from sentence_transformers import SentenceTransformer
import gradio as gr
import os
import json
from bs4 import BeautifulSoup
import requests
from huggingface_hub import InferenceClient

from langchain.vectorstores import Chroma
# Required imports
from sentence_transformers import SentenceTransformer
from langchain.embeddings import HuggingFaceEmbeddings  # Use Hugging Face wrapper for SentenceTransformers
from langchain.document_loaders import DirectoryLoader, TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.schema import Document
from langchain.vectorstores import Chroma
import numpy as np
from sklearn.manifold import TSNE
import plotly.graph_objects as go

from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.schema import Document
import chromadb.utils.embedding_functions as embedding_functions
from langchain_community.embeddings import HuggingFaceEmbeddings

hf_token = os.getenv('HF_TOKEN')
huggingface_ef = embedding_functions.HuggingFaceEmbeddingFunction(
    api_key=hf_token,
    model_name="sentence-transformers/all-MiniLM-L6-v2"
)

#get their investor type
investor_type_value = ""

# Function to set the investor type
def set_investor_type(investor_type):
    global investor_type_value
    investor_type_value = investor_type
    return f"Investor type set to: {investor_type}"



embedding_model = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
# Define global variables
BOT_AVATAR = 'https://automatedstockmining.org/wp-content/uploads/2024/08/south-west-value-mining-logo.webp'



#for the search vector database

# Initialize Chroma vector store directory
db_name2 = "search_checkvector_db"

# Read in the text for processing
health_check_text = ''
with open('search_requirements.txt', 'r', encoding='utf-8') as search_text:
    search_requirements_text = search_text.read()

# Split text into chunks
search_splitter = CharacterTextSplitter(chunk_size=20, chunk_overlap=2)
parts = search_splitter.split_text(search_requirements_text)


search_documents = [Document(page_content=chunk) for chunk in parts]

# Initialize Chroma with documents and embeddings
search_vectorstore = Chroma.from_documents(
    documents=search_documents,
    embedding=embedding_model,
    persist_directory=db_name2
)






# Initialize Chroma vector store directory
db_name = "health_checkvector_db"

# Read in the text for processing
health_check_text = ''
with open('healthcheck.txt', 'r', encoding='utf-8') as file:
    health_check_text = file.read()

# Split text into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = text_splitter.split_text(health_check_text)

# Convert chunks into Document objects
documents = [Document(page_content=chunk) for chunk in chunks]

# Initialize Chroma with documents and embeddings
vectorstore = Chroma.from_documents(
    documents=documents,
    embedding=embedding_model,
    persist_directory=db_name
)



client = InferenceClient(token=hf_token)

custom_css = '''
.gradio-container {
    font-family: 'Roboto', sans-serif;
}
.main-header {
    text-align: center;
    color: #4a4a4a;
    margin-bottom: 2rem;
}
.tab-header {
    font-size: 1.2rem;
    font-weight: bold;
    margin-bottom: 1rem;
}
.custom-chatbot {
    border-radius: 10px;
    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
}
.custom-button {
    background-color: #3498db;
    color: white;
    border: none;
    padding: 10px 20px;
    border-radius: 5px;
    cursor: pointer;
    transition: background-color 0.3s ease;
}
.custom-button:hover {
    background-color: #2980b9;
}
'''

def extract_text_from_webpage(html):
    soup = BeautifulSoup(html, "html.parser")
    for script in soup(["script", "style"]):
        script.decompose()
    visible_text = soup.get_text(separator=" ", strip=True)
    return visible_text

def search(query):
    term = query
    max_chars_per_page = 8000
    all_results = []

    with requests.Session() as session:
        try:
            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": 7},
                timeout=5
            )
            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)
                if link:
                    link = link["href"]
                    try:
                        webpage = session.get(link, headers={"User-Agent": "Mozilla/5.0"}, timeout=5)
                        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 as e:
                        print(f"Failed to retrieve {link}: {e}")
                        all_results.append({"link": link, "text": None})
        except requests.exceptions.RequestException as e:
            print(f"Google search failed: {e}")

    return all_results

def process_query(user_input, history):
    yield 'locating vectorstore 🛠️'
    docs = vectorstore.similarity_search(user_input, k=5)

    # Retrieve and concatenate results
    retrieved_texts = " ".join([doc.page_content for doc in docs])


    #similarity search on searches
    searches = search_vectorstore.similarity_search(user_input, k=3)

    # Retrieve and concatenate results
    search_texts = " ".join([doc.page_content for doc in searches])

    yield 'Preparing your request 🛠️'
    
    # Step 1: Generate a search term based on the user query
    messages = [
    {
        "role": "user",
        "content": f"""Based on this chat history {history} the user's request '{user_input}', and this vector database {search_texts} - ignore it unless it relates to the user input, provide:
- The number of searches needed to answer the query
- The specific search terms required for each query, formatted as a Python object.

