import gradio as gr
from huggingface_hub import InferenceClient
import json
import uuid
from PIL import Image
from bs4 import BeautifulSoup
import requests
import random
from transformers import LlavaProcessor, LlavaForConditionalGeneration, TextIteratorStreamer
from threading import Thread
import re
import time 
import torch
import cv2
from gradio_client import Client, file

def image_gen(prompt):
    client = Client("KingNish/Image-Gen-Pro")
    return client.predict("Image Generation",None, prompt, api_name="/image_gen_pro")

model_id = "llava-hf/llava-interleave-qwen-0.5b-hf"

processor = LlavaProcessor.from_pretrained(model_id)

model = LlavaForConditionalGeneration.from_pretrained(model_id)
model.to("cpu")


def llava(message, history):
    if message["files"]:
        image = message["files"][0]    
    else:
        for hist in history:
            if type(hist[0])==tuple:
                image = hist[0][0]
        
    txt = message["text"]
        
    gr.Info("Analyzing image")
    image = Image.open(image).convert("RGB")
    prompt = f"<|im_start|>user <image>\n{txt}<|im_end|><|im_start|>assistant"
    
    inputs = processor(prompt, image, return_tensors="pt")
    return inputs

def extract_text_from_webpage(html_content):
    soup = BeautifulSoup(html_content, 'html.parser')
    for tag in soup(["script", "style", "header", "footer"]):
        tag.extract()
    return soup.get_text(strip=True)

def search(query):
    term = query
    start = 0
    all_results = []
    max_chars_per_page = 8000
    with requests.Session() as session:
        resp = session.get(
            url="https://www.google.com/search",
            headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0"},
            params={"q": term, "num": 3, "udm": 14},
            timeout=5,
            verify=None,
        )
        resp.raise_for_status()
        soup = BeautifulSoup(resp.text, "html.parser")
        result_block = soup.find_all("div", attrs={"class": "g"})
        for result in result_block:
            link = result.find("a", href=True)
            link = link["href"]
            try:
                webpage = session.get(link, headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0"}, timeout=5, verify=False)
                webpage.raise_for_status()
                visible_text = extract_text_from_webpage(webpage.text)
                if len(visible_text) > max_chars_per_page:
                    visible_text = visible_text[:max_chars_per_page]
                all_results.append({"link": link, "text": visible_text})
            except requests.exceptions.RequestException:
                all_results.append({"link": link, "text": None})
    return all_results

# Initialize inference clients for different models
client_gemma = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
client_mixtral = InferenceClient("NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO")
client_llama = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
client_yi = InferenceClient("01-ai/Yi-1.5-34B-Chat")

# Define the main chat function
def respond(message, history):
    func_caller = []

    user_prompt = message
    # Handle image processing
    if message["files"]:
        inputs = llava(message, history)
        streamer = TextIteratorStreamer(processor, skip_prompt=True, **{"skip_special_tokens": True})
        generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)

        thread = Thread(target=model.generate, kwargs=generation_kwargs)
        thread.start()
    
        buffer = ""
        for new_text in streamer:
            buffer += new_text
            yield buffer
    else:
        functions_metadata = [
            {"type": "function", "function": {"name": "web_search", "description": "Search query on google", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "web search query"}}, "required": ["query"]}}},
            {"type": "function", "function": {"name": "general_query", "description": "Reply general query of USER", "parameters": {"type": "object", "properties": {"prompt": {"type": "string", "description": "A detailed prompt"}}, "required": ["prompt"]}}},
            {"type": "function", "function": {"name": "image_generation", "description": "Generate image for user", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "image generation prompt"}}, "required": ["query"]}}},
            {"type": "function", "function": {"name": "image_qna", "description": "Answer question asked by user related to image", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "Question by user"}}, "required": ["query"]}}},
        ]

        for msg in history:
            func_caller.append({"role": "user", "content": f"{str(msg[0])}"})
            func_caller.append({"role": "assistant", "content": f"{str(msg[1])}"})

        message_text = message["text"]
        func_caller.append({"role": "user", "content": f'[SYSTEM]You are a helpful assistant. You have access to the following functions: \n {str(functions_metadata)}\n\nTo use these functions respond with:\n<functioncall> {{ "name": "function_name", "arguments": {{ "arg_1": "value_1", "arg_1": "value_1", ... }} }}  </functioncall>  [USER] {message_text}'})
    
