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| # from huggingface_hub import InferenceClient | |
| import gradio as gr | |
| import os | |
| import requests | |
| api_url = "https://api-inference.huggingface.co/models/sf06583/gpt2-model-network-automation" | |
| access_token = os.getenv("HF_HOME") | |
| def chat(input_text): | |
| payload = {"inputs": input_text} | |
| headers = {"Authorization": f"Bearer {access_token}"} | |
| response = requests.post(api_url, json=payload, headers=headers) | |
| response_data = response.json() # Parse response as JSON | |
| response_text = "" | |
| # while True: | |
| # response = requests.post(api_url, json=payload, headers=headers) | |
| # print("response_json", response.json()) | |
| # response_text += response.json()[0].get("generated_text") # Parse response as JSON | |
| # print("response:", response) | |
| # print("response_text:", response_text) | |
| # # Check if the response contains "</s>" | |
| # if "</s>" in response_text: | |
| # print("Response contains '</s>'. Exiting loop.") | |
| # break | |
| # payload = {"inputs": response_text} | |
| # Extract text from "generated_text" key in each dictionary | |
| for item in response_data: | |
| generated_text = item.get("generated_text", "") | |
| response_text += generated_text | |
| print("Response Text:", response_text) # Check the response content | |
| index_of_inst = response_text.find("</inst>") | |
| index_of_end = response_text.find("</s>") | |
| print("Index of </inst>:", index_of_inst) # Check the index of </inst> | |
| print("Index of </s>:", index_of_end) # Check the index of </s> | |
| # Extract text between </inst> and </s> tags | |
| if index_of_inst != -1: | |
| text_between_tags = response_text[index_of_inst + len("</inst>"):index_of_end].strip() | |
| else: | |
| text_between_tags = "Error: Tags not found in response" | |
| text_between_tags_with_line_breaks = text_between_tags.replace("\\n", "\n") | |
| return text_between_tags_with_line_breaks | |
| iface = gr.Interface( | |
| fn=chat, | |
| inputs=gr.Textbox( | |
| label="Intent", | |
| info="Type your network intent here", | |
| lines=1, | |
| placeholder="I want to perform basic network configuration on my topology", | |
| ), | |
| outputs=gr.Textbox( | |
| label="Intent-based Network Commands", | |
| info="Your network commands will generate here", | |
| show_copy_button=True, | |
| ), | |
| title="CybHermes Network Automation Tool", | |
| theme='shivi/calm_seafoam' | |
| ) | |
| iface.launch(share=True) | |
| # # os.environ["HF_HOME"] = os.getenv("HF_HOME") | |
| # client = InferenceClient("sf06583/gpt2-model-network-automation") | |
| # #def format_prompt(message, history, system_prompt=None): | |
| # # prompt = "<s>" | |
| # # for user_prompt, bot_response in history: | |
| # # prompt += f"<INST> {user_prompt} </INST>" | |
| # # prompt += f" {bot_response}</s> " | |
| # # if system_prompt: | |
| # # prompt += f"[SYS] {system_prompt} [/SYS]" | |
| # # prompt += f"[INST] {message} [/INST]" | |
| # # return prompt | |
| # def format_prompt(message, history, system_prompt=None): | |
| # prompt = "<s>" | |
| # for user_prompt, bot_response in history: | |
| # inst_start_tag = "<inst>" | |
| # inst_end_tag = "</inst>" | |
| # inst_start_pos = user_prompt.find(inst_start_tag) | |
| # inst_end_pos = user_prompt.find(inst_end_tag) | |
| # if inst_start_pos != -1 and inst_end_pos != -1: | |
| # # Extract the intent and command between <inst> tags | |
| # intent = user_prompt[inst_start_pos + len(inst_start_tag):inst_end_pos] | |
| # command = user_prompt[inst_end_pos + len(inst_end_tag):] | |
| # # Format the intent and command into the prompt | |
| # prompt += f"[INST] {intent} [/INST]" | |
| # prompt += f" {command}" | |
| # else: | |
| # prompt += f"[INST] {user_prompt} [/INST]" | |
| # prompt += f" {bot_response}" | |
| # prompt += "</s>" | |
| # if system_prompt: | |
| # prompt += f"[SYS] {system_prompt} [/SYS]" | |
| # prompt += f"[INST] {message} [/INST]" | |
| # return prompt | |
| # #def generate( | |
| # # prompt, history, system_prompt=None, temperature=0.2, max_new_tokens=1024, top_p=0.95, repetition_penalty=1.0, | |
| # #): | |
| # # try: | |
| # # temperature = float(temperature) | |
| # # if temperature < 1e-2: | |
| # # temperature = 1e-2 | |
| # # top_p = float(top_p) | |
