from fastapi import FastAPI, HTTPException, Query
from fastapi.responses import JSONResponse
from fastapi import FastAPI, HTTPException
from fastapi.responses import StreamingResponse
from webscout import WEBS, transcriber, LLM, fastai
from stream import fastai_stream
from typing import Optional, List, Dict, Union
from fastapi.encoders import jsonable_encoder
from bs4 import BeautifulSoup
import requests
import urllib.parse
import asyncio
import aiohttp
import threading
import json
import os
import time
from huggingface_hub import HfApi
from huggingface_hub import InferenceClient
from PIL import Image
import io

app = FastAPI()

@app.get("/")
async def root():
    return {"message": "API documentation can be found at /docs"}

@app.get("/health")
async def health_check():
    return {"status": "OK"}

@app.get("/api/search")
async def search(
    q: str,
    max_results: int = 10,
    timelimit: Optional[str] = None,
    safesearch: str = "moderate",
    region: str = "wt-wt",
    backend: str = "api",
    proxy: Optional[str] = None  # Add proxy parameter here
):
    """Perform a text search."""
    try:
        with WEBS(proxy=proxy) as webs:  # Pass proxy to WEBS instance
            results = webs.text(
                keywords=q,
                region=region,
                safesearch=safesearch,
                timelimit=timelimit,
                backend=backend,
                max_results=max_results,
            )
            return JSONResponse(content=jsonable_encoder(results))
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during search: {e}")

@app.get("/api/images")
async def images(
    q: str,
    max_results: int = 10,
    safesearch: str = "moderate",
    region: str = "wt-wt",
    timelimit: Optional[str] = None,
    size: Optional[str] = None,
    color: Optional[str] = None,
    type_image: Optional[str] = None,
    layout: Optional[str] = None,
    license_image: Optional[str] = None,
    proxy: Optional[str] = None # Add proxy parameter here
):
    """Perform an image search."""
    try:
        with WEBS(proxy=proxy) as webs:  # Pass proxy to WEBS instance
            results = webs.images(
                keywords=q,
                region=region,
                safesearch=safesearch,
                timelimit=timelimit,
                size=size,
                color=color,
                type_image=type_image,
                layout=layout,
                license_image=license_image,
                max_results=max_results,
            )
            return JSONResponse(content=jsonable_encoder(results))
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during image search: {e}")

@app.get("/api/videos")
async def videos(
    q: str,
    max_results: int = 10,
    safesearch: str = "moderate",
    region: str = "wt-wt",
    timelimit: Optional[str] = None,
    resolution: Optional[str] = None,
    duration: Optional[str] = None,
    license_videos: Optional[str] = None,
    proxy: Optional[str] = None # Add proxy parameter here
):
    """Perform a video search."""
    try:
        with WEBS(proxy=proxy) as webs:  # Pass proxy to WEBS instance
            results = webs.videos(
                keywords=q,
                region=region,
                safesearch=safesearch,
                timelimit=timelimit,
                resolution=resolution,
                duration=duration,
                license_videos=license_videos,
                max_results=max_results,
            )
            return JSONResponse(content=jsonable_encoder(results))
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during video search: {e}")

@app.get("/api/news")
async def news(
    q: str,
    max_results: int = 10,
    safesearch: str = "moderate",
    region: str = "wt-wt",
    timelimit: Optional[str] = None,
    proxy: Optional[str] = None  # Add proxy parameter here
):
    """Perform a news search."""
    try:
        with WEBS(proxy=proxy) as webs:  # Pass proxy to WEBS instance
            results = webs.news(
                keywords=q,
                region=region,
                safesearch=safesearch,
                timelimit=timelimit,
                max_results=max_results
            )
            return JSONResponse(content=jsonable_encoder(results))
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during news search: {e}")

@app.get("/api/llm")
async def llm_chat(
    model: str,
    message: str,
    system_prompt: str = Query(None, description="Optional custom system prompt")
):
    """Interact with a specified large language model with an optional system prompt."""
    try:
        messages = [{"role": "user", "content": message}]
        if system_prompt:
            messages.insert(0, {"role": "system", "content": system_prompt})  # Add system message at the beginning

        llm = LLM(model=model)
        response = llm.chat(messages=messages)
        return JSONResponse(content={"response": response})
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during LLM chat: {e}")

@app.get("/api/fastAI")
async def fast_ai(user: str, model: str = "llama3-70b", system: str = "Answer as concisely as possible."):
    """Get a response from the Snova AI service."""
    try:
        response = await asyncio.to_thread(fastai, user, model, system)
        return JSONResponse(content={"response": response})
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during Snova AI request: {e}")