The response should follow this exact format (example shown):
{{ "number_of_searches_needed": 4,
  "searches": ["First search term", "Second search term", "Third search term", "Fourth search term"]
}}

Return only the data in the specified format without additional text or explanation.
IMPORTANT: NEVER ASSUME THE DATE IN ANY OF YOUR SEARCHES UNLESS THE USER GIVES YOU ONE, YOU DONT KNOW WHAT THE DATE IS BECAUSE OF YOUR KNOWLEDGE CUTOFF. ONLY SEARCH FOR RELEVANT THINGS BASED ON THE USER REQUEST FOR EXAMPLE IF THEY ASK ABOUT STOCK PRICE ONL;Y SEARCH FOR STOCK PRICE
important: if the user input isnt complex use less searches in the intrest of time
"""
    }
]

    # Create streaming chat completion request
    stream = client.chat.completions.create(
        model="Qwen/Qwen2.5-72B-Instruct",
        messages=messages,
        max_tokens=500,
        stream=False
    )


    #take the response from the model and convert it into a list of searches
    returned = stream.choices[0].message.content
    dictionary_returned = json.loads(returned)
    searches_needed = dictionary_returned['searches']
    yield f'Searching the web for {searches_needed} 🌐'
    completed_search = []


    for value in searches_needed:
      print(f'searching for {value}')
      var = search(value)
      completed_search.append(json.dumps(var))

        
    
    
   

    

    # Format results as a JSON string for model input
    
    yield 'thinking...'
    # Step 3: Generate a response using the search results
    response = client.chat_completion(
        model="Qwen/Qwen2.5-72B-Instruct",
        messages=[{"role": "user", "content": f"Using the search results: {completed_search} and chat history {history}, this vector database on health checks {retrieved_texts} answer the user's query '{user_input}' in an concise way,  using numerical data if available, follow the instructions from the vector database if they apply. tailor it to them - {investor_type_value}"}],
        max_tokens=3000,
        stream=True
    )

    yield "Analyzing the data and getting ready to respond 📊"

    # Stream final response
    final_response = ""
    for chunk in response:
        content = chunk.choices[0].delta.content or ''
        final_response += content
        yield final_response

theme = gr.themes.Citrus(
    primary_hue="indigo",
    secondary_hue="indigo",
    neutral_hue="zinc",
)

examples = [
    
    ["whats the trending social sentiment like for Nvidia"],
    ["What's the latest news on Cisco Systems stock"],
    ["Analyze technical indicators for Adobe, are they presenting buy or sell signals"],
    ["Write me a smart sheet on the trending social sentiment and technical indicators for Nvidia"],
    ["What are the best stocks to buy this month"],
    ["What companies report earnings this week"],
    ["write me a health check on adobe"],
]

chatbot = gr.Chatbot(
    label="IM.analyst",
    avatar_images=[None, BOT_AVATAR],
    show_copy_button=True,
    layout="panel",
    height=700
)

with gr.Blocks(theme=theme) as demo:
    with gr.Column():
        gr.Markdown("## quantineuron.com: IM.analyst - Building the Future of Investing")
        investor_type_input = gr.Textbox(label="tell IM.analyst about how you invest", placeholder="Enter your investment style", interactive=True)
        set_type_button = gr.Button("Set Investor Type")
        set_type_button.click(set_investor_type, inputs=investor_type_input, outputs=None)
          
    with gr.Column(scale=3, min_width=600):
        chat_interface = gr.ChatInterface(
            fn=process_query,
            chatbot=chatbot,
            examples=examples
        )

    with gr.Column():
        gr.Markdown('''
        **Disclaimer**: The information provided by IM.analyst is for educational and informational purposes only and does not constitute financial, investment, or professional advice. By using this service, you acknowledge and agree that all decisions you make based on the information provided are made at your own risk. Neither IM.analyst nor quantineuron.com is liable for any financial losses or damages resulting from reliance on information provided by this chatbot.

        By using IM.analyst, you agree to be bound by quantineuron.com’s [Terms of Service](https://quantineuron.com/disclaimer-statement/), [Terms and Conditions](https://quantineuron.com/terms-and-conditions/), [Data Protection and Privacy Policy](https://quantineuron.com/data-protection-and-privacy-policy/), [our discalimer statement](https://quantineuron.com/disclaimer-statement/) and this Disclaimer Statement. We recommend reviewing these documents carefully. Your continued use of this service confirms your acceptance of these terms and conditions, and it is your responsibility to stay informed of any updates or changes.

        **Important Note**: Investing in financial markets carries risk, and it is possible to lose some or all of the invested capital. Always consider seeking advice from a qualified financial advisor.
        ''')
   
demo.launch()