app.py

import json 
import gradio as gr
import os
import requests

hf_token = os.getenv('HF_TOKEN')
api_url = os.getenv('API_URL') 
headers = {
    'Content-Type': 'application/json',
}

system_message = "\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."

This Space demonstrates model Llama-2-70b-chat-hf by Meta, running on Inference Endpoints using TGI. Read everything about it in our blog post” (with links to the model, Inference Endpoints, TGI, and the blog)

def predict(message, chatbot):

    title = "Llama2 70B Chatbot : Explore exclusively on HuggingFace"
    description = """This Space demonstrates model [Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) by Meta, running on Inference Endpoints using text-generation-inference. To have your own dedicated endpoint, you can [deploy it on Inference Endpoints](https://ui.endpoints.huggingface.co/). """ 
    
    print(f"Logging: message is - {message}")
    print(f"Logging: chatbot is - {chatbot}")

    input_prompt = f"[INST]<<SYS>>\n{system_message}\n<</SYS>>\n\n "
    for interaction in chatbot:
        input_prompt = input_prompt + interaction[0] + " [/INST] " + interaction[1] + " </s><s> [INST] "

    input_prompt = input_prompt + message + " [/INST] "

    print(f"Logging: input_prompt is - {input_prompt}")
    data = {
        "inputs": input_prompt,
        "parameters": {"max_new_tokens":256}
    }

    response = requests.post(api_url, headers=headers, data=json.dumps(data), auth=('hf', hf_token))

    print(f'Logging: API response is - {response.text}')
    response_json_object = json.loads(response.text)
    return response_json_object[0]['generated_text']


gr.ChatInterface(predict).queue().launch(debug= True)