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import gradio as gr
from transformers import pipeline
from huggingface_hub import InferenceClient
import requests
from bs4 import BeautifulSoup
# Initialize the text generation pipeline
pipe = pipeline("text-generation", model="microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def web_search(query):
# Simulate a web search using Google
response = requests.get(f"https://www.google.com/search?q={query}")
soup = BeautifulSoup(response.text, "html.parser")
results = []
for g in soup.find_all('div', class_='BNeawe vvjwJb AP7Wnd'):
results.append(g.get_text())
return results
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
# Check if message is a search request
if "search:" in message.lower():
search_query = message.split("search:", 1)[1].strip()
search_results = web_search(search_query)
response = "\n".join(search_results[:5]) # Return top 5 search results
else:
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
yield response
demo = gr.ChatInterface(
respond,
title="INDONESIAN CHATBOT"
additional_inputs=[
gr.Textbox(value="You are a friendly AI Assistens Speak in indonesian", label="System message", visible=False),
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()
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