fixed

.gitignore

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+.env
+env
+/venv

app.py

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+import os
+from openai import OpenAI
+import gradio as gr
+import json
+from dotenv import load_dotenv
+
+# Load environment variables from the .env file de forma local
+load_dotenv()
+import base64
+
+with open("Iso_Logotipo_Ceibal.png", "rb") as image_file:
+    encoded_image = base64.b64encode(image_file.read()).decode()
+
+
+client1= OpenAI(api_key=os.environ['OPENAI_API_KEY'])
+client2= OpenAI(api_key=os.environ['OPENAI_API_KEY'])
+
+
+def clear_chat(message, chat_history):
+     return "", []
+
+def add_new_message_client1(message,person, chat_history):
+     new_chat = []
+     
+     new_chat.append({"role": "system", "content": 'Sos {} y tendrás que responder preguntas, las respuestas tienen que ser cómo si las hubiera dicho {} '.format(person,person)})
+   
+     for turn in chat_history:
+          user, bot = turn
+          new_chat.append({"role": "user", "content": user})
+          new_chat.append({"role": "assistant","content":bot})
+     new_chat.append({"role": "user","content":message})
+     return new_chat
+def add_new_message_client2(message,person, chat_history):
+     new_chat = []
+     
+     new_chat.append({"role": "system", "content": 'Sos {} y tendrás que responder preguntas, las respuestas tienen que ser cómo si las hubiera dicho {} '.format(person,person)})
+   
+     for turn in chat_history:
+          user, bot = turn
+          new_chat.append({"role": "user", "content": user})
+          new_chat.append({"role": "assistant","content":bot})
+     new_chat.append({"role": "user","content":message})
+     return new_chat   
+          
+counter2 =1
+def respond(person1,person2, chat_history):
+    print(chat_history)
+    global counter2
+    if(len(chat_history)<1):
+        message="Hola"
+        prompt = add_new_message_client1(message, person1, chat_history)
+        response = client1.chat.completions.create(
+                model="gpt-3.5-turbo",
+                messages= prompt,
+                temperature=0.5,
+                max_tokens=1000,
+                stream=False,
+                )
+        chat_history.append((message, response.choices[0].message.content))
+        
+    else:
+        counter2 +=1
+        if(counter2 % 2==0):
+            prompt = add_new_message_client1(chat_history[-1][1], person1, chat_history)
+            response = client1.chat.completions.create(
+                model="gpt-3.5-turbo",
+                messages= prompt,
+                temperature=0.5,
+                max_tokens=1000,
+                stream=False,
+                )
+            chat_history.append((response.choices[0].message.content, "" ))
+            
+
+        else:
+            prompt =add_new_message_client2(chat_history[-1][1], person2, chat_history)
+            response = client2.chat.completions.create(
+                model="gpt-3.5-turbo",
+                messages= prompt,
+                temperature=0.5,
+                max_tokens=1000,
+                stream=False,
+                )
+            chat_history[-1][1]=response.choices[0].message.content #.append([chat_history[-1][1], response.choices[0].message.content])
+    print(chat_history)
+            
+    token_counter = 0 
+    partial_words = "" 
+
+    counter=0
+    partial_message = "" 
+    print(chat_history)
+    return "", chat_history
+    # for chunk in response:
+    #     if len(chunk.choices[0].delta.content) != 0:
+    #         partial_message = partial_message + chunk.choices[0].delta.content
+    #         yield partial_message
+    # for chunk in response:
+    #     print(chunk)
+    #     print( "text",chunk.choices[0].delta.content)
+    #     chunk_message = chunk.choices[0].delta
+    #     if(len(chat_history))<1:
+    #         # print("entró acaá")
+    #         partial_words += chunk_message.content
+    #         chat_history.append([message,chunk_message.content])
+    #     else:
+    #         # print("antes", chat_history)
+    #         if(len(chunk_message.content)!=0):
+    #             if(len(chunk_message.content)==2):
+    #                 partial_words += chunk_message.content
+    #                 chat_history.append([message,chunk_message.content])
+    #             else:
+    #                 partial_words += chunk_message.content
+    #                 chat_history[-1] =([message,partial_words])
+    #     yield "",chat_history
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("""
+    <center>
+    <h1>
+    Uso de AI para un chatbot.
+    </h1>
+    <img src='data:image/jpg;base64,{}' width=200px>
+    <h3>
+    Con este espacio podrás hablar en formato conversación con el personaje famoso que quieras, puede ser Albert Einstein, Marie Curie o el/la que quieras!
+    </h3>
+    </center>
+    """.format(encoded_image))
+    with gr.Row():
+        person1 = gr.Textbox(label="Escribí el nombre del perosnaje famoso:")
+        person2 = gr.Textbox(label="Escribí el nombre del perosnaje famoso:")
+
+    with gr.Row():
+        chatbot = gr.Chatbot( height=550) #just to fit the notebook
+    with gr.Row():
+        with gr.Row():
+            with gr.Column(scale=4):
+                msg = gr.Textbox(label="Texto de entrada")
+            with gr.Column(scale=1):
+                btn = gr.Button("Enviar")
+                clear = gr.ClearButton(components=[msg, chatbot], value="Borrar chat")
+
+   
+
+
+    btn.click(respond, inputs=[person1,person2, chatbot], outputs=[msg, chatbot])
+    #msg.submit(respond, inputs=[msg, person,chatbot], outputs=[msg, chatbot]) #Press enter to submit
+    clear.click(clear_chat,inputs=[msg, chatbot], outputs=[msg, chatbot])
+demo.queue()
+demo.launch()

requirements.txt

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+einops
+openai
+gradio
+python-dotenv