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": 4},
                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):
    
    # 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.)"}],
        max_tokens=400,
        stream=True
    )
    
    # 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

    print("Search Query:", streamed_search_query)  # Debugging: Check the final search term

    # 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 IM.S, AN INVESTMENT CHATBOT BUILT BY automatedstockmining.org. Answer the user's request '{user_input}' using the following information: {search_results_str} 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."}],
        max_tokens=3000,
        stream=True
    )

    # 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()