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# Week - 3 Assignment - Integrate Traditional Chatbot with AI Service Project (Transformers) Praveen Kumar Parimi
#importing the required libraries including transformers
import gradio as gr
from huggingface_hub import InferenceClient
from transformers import pipeline
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
print("starting Praveen's smarter chatbot...")
"""
The transformer model used here is Microsoft-trained Phi-3.5-mini-instruct
"""
#model_name = "microsoft/Phi-3.5-mini-instruct"
model_name="meta-llama/Llama-2-7b-chat-hf"
chat_model = pipeline("text-generation", model=model_name)
print("defining the chat_response function")
def chat_response(
message,
history: list[tuple[str, str]],
system_message,
max_tokens
):
print("Inside chat_response progressing...")
messages = [{"role": "system", "content": system_message}]
print ("System Messages", messages)
messages.append({"role": "user", "content": message})
print ("Messages after adding user messages", messages)
response = chat_model(messages) #Passing system and user messages to the transformer model Phi-3.5-mini-instruct to get smarter responses
print("Response received from model",response)
return response[-1]['generated_text'][-1]['content']
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
chat_response,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
],
)
if __name__ == "__main__":
demo.launch()
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