@app.get("/api/streaming-fastAI")
async def fast_ai(user: str, model: str = "llama3-8b", system: str = "Answer as concisely as possible."):
    """Get a streaming response from the Snova AI service."""
    try:
        return StreamingResponse(fastai_stream(user, model, system), media_type="text/event-stream")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during Snova AI request: {e}")

@app.get("/api/answers")
async def answers(q: str, proxy: Optional[str] = None):
    """Get instant answers for a query."""
    try:
        with WEBS(proxy=proxy) as webs:
            results = webs.answers(keywords=q)
            return JSONResponse(content=jsonable_encoder(results))
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error getting instant answers: {e}")

@app.get("/api/chat")
async def chat(
    q: str,
    model: str = "gpt-4o-mini",
    proxy: Optional[str] = None
):
    """Perform a text search."""
    try:
        with WEBS(proxy=proxy) as webs:
            results = webs.chat(keywords=q, model=model)
            return JSONResponse(content=jsonable_encoder(results))
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error getting chat results: {e}")

def extract_text_from_webpage(html_content):
    """Extracts visible text from HTML content using BeautifulSoup."""
    soup = BeautifulSoup(html_content, "html.parser")
    # Remove unwanted tags
    for tag in soup(["script", "style", "header", "footer", "nav"]):
        tag.extract()
    # Get the remaining visible text
    visible_text = soup.get_text(strip=True)
    return visible_text

async def fetch_and_extract(url, max_chars, proxy: Optional[str] = None):
    """Fetches a URL and extracts text asynchronously."""

    async with aiohttp.ClientSession() as session:
        try:
            async with session.get(url, headers={"User-Agent": "Mozilla/5.0"}, proxy=proxy) as response:
                response.raise_for_status()
                html_content = await response.text()
                visible_text = extract_text_from_webpage(html_content)
                if len(visible_text) > max_chars:
                    visible_text = visible_text[:max_chars] + "..."
                return {"link": url, "text": visible_text}
        except (aiohttp.ClientError, requests.exceptions.RequestException) as e:
            print(f"Error fetching or processing {url}: {e}")
            return {"link": url, "text": None}

@app.get("/api/web_extract")
async def web_extract(
    url: str,
    max_chars: int = 12000,  # Adjust based on token limit
    proxy: Optional[str] = None
):
    """Extracts text from a given URL."""
    try:
        result = await fetch_and_extract(url, max_chars, proxy)
        return {"url": url, "text": result["text"]}
    except requests.exceptions.RequestException as e:
        raise HTTPException(status_code=500, detail=f"Error fetching or processing URL: {e}")

@app.get("/api/search-and-extract")
async def web_search_and_extract(
    q: str,
    max_results: int = 3,
    timelimit: Optional[str] = None,
    safesearch: str = "moderate",
    region: str = "wt-wt",
    backend: str = "html",
    max_chars: int = 6000,
    extract_only: bool = True,
    proxy: Optional[str] = None
):
    """
    Searches using WEBS, extracts text from the top results, and returns both.
    """
    try:
        with WEBS(proxy=proxy) as webs:
            # Perform WEBS search
            search_results = webs.text(keywords=q, region=region, safesearch=safesearch,
                                     timelimit=timelimit, backend=backend, max_results=max_results)

            # Extract text from each result's link asynchronously
            tasks = [fetch_and_extract(result['href'], max_chars, proxy) for result in search_results if 'href' in result]
            extracted_results = await asyncio.gather(*tasks)

            if extract_only:
                return JSONResponse(content=jsonable_encoder(extracted_results))
            else:
                return JSONResponse(content=jsonable_encoder({"search_results": search_results, "extracted_results": extracted_results}))
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during search and extraction: {e}")

def extract_text_from_webpage2(html_content):
    """Extracts visible text from HTML content using BeautifulSoup."""
    soup = BeautifulSoup(html_content, "html.parser")
    # Remove unwanted tags
    for tag in soup(["script", "style", "header", "footer", "nav"]):
        tag.extract()
    # Get the remaining visible text
    visible_text = soup.get_text(strip=True)
    return visible_text

def fetch_and_extract2(url, max_chars, proxy: Optional[str] = None):
    """Fetches a URL and extracts text using threading."""
    proxies = {'http': proxy, 'https': proxy} if proxy else None
    try:
        response = requests.get(url, headers={"User-Agent": "Mozilla/5.0"}, proxies=proxies)
        response.raise_for_status()
        html_content = response.text
        visible_text = extract_text_from_webpage2(html_content)
        if len(visible_text) > max_chars:
            visible_text = visible_text[:max_chars] + "..."
        return {"link": url, "text": visible_text}
    except (requests.exceptions.RequestException) as e:
        print(f"Error fetching or processing {url}: {e}")
        return {"link": url, "text": None}

