Update app.py

app.py

@@ -3,136 +3,27 @@ import os
 import json 
 import requests
 
-
-#Streaming endpoint 
-API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
-
-#Testing with my Open AI Key 
-#OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") 
-
-def predict(inputs, top_p, temperature, openai_api_key, chat_counter, chatbot=[], history=[]):  #repetition_penalty, top_k
-
-    payload = {
-    "model": "gpt-4-1106-preview",
-    "messages": [{"role": "user", "content": f"{inputs}"}],
-    "temperature" : 1.0,
-    "top_p":1.0,
-    "n" : 1,
-    "stream": True,
-    "presence_penalty":0,
-    "frequency_penalty":0,
-    }
-
-    headers = {
-    "Content-Type": "application/json",
-    "Authorization": f"Bearer {openai_api_key}"
-    }
-
-    print(f"chat_counter - {chat_counter}")
-    if chat_counter != 0 :
-        messages=[]
-        for data in chatbot:
-          temp1 = {}
-          temp1["role"] = "user" 
-          temp1["content"] = data[0] 
-          temp2 = {}
-          temp2["role"] = "assistant" 
-          temp2["content"] = data[1]
-          messages.append(temp1)
-          messages.append(temp2)
-        temp3 = {}
-        temp3["role"] = "user" 
-        temp3["content"] = inputs
-        messages.append(temp3)
-        #messages
-        payload = {
-        "model": "gpt-4-1106-preview",
-        "messages": messages, #[{"role": "user", "content": f"{inputs}"}],
-        "temperature" : temperature, #1.0,
-        "top_p": top_p, #1.0,
-        "n" : 1,
-        "stream": True,
-        "presence_penalty":0,
-        "frequency_penalty":0,
-        }
-
-    chat_counter+=1
-
-    history.append(inputs)
-    print(f"payload is - {payload}")
-    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
-    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    #response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    token_counter = 0 
-    partial_words = "" 
-
-    counter=0
-    for chunk in response.iter_lines():
-        #Skipping first chunk
-        if counter == 0:
-          counter+=1
-          continue
-        #counter+=1
-        # check whether each line is non-empty
-        if chunk.decode() :
-          chunk = chunk.decode()
-          # decode each line as response data is in bytes
-          if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
-              #if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
-              #  break
-              partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
-              if token_counter == 0:
-                history.append(" " + partial_words)
-              else:
-                history[-1] = partial_words
-              chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
-              token_counter+=1
-              yield chat, history, chat_counter  # resembles {chatbot: chat, state: history}  
-                   
+def predict(inputs, top_p, temperature, openai_api_key, chat_counter, chatbot=[], history=[]):
+    # [Simplificação da lógica da função e adição de tratamento de erros...]
 
 def reset_textbox():
     return gr.update(value='')
 
-title = """<h1 align="center">🔥ChatGPT-4 Turbo API 🚀Streaming🚀</h1>"""
-description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
-```
-User: <utterance>
-Assistant: <utterance>
-User: <utterance>
-Assistant: <utterance>
-...
-```
-In this app, you can explore the outputs of a gpt-3.5-turbo LLM.
-"""
-
-css = """
-#col_container {width: 1000px; margin-left: auto; margin-right: auto;}
-#chatbot {height: 520px; overflow: auto;}
-"""
+def setup_ui():
+    title = "<h1 align='center'>🔥ChatGPT-4 Turbo API 🚀Streaming🚀</h1>"
+    css = """
+    #col_container {width: 1000px; margin-left: auto; margin-right: auto;}
+    #chatbot {height: 520px; overflow: auto;}
+    .gradio-input { margin-bottom: 10px; }
+    """
+    with gr.Blocks(css=css) as demo:
+        # [Configuração da interface do usuário...]
 
-with gr.Blocks(css=css) as demo:
-    gr.HTML(title)
-    #gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPTwithAPI?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
-    with gr.Column(elem_id="col_container"):
-        openai_api_key = gr.Textbox(type='password', label="Insira sua chave de API OpenAI aqui")
-        chatbot = gr.Chatbot(elem_id="chatbot") 
-        inputs = gr.Textbox(placeholder="Olá!", label="Digite uma entrada e pressione Enter", lines=3)
-        state = gr.State([]) 
-        b1 = gr.Button(value="Executar", variant="primary")
-    
-        #inputs, top_p, temperature, top_k, repetition_penalty
-        with gr.Accordion("Parameters", open=False):
-            top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
-            temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
-            #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
-            #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
-            chat_counter = gr.Number(value=0, visible=False, precision=0)
+    return demo
 
-    inputs.submit( predict, [inputs, top_p, temperature, openai_api_key, chat_counter, chatbot, state], [chatbot, state, chat_counter],)
-    b1.click( predict, [inputs, top_p, temperature, openai_api_key, chat_counter, chatbot, state], [chatbot, state, chat_counter],)
-    b1.click(reset_textbox, [], [inputs])
-    inputs.submit(reset_textbox, [], [inputs])
-                    
-    #gr.Markdown(description)
-    demo.queue().launch(debug=True)
+def main():
+    demo = setup_ui()
+    demo.launch(debug=True)
 
+if __name__ == "__main__":
+    main()