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| import gradio as gr | |
| import requests | |
| import json | |
| API_URL = "https://api.openai.com/v1/chat/completions" | |
| def predict(inputs, top_p, temperature, openai_api_key, chat_counter, chatbot=[], history=[]): | |
| messages = format_messages(chatbot, inputs, chat_counter) | |
| payload = create_payload(messages, top_p, temperature) | |
| response = make_request(API_URL, openai_api_key, payload) | |
| # Processar a resposta | |
| chat, new_history, token_counter = process_response(response, history) | |
| # Atualizar o histórico apenas se houver novos tokens | |
| if token_counter > 0: | |
| history = new_history | |
| return chat, history, token_counter | |
| def format_messages(chatbot, inputs, chat_counter): | |
| messages = [] | |
| if chat_counter != 0: | |
| for i in range(len(chatbot)): | |
| user_message = {"role": "user", "content": chatbot[i][0]} | |
| assistant_message = {"role": "assistant", "content": chatbot[i][1]} | |
| messages.extend([user_message, assistant_message]) | |
| messages.append({"role": "user", "content": inputs}) | |
| return messages | |
| def create_payload(messages, top_p, temperature): | |
| return { | |
| "model": "gpt-4-1106-preview", | |
| "messages": messages, | |
| "temperature": temperature, | |
| "top_p": top_p, | |
| "n": 1, | |
| "stream": True, | |
| "presence_penalty": 0, | |
| "frequency_penalty": 0, | |
| } | |
| def make_request(url, api_key, payload): | |
| headers = { | |
| "Content-Type": "application/json", | |
| "Authorization": f"Bearer {api_key}" | |
| } | |
| response = requests.post(url, headers=headers, json=payload, stream=True) | |
| return response | |
| def process_response(response, history): | |
| token_counter = 0 | |
| partial_words = "" | |
| for chunk in response.iter_lines(): | |
| if chunk: | |
| chunk_str = chunk.decode('utf-8').lstrip('data: ') | |
| if "[DONE]" in chunk_str: | |
| break | |
| try: | |
| chunk_json = json.loads(chunk_str) | |
| if 'choices' in chunk_json and len(chunk_json['choices']) > 0: | |
| chunk_data = chunk_json['choices'][0].get('delta', {}) | |
| if 'content' in chunk_data: | |
| content = chunk_data['content'] | |
| partial_words += content | |
| if token_counter == 0: | |
| history.append(" " + partial_words) | |
| else: | |
| history[-1] = partial_words | |
| token_counter += 1 | |
| except json.JSONDecodeError as e: | |
| print("Error decoding JSON response:", e) | |
| print("Raw chunk:", chunk_str) | |
| chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2)] | |
| return chat, history, token_counter | |
| def setup_ui(): | |
| with gr.Blocks() as demo: | |
| with gr.Column(): | |
| openai_api_key = gr.Textbox(type='password', label="Insira sua chave de API OpenAI aqui") | |
| chatbot = gr.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") | |
| top_p = gr.Slider(minimum=0, maximum=1.0, value=1.0, step=0.05, label="Top-p") | |
| temperature = gr.Slider(minimum=0, maximum=1.0, value=1.0, step=0.05, label="Temperature") | |
| chat_counter = gr.Number(value=0, visible=False) | |
| 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]) | |
| return demo | |
| def main(): | |
| demo = setup_ui() | |
| demo.launch() | |
| if __name__ == "__main__": | |
| main() | |