import gradio as gr
from huggingface_hub import InferenceClient
import json
from bs4 import BeautifulSoup
import requests

# Futuristic, modern custom CSS for DeepConvoAI interface with a purple theme
css = '''
@import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@500;700&family=Roboto:wght@400;500&display=swap');

body {
    margin: 0;
    padding: 0;
    font-family: 'Roboto', sans-serif;
    background: #0d0d0d;
    color: #e0e0e0;
}

.gradio-container {
    max-width: 1000px !important;
    margin: 2rem auto;
    background: rgba(15, 15, 15, 0.95);
    border-radius: 15px;
    padding: 2rem;
    box-shadow: 0 15px 30px rgba(0,0,0,0.7);
    position: relative;
    overflow: hidden;
}

.gradio-container::before {
    content: "";
    position: absolute;
    top: -50%;
    left: -50%;
    width: 200%;
    height: 200%;
    background: linear-gradient(45deg, #8A2BE2, #4B0082, #8A2BE2);
    animation: gradientShift 6s linear infinite;
    opacity: 0.1;
}

@keyframes gradientShift {
    0% { transform: translate(0, 0) rotate(0deg); }
    50% { transform: translate(20%, 20%) rotate(180deg); }
    100% { transform: translate(0, 0) rotate(360deg); }
}

h1 {
    text-align: center;
    font-family: 'Orbitron', sans-serif;
    font-size: 2.5rem;
    font-weight: 700;
    color: #8A2BE2;
    margin-bottom: 0.5rem;
}

p.description {
    text-align: center;
    font-size: 1.1rem;
    margin-bottom: 1rem;
    color: #a0a0a0;
}

footer {
    display: none;
}


/* Textbox styling */
textarea {
    border: 2px solid #6A0DAD;
    background: #1a1a1a;
    border-radius: 10px;
    padding: 12px;
    font-size: 1rem;
    color: #e0e0e0;
}


'''

# Function to extract text from a webpage
def get_text_from_html(html_content):
    soup = BeautifulSoup(html_content, 'html.parser')
    for tag in soup(["script", "style", "header", "footer"]):
        tag.extract()
    return soup.get_text(strip=True)

# Function to perform a web search
def perform_search(query):
    search_term = query
    all_results = []
    max_chars_per_page = 8000
    with requests.Session() as session:
        response = session.get(
            url="https://www.google.com/search",
            headers={
                "User-Agent": ("Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
                               "AppleWebKit/537.36 (KHTML, like Gecko) "
                               "Chrome/111.0.0.0 Safari/537.36 Edg/111.0.0.0")
            },
            params={"q": search_term, "num": 3, "udm": 14},
            timeout=5,
            verify=False,
        )
        response.raise_for_status()
        soup = BeautifulSoup(response.text, "html.parser")
        result_block = soup.find_all("div", attrs={"class": "g"})
        for result in result_block:
            link = result.find("a", href=True)["href"]
            try:
                webpage_response = session.get(
                    link,
                    headers={
                        "User-Agent": ("Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
                                       "AppleWebKit/537.36 (KHTML, like Gecko) "
                                       "Chrome/111.0.0.0 Safari/537.0.0.0")
                    },
                    timeout=5,
                    verify=False
                )
                webpage_response.raise_for_status()
                visible_text = get_text_from_html(webpage_response.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
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")

# Function to handle responses
def chat_response(message, history):
    func_calls = []
    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"]
                }
            }
        }
    ]

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

    func_calls.append({
        "role": "user",
        "content": (f'[SYSTEM] You are a helpful assistant named DeepConvoAI with integrated search capabilities. '
                    f'You have access to the following functions:\n{functions_metadata}\n\n'
                    f'To use these functions respond with:\n'
                    f'<functioncall> {{ "name": "function_name", "arguments": {{ "arg_1": "value_1", ... }} }} </functioncall>\n'
                    f'[USER] {message}')
    })
    
    response = client_gemma.chat_completion(func_calls, max_tokens=200)
    response = str(response)
    try:
        response = response[response.find("{"):response.rindex("}")+1]
    except Exception:
        response = response[response.find("{"):response.rfind("}")+1]
    response = response.replace("\\n", "").replace("\\'", "'").replace('\\"', '"').replace('\\', '')
    print(f"\n{response}")
    
    try:
        json_data = json.loads(response)
        if json_data["name"] == "web_search":
            query = json_data["arguments"]["query"]
            gr.Info("Initiating web search...")
            web_results = perform_search(query)
            gr.Info("Extracting relevant information...")
            web_summary = ' '.join([
                f"Link: {res['link']}\nText: {res['text']}\n\n"
                for res in web_results if res['text']
            ])
            messages = "system\nDeepConvoAI uses advanced search capabilities for chat responses."
            for msg in history:
                messages += f"\nuser\n{msg[0]}"
                messages += f"\nassistant\n{msg[1]}"
            messages += f"\nuser\n{message}\nweb_result\n{web_summary}\nassistant\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 response.token.text != "":
                    output += response.token.text
                    yield output
        else:
            messages = "system\nDeepConvoAI uses advanced search capabilities for chat responses."
            for msg in history:
                messages += f"\nuser\n{msg[0]}"
                messages += f"\nassistant\n{msg[1]}"
            messages += f"\nuser\n{message}\nassistant\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 response.token.text != "":
                    output += response.token.text
                    yield output
    except Exception:
        messages = "system\nDeepConvoAI uses advanced search capabilities for chat responses."
        for msg in history:
            messages += f"\nuser\n{msg[0]}"
            messages += f"\nassistant\n{msg[1]}"
        messages += f"\nuser\n{message}\nassistant\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 response.token.text != "":
                output += response.token.text
                yield output
                
# Create Gradio interface with futuristic aesthetics and improved text readability
demo = gr.ChatInterface(
    fn=chat_response,
    chatbot=gr.Chatbot(),
    description="""
    <h1>DeepConvoAI by Abhijeet Sarkar</h1>
    <p class="description">
        Welcome to DeepConvoAI – your intelligent assistant powered by advanced AI and integrated web search.
        Explore the future of conversation and discovery.
    </p>
    """,
    textbox=gr.Textbox(placeholder="Ask anything here...", lines=2), 
    multimodal=False,
    concurrency_limit=200,
    css=css,
    theme="allenai/gradio-theme",
)

demo.launch(share=True)