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import gradio as gr
import requests
import importlib
import pytz
from datetime import datetime
from bs4 import BeautifulSoup
from huggingface_hub import InferenceClient
# Import weather script
weather = importlib.import_module("weather")
# Hugging Face model
client = InferenceClient("Futuresony/future_ai_12_10_2024.gguf")
def google_search(query):
"""Scrape Google search for an answer."""
url = f"https://www.google.com/search?q={query}"
headers = {"User-Agent": "Mozilla/5.0"}
try:
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.text, "html.parser")
result = soup.find("div", class_="BNeawe iBp4i AP7Wnd")
if result:
return result.text
return "Sorry, I couldn't find an answer."
except Exception:
return "I'm unable to fetch data from Google right now."
def get_time_in_city(city):
"""Fetch current time for any city using pytz"""
try:
timezone = pytz.timezone(pytz.country_timezones['US'][0]) if city.lower() == "new york" else pytz.utc
now = datetime.now(timezone)
return f"The current time in {city} is {now.strftime('%H:%M:%S')}."
except Exception:
return "I couldn't fetch the time for that city."
def get_current_date():
"""Return today's date"""
return f"Today's date is {datetime.today().strftime('%d %B %Y')}."
def respond(message, history, system_message, max_tokens, temperature, top_p):
"""Chatbot that answers user and fetches real-time info if needed."""
message_lower = message.lower()
# Time-related questions
if "what time" in message_lower or "saa ngapi" in message_lower:
city = message.split()[-1] # Assume last word is city name
return get_time_in_city(city)
# Date-related questions
if "what date" in message_lower or "leo ni tarehe ngapi" in message_lower:
return get_current_date()
# Weather-related questions
if "weather" in message_lower or "hali ya hewa" in message_lower:
city = message.split()[-1]
return weather.get_weather(city)
# General knowledge questions → Try Google if model fails
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]})
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
# If model doesn't know, use Google
if "I don't know" in response or response.strip() == "":
response = google_search(message)
return response
# Gradio UI
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"),
],
)
if __name__ == "__main__":
demo.launch()
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