        response = client_gemma.chat_completion(func_caller, max_tokens=200)
        response = str(response)
        try:
            response = response[int(response.find("{")):int(response.rindex("</"))]
        except:
            response = response[int(response.find("{")):(int(response.rfind("}"))+1)]
        response = response.replace("\\n", "")
        response = response.replace("\\'", "'")
        response = response.replace('\\"', '"')
        response = response.replace('\\', '')
        print(f"\n{response}")
    
        try:
            json_data = json.loads(str(response))
            if json_data["name"] == "web_search":
                query = json_data["arguments"]["query"]
               # gr.Info("Searching Web")
                web_results = search(query)
               # gr.Info("Extracting relevant Info")
                web2 = ' '.join([f"Link: {res['link']}\nText: {res['text']}\n\n" for res in web_results])
                messages = f"<|im_start|>system\n Hi 👋, I am Nora,mini a helpful assistant.Ask me! I will do my best!! <|im_end|>"
                for msg in history:
                    messages += f"\n<|im_start|>user\n{str(msg[0])}<|im_end|>"
                    messages += f"\n<|im_start|>assistant\n{str(msg[1])}<|im_end|>"
                messages+=f"\n<|im_start|>user\n{message_text}<|im_end|>\n<|im_start|>web_result\n{web2}<|im_end|>\n<|im_start|>assistant\n"
                stream = client_mixtral.text_generation(messages, max_new_tokens=2000, do_sample=True, stream=True, details=True, return_full_text=False)
                output = ""
                for response in stream:
                    if not response.token.text == "#":
                        output += response.token.text
                        yield output
            elif json_data["name"] == "image_generation":
                query = json_data["arguments"]["query"]
                gr.Info("Generating Image, Please wait 10 sec...")
                yield "Generating Image, Please wait 10 sec..."
                try:
                    image = image_gen(f"{str(query)}")
                    yield gr.Image(image[1])
                except:
                    client_sd3 = InferenceClient("stabilityai/stable-diffusion-3-medium-diffusers")
                    seed = random.randint(0,999999)
                    image = client_sd3.text_to_image(query, negative_prompt=f"{seed}")
                    yield gr.Image(image)
            elif json_data["name"] == "image_qna":
                inputs = llava(message, history)
                streamer = TextIteratorStreamer(processor, skip_prompt=True, **{"skip_special_tokens": True})
                generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)

                thread = Thread(target=model.generate, kwargs=generation_kwargs)
                thread.start()
    
                buffer = ""
                for new_text in streamer:
                    buffer += new_text
                    yield buffer
            else:
                messages = f"<|im_start|>system\n 👋, I am Nora,mini a helpful assistant.Ask me! I will do my best!!<|im_end|>"
                for msg in history:
                    messages += f"\n<|im_start|>user\n{str(msg[0])}<|im_end|>"
                    messages += f"\n<|im_start|>assistant\n{str(msg[1])}<|im_end|>"
                messages+=f"\n<|im_start|>user\n{message_text}<|im_end|>\n<|im_start|>assistant\n"
                stream = client_yi.text_generation(messages, max_new_tokens=2000, do_sample=True, stream=True, details=True, return_full_text=False)
                output = ""
                for response in stream:
                    if not response.token.text == "<|endoftext|>":
                        output += response.token.text
                        yield output
        except:
            messages = f"<|start_header_id|>system\nHi 👋, I am Nora,mini a helpful assistant.Ask me! I will do my best!!<|end_header_id|>"
            for msg in history:
                messages += f"\n<|start_header_id|>user\n{str(msg[0])}<|end_header_id|>"
                messages += f"\n<|start_header_id|>assistant\n{str(msg[1])}<|end_header_id|>"
            messages+=f"\n<|start_header_id|>user\n{message_text}<|end_header_id|>\n<|start_header_id|>assistant\n"
            stream = client_llama.text_generation(messages, max_new_tokens=2000, do_sample=True, stream=True, details=True, return_full_text=False)
            output = ""
            for response in stream:
                if not response.token.text == "<|eot_id|>":
                    output += response.token.text
                    yield output

# Create the Gradio interface
demo = gr.ChatInterface(
    fn=respond,
    chatbot=gr.Chatbot(layout="panel"),
    textbox=gr.MultimodalTextbox(),
    multimodal=True,
    concurrency_limit=200,
    cache_examples=False,css="footer{display:none !important}"
)
demo.launch()