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()