:
+        new_msg = 'Incluye 1 pregunta dentro del mensaje. Puede estar relacionada a lo que se hablo antes o algo nuevo.'
+        AI_response, msg_history, needed_time = get_ai_answer(new_msg, msg_history, temperature)
 
     # Save waiting time
     waiting_time.append(needed_time)
@@ -140,13 +150,12 @@ def get_answer(
 
 def save_scores(
         author: gr.Dropdown, temperature: gr.State,
-        history: gr.Chatbot, waiting_time: gr.State,
-        model: gr.Dropdown, opinion: gr.Textbox, *score_values):
+        history: gr.Chatbot, waiting_time: gr.State, 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()
+    score_parameters, _, model, _ = get_main_data()
 
     # Get the score of each parameter
     scores = dict()