@app.get("/api/websearch-and-extract-threading")
def web_search_and_extract_threading(
    q: str,
    max_results: int = 3,
    timelimit: Optional[str] = None,
    safesearch: str = "moderate",
    region: str = "wt-wt",
    backend: str = "html",
    max_chars: int = 6000,
    extract_only: bool = True,
    proxy: Optional[str] = None
):
    """
    Searches using WEBS, extracts text from the top results using threading, and returns both.
    """
    try:
        with WEBS(proxy=proxy) as webs:
            # Perform WEBS search
            search_results = webs.text(keywords=q, region=region, safesearch=safesearch,
                                     timelimit=timelimit, backend=backend, max_results=max_results)

            # Extract text from each result's link using threading
            extracted_results = []
            threads = []
            for result in search_results:
                if 'href' in result:
                    thread = threading.Thread(target=lambda: extracted_results.append(fetch_and_extract2(result['href'], max_chars, proxy)))
                    threads.append(thread)
                    thread.start()

            # Wait for all threads to finish
            for thread in threads:
                thread.join()

            if extract_only:
                return JSONResponse(content=jsonable_encoder(extracted_results))
            else:
                return JSONResponse(content=jsonable_encoder({"search_results": search_results, "extracted_results": extracted_results}))
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during search and extraction: {e}")

@app.get("/api/adv_web_search")
async def adv_web_search(
    q: str,
    model: str = "llama3-8b",
    max_results: int = 5,
    timelimit: Optional[str] = None,
    safesearch: str = "moderate",
    region: str = "wt-wt",
    backend: str = "html",
    max_chars: int = 6000,
    system_prompt: str = "You are an advanced AI chatbot. Provide the best answer to the user based on Google search results.",
    proxy: Optional[str] = None
):
    """
    Combines web search, web extraction, and FastAI chat for advanced search.
    """
    try:
        with WEBS(proxy=proxy) as webs:
            # 1. Perform the web search
            search_results = webs.text(keywords=q, region=region,
                                     safesearch=safesearch,
                                     timelimit=timelimit, backend=backend,
                                     max_results=max_results)

            # 2. Extract text from top search result URLs asynchronously
            extracted_text = ""
            tasks = [fetch_and_extract(result['href'], max_chars, proxy) for result in search_results if 'href' in result]
            extracted_results = await asyncio.gather(*tasks)
            for result in extracted_results:
                if result['text']:
                    extracted_text += f"## Content from: {result['link']}\n\n{result['text']}\n\n"

        # 3. Construct the prompt for FastAI
        ai_prompt = f"Query by user: {q} . Answer the query asked by user in detail. SEarch Result: {extracted_text}"

        # 4. Get the FastAI's response using FastAI service
        try:
            response = await asyncio.to_thread(FastAI(model=model, system_prompt=system_prompt).get_response, ai_prompt)
        except Exception as e:
            raise HTTPException(status_code=500, detail=f"Error during FastAI request: {e}")

        # 5. Return the results
        return JSONResponse(content={"response": response})

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during advanced search: {e}")

@app.get("/api/website_summarizer")
async def website_summarizer(url: str, proxy: Optional[str] = None):
    """Summarizes the content of a given URL using a chat model."""
    try:
        # Extract text from the given URL
        proxies = {'http': proxy, 'https': proxy} if proxy else None
        response = requests.get(url, headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0"}, proxies=proxies)
        response.raise_for_status()
        visible_text = extract_text_from_webpage(response.text)
        if len(visible_text) > 7500:  # Adjust max_chars based on your needs
            visible_text = visible_text[:7500] + "..."

        # Use chat model to summarize the extracted text
        with WEBS(proxy=proxy) as webs:
            summary_prompt = f"Summarize this in detail in Paragraph: {visible_text}"
            summary_result = webs.chat(keywords=summary_prompt, model="gpt-4o-mini")

        # Return the summary result
        return JSONResponse(content=jsonable_encoder({summary_result}))

    except requests.exceptions.RequestException as e:
        raise HTTPException(status_code=500, detail=f"Error fetching or processing URL: {e}")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during summarization: {e}")

@app.get("/api/ask_website")
async def ask_website(url: str, question: str, model: str = "llama-3-70b", proxy: Optional[str] = None):
    """
    Asks a question about the content of a given website.
    """
    try:
        # Extract text from the given URL
        proxies = {'http': proxy, 'https': proxy} if proxy else None
        response = requests.get(url, headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0"}, proxies=proxies)
        response.raise_for_status()
        visible_text = extract_text_from_webpage(response.text)
        if len(visible_text) > 7500:  # Adjust max_chars based on your needs
            visible_text = visible_text[:7500] + "..."

