app.py

import gradio as gr
from urllib.request import urlopen
import certifi
import json
import pandas as pd
import os
import warnings
import re
warnings.filterwarnings("ignore")

from langchain_community.document_loaders import CSVLoader
from langchain_community.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain_community.llms import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

# Hugging Face API Key
os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.environ.get("HUGGINGFACEHUB_API_TOKEN")

# Load Falcon LLM
model_id = "tiiuae/Falcon3-3B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.1,
    top_k=50,
    top_p=0.95,
    eos_token_id=tokenizer.eos_token_id,
)

llm = HuggingFacePipeline(pipeline=pipe)

# Enhanced Prompt Template
template = """
You are a Financial Market Expert. Analyze the provided market data and create a comprehensive financial report.
Market Data: {context}
Company: {question}
Please provide a detailed analysis including:
1. Current stock performance metrics
2. Price movements and trends
3. Market capitalization details
4. Trading volume analysis
5. Key financial indicators
Format the response as a clear, professional financial report with proper sections and bullet points.
"""
PROMPT = PromptTemplate(input_variables=["context", "question"], template=template)

def format_currency(value):
    """Format currency values"""
    try:
        if isinstance(value, (int, float)):
            return f"${value:,.2f}"
        return str(value)
    except:
        return str(value)

def format_percentage(value):
    """Format percentage values"""
    try:
        if isinstance(value, (int, float)):
            return f"{value:.2f}%"
        return str(value)
    except:
        return str(value)

def create_html_report(data, llm_analysis):
    """Create a formatted HTML report"""
    
    if not data or len(data) == 0:
        return "<div class='error'>No data available for this ticker.</div>"
    
    # Get the first data point (assuming it's the most relevant)
    stock_data = data[0] if isinstance(data, list) else data
    
    # Extract key metrics
    symbol = stock_data.get('symbol', 'N/A')
    name = stock_data.get('name', 'N/A')
    price = stock_data.get('price', 0)
    change = stock_data.get('change', 0)
    change_percent = stock_data.get('changesPercentage', 0)
    market_cap = stock_data.get('marketCap', 0)
    volume = stock_data.get('volume', 0)
    avg_volume = stock_data.get('avgVolume', 0)
    pe_ratio = stock_data.get('pe', 'N/A')
    eps = stock_data.get('eps', 'N/A')
    
    # Determine trend color
    trend_color = "#22c55e" if change >= 0 else "#ef4444"
    trend_icon = "📈" if change >= 0 else "📉"
    
    html = f"""
    <div style="font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; max-width: 800px; margin: 0 auto;">
        <!-- Header Section -->
        <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 25px; border-radius: 12px; margin-bottom: 20px; box-shadow: 0 4px 15px rgba(0,0,0,0.1);">
            <h1 style="margin: 0 0 10px 0; font-size: 28px; font-weight: 600;">{symbol} - Financial Report</h1>
            <h2 style="margin: 0; font-size: 18px; font-weight: 400; opacity: 0.9;">{name}</h2>
            <div style="margin-top: 15px; font-size: 14px; opacity: 0.8;">
                📅 Generated on {pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')}
            </div>
        </div>
        <!-- Key Metrics Grid -->
        <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 15px; margin-bottom: 25px;">
            
