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import os
import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
# Load your model and tokenizer
model_name = "Renjith95/renj-portfolio-finetuned-model" # Replace with your model name
auth_token = os.getenv("HF_TOKEN") # Get token from environment variable
tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=auth_token)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, use_auth_token=auth_token)
"""
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
"""
# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def respond(message, history, system_message, max_tokens, temperature, top_p):
messages = [{"role": "system", "content": system_message}]
for user_msg, assistant_msg in history:
messages.append({"role": "user", "content": user_msg})
messages.append({"role": "assistant", "content": assistant_msg})
messages.append({"role": "user", "content": message})
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
input_ids=inputs,
max_new_tokens=max_tokens,
use_cache=True,
temperature=temperature,
top_p=top_p,
)
response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
# Assuming your model's response is the last part after the user's message
response = response.split(message)[-1].strip()
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
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"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch(share = True)
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