| # # generate_kwargs = dict( | |
| # # temperature=temperature, | |
| # # max_length=max_new_tokens, | |
| # # top_p=top_p, | |
| # # repetition_penalty=repetition_penalty, | |
| # # do_sample=True, | |
| # # pad_token_id=model.config.eos_token_id, | |
| # # ) | |
| # # formatted_prompt = format_prompt(prompt, history, system_prompt) | |
| # # ids = tokenizer.encode(formatted_prompt, return_tensors='pt') | |
| # # outputs = model.generate(ids, **generate_kwargs) | |
| # # generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # # return generated_text | |
| # # except Exception as e: | |
| # # print("An error occurred during text generation:", e) | |
| # # return "An error occurred during text generation. Please try again later." | |
| # def generate_text(prompt): | |
| # try: | |
| # # Call the text_generation method of the client to generate text | |
| # stream = client.text_generation(prompt, stream=True, details=True, return_full_text=False) | |
| # output = "" | |
| # for response in stream: | |
| # output += response.token.text | |
| # return output | |
| # except Exception as e: | |
| # print("An error occurred during text generation:", e) | |
| # return "An error occurred during text generation. Please try again later." | |
| # mychatbot = gr.Chatbot( | |
| # avatar_images=["./user.png", "./botm.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,) | |
| # demo = gr.ChatInterface( | |
| # fn=generate, | |
| # chatbot=mychatbot, | |
| # title="Adaab! I am CybHermes, what do you want to do on your network?", | |
| # css="body { background-color: inherit; overflow-x:hidden;}" | |
| # ":root {--color-accent: transparent !important; --color-accent-soft:transparent !important; --code-background-fill:black !important; --body-text-color:white !important;}" | |
| # "#component-2 {background:#ffffff1a; display:contents;}" | |
| # "div#component-0 { height: auto !important;}" | |
| # ".gradio-container.gradio-container-4-8-0.svelte-1kyws56.app {max-width: 100% !important;}" | |
| # "gradio-app {background: linear-gradient(134deg,#00425e 0%,#001a3f 43%,#421438 77%) !important; background-attachment: fixed !important; background-position: top;}" | |
| # ".panel.svelte-vt1mxs {background: transparent; padding:0;}" | |
| # ".block.svelte-90oupt { background: transparent; border-color: transparent;}" | |
| # ".bot.svelte-12dsd9j.svelte-12dsd9j.svelte-12dsd9j { background: #ffffff1a; border-color: transparent; color: black !important;}" | |
| # ".user.svelte-12dsd9j.svelte-12dsd9j.svelte-12dsd9j { background: #ffffff1a; border-color: transparent; color: white; padding: 10px 18px;}" | |
| # "div.svelte-iyf88w{ background: #cc98d445; border-color: transparent; border-radius: 25px;}" | |
| # "textarea.scroll-hide.svelte-1f354aw { background: transparent; color: black !important;}" | |
| # ".primary.svelte-cmf5ev { background: transparent; color: white;}" | |
| # ".primary.svelte-cmf5ev:hover { background: transparent; color: white;}" | |
| # "button#component-8 { display: none; position: absolute; margin-top: 60px; border-radius: 25px;}" | |
| # "div#component-9 { max-width: fit-content; margin-left: auto; margin-right: auto;}" | |
| # "button#component-10, button#component-11, button#component-12 { flex: none; background: #ffffff1a; border: none; color: white; margin-right: auto; margin-left: auto; border-radius: 9px; min-width: fit-content;}" | |
| # ".share-button.svelte-12dsd9j { display: none;}" | |
| # "footer.svelte-mpyp5e { display: none !important;}" | |
| # ".message-buttons-bubble.svelte-12dsd9j.svelte-12dsd9j.svelte-12dsd9j { border-color: #31546E; background: #31546E;}" | |
| # ".bubble-wrap.svelte-12dsd9j.svelte-12dsd9j.svelte-12dsd9j {padding: 0;}" | |
| # ".prose h1 { color: white !important; font-size: 16px !important; font-weight: normal !important; background: #ffffff1a; padding: 20px; border-radius: 20px; width: 90%; margin-left: auto !important; margin-right: auto !important;}" | |
| # ".toast-wrap.svelte-pu0yf1 { display:none !important;}" | |
| # ".scroll-hide { scrollbar-width: auto !important;}" | |
| # ".main svelte-1kyws56 {max-width: 800px; align-self: center;}" | |
| # "div#component-4 {max-width: 650px; margin-left: auto; margin-right: auto;}" | |
| # "body::-webkit-scrollbar { display: none;}" | |
| # ) | |
| # demo.queue().launch(show_api=False) |