| import gradio as gr | |
| from model import llm | |
| def generate_response(message: str, history) -> str: | |
| return llm.create_chat_completion( | |
| messages=[ | |
| { | |
| "role": "system", | |
| "content": "You are a top-rated customer service agent named John. Be polite to customers and answer all their questions. If the question is out of context and not related to your job as a customer service agent, let the customer know that you can not help and they should look elsewhere for answers." | |
| }, | |
| { | |
| "role": "user", | |
| "content": message | |
| } | |
| ] | |
| )['choices'][0]['message']['content'] | |
| demo = gr.ChatInterface( | |
| fn=generate_response, | |
| examples=[ | |
| "What Payment Modalities are accepted?", | |
| "Can you help me cancel an order?", | |
| "What is your name and how can you help me today?" | |
| ], | |
| title="Customer Support", | |
| description="""This is the further fine tuned version of meta-llama/Llama-3.2-1B-Instruct. | |
| Fine tuned on the https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset dataset. Random seed of 65 was used to select 1k rows from the dataset, find that version at https://huggingface.co/datasets/Victorano/customer-support-1k, all on huggingface. | |
| You can find the full source code at (https://github.com/Victoran0/ECommerce-customer-support-chatbot).""", | |
| theme="HaleyCH/HaleyCH_Theme" | |
| ) | |
| demo.launch() | |