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| import os | |
| from gpt_index import SimpleDirectoryReader, GPTSimpleVectorIndex, LLMPredictor, PromptHelper | |
| from langchain import OpenAI | |
| import gradio as gr | |
| import openai | |
| API_URL = "https://api.openai.com/v1/chat/completions" | |
| openai.api_key = os.environ["OPENAI_API_KEY"] | |
| top_p_chatgpt = 1.0 | |
| temperature_chatgpt = 1.0 | |
| def predict_chatgpt(inputs,chat_counter_chatgpt, chatbot_chatgpt=[], history=[]): | |
| if chat_counter_chatgpt != 0: | |
| messages = [] | |
| for data in chatbot_chatgpt: | |
| 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) | |
| #os.environ['OPENAI_API_KEY'] = openai.api_key | |
| chat_counter_chatgpt += 1 | |
| history.append("You typed: " + inputs) | |
| res = openai.Moderation.create(input=inputs) | |
| reply = res["results"][0].flagged | |
| if reply == True: | |
| result= "This content is offensive and needs to be moderated" | |
| else: | |
| result= "This content doesn't need moderation" | |
| response = result.split() | |
| token_counter = 0 | |
| partial_words = "" | |
| counter = 0 | |
| for chunk in response: | |
| partial_words=partial_words+" "+chunk | |
| 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_chatgpt # This resembles {chatbot: chat, state: history} | |
| def reset_textbox(): | |
| return gr.update(value="") | |
| def reset_chat(chatbot, state): | |
| return None, [] | |
| with gr.Blocks(css="""#col_container {width: 1000px; margin-left: auto; margin-right: auto;} | |
| #chatgpt {height: 400px; overflow: auto;}} """, theme=gr.themes.Default(primary_hue="slate") ) as ModerationAI: | |
| with gr.Row(): | |
| with gr.Column(scale=14): | |
| with gr.Box(): | |
| with gr.Row(): | |
| with gr.Column(scale=13): | |
| inputs = gr.Textbox(label="Type any abusive sentence ⤵️ Try : I will pi** on you" ) | |
| with gr.Column(scale=1): | |
| b1 = gr.Button('Submit', elem_id = 'submit').style(full_width=True) | |
| b2 = gr.Button('Clear', elem_id = 'clear').style(full_width=True) | |
| state_chatgpt = gr.State([]) | |
| with gr.Box(): | |
| with gr.Row(): | |
| chatbot_chatgpt = gr.Chatbot(elem_id="chatgpt", label="Moderation AI") | |
| chat_counter_chatgpt = gr.Number(value=0, visible=False, precision=0) | |
| inputs.submit(reset_textbox, [], [inputs]) | |
| b1.click( predict_chatgpt, | |
| [ inputs, chat_counter_chatgpt, chatbot_chatgpt, state_chatgpt], | |
| [chatbot_chatgpt, state_chatgpt],) | |
| b2.click(reset_chat, [chatbot_chatgpt, state_chatgpt], [chatbot_chatgpt, state_chatgpt]) | |
| ModerationAI.queue(concurrency_count=16).launch(height= 2500, debug=True) | |