            <!-- Current Price Card -->
            <div style="background: white; border-radius: 10px; padding: 20px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); border-left: 4px solid #3b82f6;">
                <div style="color: #6b7280; font-size: 14px; margin-bottom: 5px;">Current Price</div>
                <div style="font-size: 24px; font-weight: 700; color: #1f2937;">{format_currency(price)}</div>
            </div>
            <!-- Price Change Card -->
            <div style="background: white; border-radius: 10px; padding: 20px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); border-left: 4px solid {trend_color};">
                <div style="color: #6b7280; font-size: 14px; margin-bottom: 5px;">Price Change {trend_icon}</div>
                <div style="font-size: 18px; font-weight: 600; color: {trend_color};">
                    {format_currency(change)} ({format_percentage(change_percent)})
                </div>
            </div>
            <!-- Market Cap Card -->
            <div style="background: white; border-radius: 10px; padding: 20px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); border-left: 4px solid #10b981;">
                <div style="color: #6b7280; font-size: 14px; margin-bottom: 5px;">Market Cap</div>
                <div style="font-size: 18px; font-weight: 600; color: #1f2937;">
                    {format_currency(market_cap) if market_cap else 'N/A'}
                </div>
            </div>
            <!-- Volume Card -->
            <div style="background: white; border-radius: 10px; padding: 20px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); border-left: 4px solid #f59e0b;">
                <div style="color: #6b7280; font-size: 14px; margin-bottom: 5px;">Volume</div>
                <div style="font-size: 18px; font-weight: 600; color: #1f2937;">
                    {f"{volume:,}" if volume else 'N/A'}
                </div>
            </div>
        </div>
        <!-- Additional Metrics Table -->
        <div style="background: white; border-radius: 10px; padding: 25px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); margin-bottom: 25px;">
            <h3 style="margin: 0 0 20px 0; color: #1f2937; font-size: 20px; font-weight: 600; border-bottom: 2px solid #e5e7eb; padding-bottom: 10px;">
                📊 Detailed Metrics
            </h3>
            <table style="width: 100%; border-collapse: collapse;">
                <tr style="border-bottom: 1px solid #e5e7eb;">
                    <td style="padding: 12px 0; font-weight: 600; color: #374151; width: 40%;">P/E Ratio</td>
                    <td style="padding: 12px 0; color: #6b7280;">{pe_ratio}</td>
                </tr>
                <tr style="border-bottom: 1px solid #e5e7eb;">
                    <td style="padding: 12px 0; font-weight: 600; color: #374151;">Earnings Per Share (EPS)</td>
                    <td style="padding: 12px 0; color: #6b7280;">{format_currency(eps) if eps != 'N/A' else 'N/A'}</td>
                </tr>
                <tr style="border-bottom: 1px solid #e5e7eb;">
                    <td style="padding: 12px 0; font-weight: 600; color: #374151;">Average Volume</td>
                    <td style="padding: 12px 0; color: #6b7280;">{f"{avg_volume:,}" if avg_volume else 'N/A'}</td>
                </tr>
                <tr>
                    <td style="padding: 12px 0; font-weight: 600; color: #374151;">Exchange</td>
                    <td style="padding: 12px 0; color: #6b7280;">{stock_data.get('exchange', 'N/A')}</td>
                </tr>
            </table>
        </div>
        <!-- AI Analysis Section -->
        <div style="background: white; border-radius: 10px; padding: 25px; box-shadow: 0 2px 10px rgba(0,0,0,0.1);">
            <h3 style="margin: 0 0 20px 0; color: #1f2937; font-size: 20px; font-weight: 600; border-bottom: 2px solid #e5e7eb; padding-bottom: 10px;">
                🤖 AI Analysis
            </h3>
            <div style="background: #f8fafc; border-radius: 8px; padding: 20px; border-left: 4px solid #6366f1;">
                <pre style="white-space: pre-wrap; font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; margin: 0; line-height: 1.6; color: #374151;">{llm_analysis}</pre>
            </div>
        </div>
        <!-- Footer -->
        <div style="text-align: center; margin-top: 25px; padding: 20px; color: #6b7280; font-size: 14px; border-top: 1px solid #e5e7eb;">
            <p style="margin: 0;">⚠️ This report is for informational purposes only and should not be considered as investment advice.</p>
        </div>
    </div>
    """
    
    return html

def generate_report(ticker: str, exchange: str):
    """Generate enhanced financial report"""
    api_key = "C1HRSweTniWdBuLmTTse9w8KpkoiouM5"
    
    if not ticker.strip():
        return "<div style='color: red; padding: 20px; text-align: center;'>❌ Please enter a valid ticker symbol.</div>"
    
    try:
        # Show loading message
        loading_html = """
        <div style="text-align: center; padding: 40px; font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;">
            <div style="display: inline-block; padding: 20px; background: #f0f9ff; border-radius: 10px; border: 1px solid #0ea5e9;">
                <div style="font-size: 18px; color: #0369a1; margin-bottom: 10px;">🔄 Generating Report...</div>
                <div style="color: #64748b;">Fetching data and running AI analysis</div>
            </div>
        </div>
        """
        
