app.py

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

# Define global variables
BOT_AVATAR = 'https://automatedstockmining.org/wp-content/uploads/2024/08/south-west-value-mining-logo.webp'
hf_token = os.getenv("HF_TOKEN")

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 'Preparing your request 🛠️'
    # Accumulate streamed content from the initial request
    stream_search = client.chat_completion(
        model="Qwen/Qwen2.5-72B-Instruct",
        messages=[{"role": "user", "content": f"Based on this chat history {history} and the user's request '{user_input}', suggest a Google search term in a single line without assuming any specific dates use 'this year', 'this month', etc. make sure that you do generate a good search based on the query, for example if the user asks 'build a DFCF model for Apple', search up something like 'Apple DFCF model', and if they ask for something like a market cap, just search up what is Apple's current market cap. make sure that you provide a good google search all of the time, for example if they ask for the price of pulsar helium stock just search for it"}],
        max_tokens=400,
        stream=True
    )
    yield 'Generating search term based on your input 🔍'
    # Accumulate content from the streamed response
    streamed_search_query = ""
    for chunk in stream_search:
        content = chunk.choices[0].delta.content or ''
        streamed_search_query += content

    
    yield 'Searching the web for relevant information 🌐'
    # Perform the web search based on the accumulated query
    search_results = search(streamed_search_query)
    search_results_str = json.dumps(search_results)
    
    # Create the response request with HuggingFace using search results
    response = client.chat_completion(
        model="Qwen/Qwen2.5-72B-Instruct",
        messages=[{"role": "user", "content": f"YOU ARE AN INVESTMENT CHATBOT . Answer the user's request '{user_input}' using the following information: {search_results} and the chat history {history}. Provide a concise, direct answer in no more than 2-3 sentences, with appropriate emojis. If the user asks for a smart sheet, generate up to 3000 tokens analyzing all trends and patterns as though you are a stock analyst, look for every pattern and form conclusions. IF YOU ARE ASKED TO BUILD MODELS OR LIST STOCKS, USE THE SEARCH DATA TO HELP YOU BUILD THEM AND MAKE SURE YOU ACTUALLY GIVE THE VALUES AND WHAT THE ARTICLE SAYS RATHER THAN JUST SAYING THAT IT IS AVAILABLE FROM THE WEBSITE. unless the user asks for a smart sheet. keep responses short, quick and logical, using the data provided. AT THE END OF EACH RESPONSE ESPECIALLY SAY HOW THIS IS NOT FINANCIAL ADVICE AND QUANTINEURON.COM IS NOT RESPONSIBLE FOR ANYTHING THAT GOES WRONG BECAUSE OF WHAT YOU SAY, AND THAT BY USING YOU, YOU AGREE TO OUR DISCLAIMER STATEMENT, TERMS OF SERVICE, TERMS AND CONSITIONS AND DATA COLLECTION AND PRIVACY POLICY"}],
        max_tokens=3000,
        stream=True
    )
    yield "Analyzing the search results and crafting a response 📊"
    # Accumulate the final response and stream it
    final_response = ""
    for chunk in response:
        content = chunk.choices[0].delta.content or ''
        final_response += content
        yield final_response  # Yield the accumulated response for real-time streaming

theme = gr.themes.Citrus(
    primary_hue="blue",
    neutral_hue="slate",
)

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 techncial indicators for nvidia"],
    ["What are the best stocks to buy this month"],
    ["What companies report earnings this week"],
    ["What's Apple's current market cap"],
    ["analyse the technical indicators for apple"],
    ["build a DFCF model for Apple"],
    ["Make a table of Apple's stock price for the last 3 days"],
    ['what is Apples PE ratio and how does it compare top other companies in consumer electronics'],
    ['how did salesforce do on its last earnings?'],
    ['what is the average analyst price target for Nvidia'],
    ['whats the outlook for the stock market in 2025'],
    ['when does Nvidia next report earnings'],
    ['what are the latest products from apple'],
    ["What is Tesla's current price-to-earnings ratio and how does it compare to other car manufacturers?"],
    ["List the top 5 performing stocks in the S&P 500 this month"],
    ["What is the dividend yield for Coca-Cola?"],
    ["Which companies in the tech sector are announcing dividends this month?"],
    ["Analyze the latest moving averages for Microsoft; are they indicating a trend reversal?"],
    ["What is the latest guidance on revenue for Meta?"],
    ["What is the current beta of Amazon stock and how does it compare to the industry average?"],
    ["What are the top-rated ETFs for technology exposure this quarter?"]
]

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

chat_interface = gr.ChatInterface(
    theme=theme,
    fn=process_query,
    chatbot=chatbot,
    examples=examples,
)

chat_interface.launch()