        # Construct a prompt for the chat model
        prompt = f"Based on the following text, answer this question in Paragraph: [QUESTION] {question} [TEXT] {visible_text}"

        # Use chat model to get the answer
        with WEBS(proxy=proxy) as webs:
            answer_result = webs.chat(keywords=prompt, model=model)

        # Return the answer result
        return JSONResponse(content=jsonable_encoder({answer_result}))

    except requests.exceptions.RequestException as e:
        raise HTTPException(status_code=500, detail=f"Error fetching or processing URL: {e}")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during question answering: {e}")

client_sd3 = InferenceClient("stabilityai/stable-diffusion-3-medium-diffusers")

@app.get("/api/sd3")
def sd3(prompt :str = "",
        steps: int = 20,
        width: int = 1000,
        height: int = 1000
       ):
    try:
        image = client_sd3.text_to_image(prompt = f"{prompt}, hd, high quality, 4k, masterpiece",
                num_inference_steps = steps,
                width = width, height = height )
        image = Image.open(io.BytesIO(image))
        return image
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during image generation: {e}")

@app.get("/api/maps")
async def maps(
    q: str,
    place: Optional[str] = None,
    street: Optional[str] = None,
    city: Optional[str] = None,
    county: Optional[str] = None,
    state: Optional[str] = None,
    country: Optional[str] = None,
    postalcode: Optional[str] = None,
    latitude: Optional[str] = None,
    longitude: Optional[str] = None,
    radius: int = 0,
    max_results: int = 10,
    proxy: Optional[str] = None
):
    """Perform a maps search."""
    try:
        with WEBS(proxy=proxy) as webs:
            results = webs.maps(keywords=q, place=place, street=street, city=city, county=county, state=state, country=country, postalcode=postalcode, latitude=latitude, longitude=longitude, radius=radius, max_results=max_results)
            return JSONResponse(content=jsonable_encoder(results))
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during maps search: {e}")

@app.get("/api/translate")
async def translate(
    q: str,
    from_: Optional[str] = None,
    to: str = "en",
    proxy: Optional[str] = None
):
    """Translate text."""
    try:
        with WEBS(proxy=proxy) as webs:
            results = webs.translate(keywords=q, from_=from_, to=to)
            return JSONResponse(content=jsonable_encoder(results))
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during translation: {e}")

from easygoogletranslate import EasyGoogleTranslate

@app.get("/api/google_translate")
def google_translate(q: str, from_: Optional[str] = 'auto', to: str = "en"):
    try:
        translator = EasyGoogleTranslate(
    source_language=from_,
    target_language=to,
    timeout=10
)
        result = translator.translate(q)
        return JSONResponse(content=jsonable_encoder({"detected_language": from_ , "original": q , "translated": result}))
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error during translation: {e}")

@app.get("/api/youtube/transcript")
async def youtube_transcript(
    video_id: str,
    languages: str = "en",
    preserve_formatting: bool = False,
    proxy: Optional[str] = None  # Add proxy parameter
):
    """Get the transcript of a YouTube video."""
    try:
        languages_list = languages.split(",")
        transcript = transcriber.get_transcript(video_id, languages=languages_list, preserve_formatting=preserve_formatting, proxies=proxy)
        return JSONResponse(content=jsonable_encoder(transcript))
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error getting YouTube transcript: {e}")

import requests
@app.get("/weather/json/{location}")
def get_weather_json(location: str):
    url = f"https://wttr.in/{location}?format=j1"
    response = requests.get(url)
    if response.status_code == 200:
        return response.json()
    else:
        return {"error": f"Unable to fetch weather data. Status code: {response.status_code}"}

@app.get("/weather/ascii/{location}")
def get_ascii_weather(location: str):
    url = f"https://wttr.in/{location}"
    response = requests.get(url, headers={'User-Agent': 'curl'})
    if response.status_code == 200:
        return response.text
    else:
        return {"error": f"Unable to fetch weather data. Status code: {response.status_code}"}

# Run the API server if this script is executed
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8083)

def main():
    # Retrieve the space ID and token from environment variables
    space_id = os.getenv("SPACE_ID")
    token = os.getenv("HF_TOKEN")

    # Initialize the HfApi with the retrieved token
    api = HfApi(token=token)

    while True:
        try:
            # Restart the space
            api.restart_space(space_id, factory_reboot=False)
            print(f"Successfully restarted the space: {space_id}")
        except Exception as e:
            print(f"Error restarting the space: {e}")

        # Wait for 10 minutes before restarting again
        time.sleep(600)  # Sleep for 600 seconds (10 minutes)

if __name__ == "__main__":
    main()