        # Fetch Data
        ticker = ticker.upper().strip()
        if exchange == "NSE":
            url = f"https://financialmodelingprep.com/api/v3/search?query={ticker}&exchange=NSE&apikey={api_key}"
        else:
            url = f"https://financialmodelingprep.com/api/v3/quote/{ticker}?apikey={api_key}"
        
        response = urlopen(url, cafile=certifi.where())
        data = json.loads(response.read().decode("utf-8"))

        if not data:
            return "<div style='color: red; padding: 20px; text-align: center;'>❌ No data found for ticker: " + ticker + "</div>"

        # Save to CSV for RAG processing
        df = pd.DataFrame(data)
        if 'timestamp' in df.columns:
            df['timestamp'] = pd.to_datetime(df['timestamp'], errors='coerce')
        if 'earningsAnnouncement' in df.columns:
            df['earningsAnnouncement'] = pd.to_datetime(df['earningsAnnouncement'], errors='coerce')
        df.to_csv("eco_ind.csv", index=False)

        # Create vector store and run LLM analysis
        loader = CSVLoader("eco_ind.csv")
        documents = loader.load()
        splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=20)
        texts = splitter.split_documents(documents)
        embeddings = HuggingFaceEmbeddings()

        chroma = Chroma.from_documents(
            documents=texts,
            collection_name="economic_data",
            embedding=embeddings,
            persist_directory="docs/chroma_rag"
        )

        retriever = chroma.as_retriever(search_kwargs={"k": 3})
        qa = RetrievalQA.from_chain_type(
            llm=llm,
            chain_type="stuff",
            chain_type_kwargs={"prompt": PROMPT},
            retriever=retriever,
            return_source_documents=False
        )

        # Get LLM analysis
        query = f"Provide a comprehensive financial analysis for {ticker}"
        result = qa({"query": query})
        llm_analysis = result["result"]

        # Create and return formatted HTML report
        return create_html_report(data, llm_analysis)

    except Exception as e:
        error_html = f"""
        <div style="font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; max-width: 600px; margin: 20px auto; padding: 25px; background: #fef2f2; border-radius: 10px; border: 1px solid #fecaca;">
            <h3 style="color: #dc2626; margin: 0 0 15px 0; display: flex; align-items: center;">
                ❌ Error Occurred
            </h3>
            <div style="background: white; padding: 15px; border-radius: 6px; border: 1px solid #f3f4f6;">
                <strong>Details:</strong> {str(e)}
            </div>
            <div style="margin-top: 15px; color: #6b7280; font-size: 14px;">
                Please check your ticker symbol and try again. Make sure you have a stable internet connection.
            </div>
        </div>
        """
        return error_html

# Custom CSS for better styling
custom_css = """
#component-0 {
    max-width: 1200px !important;
    margin: 0 auto !important;
}
.gradio-container {
    background-color: black !important;
    min-height: 100vh !important;
}
#title {
    text-align: center !important;
    color: white !important;
    font-size: 2.5rem !important;
    font-weight: 700 !important;
    margin-bottom: 1rem !important;
    text-shadow: 0 2px 4px rgba(0,0,0,0.1) !important;
}
#description {
    text-align: center !important;
    color: white !important;
    font-size: 1.1rem !important;
    margin-bottom: 2rem !important;
}
.input-container {
    background: white !important;
    border-radius: 5px !important;
    padding: 3px !important;
    box-shadow: 0 10px 30px rgba(0,0,0,0.1) !important;
    margin-bottom: 20px !important;
}
.output-container {
    background: transparent !important;
    border-radius: 15px !important;
    padding: 10px;
    border: 2px white solid ;
    overflow: hidden !important;
    box-shadow: 0 10px 30px rgba(0,0,0,0.2) !important;
}
"""

# Create the Gradio interface with sidebar for inputs
with gr.Blocks(css=custom_css, title="📊 Financial Report Generator") as iface:
    gr.HTML('<div id="title">📊 AI-Powered Financial Report Generator</div>')
    gr.HTML('<div id="description">Enter a stock ticker and exchange to get a comprehensive AI-generated financial analysis</div>')

    # Sidebar for inputs
    with gr.Sidebar():
        gr.Markdown("## 📥 Input Parameters")

        ticker_input = gr.Textbox(
            label="🎯 Stock Ticker Symbol",
            placeholder="e.g., MSFT, AAPL, GOOGL",
            info="Enter the stock symbol you want to analyze"
        )

        exchange_input = gr.Radio(
            choices=["US", "NSE"],
            label="🏢 Exchange",
            value="US",
            info="Select the stock exchange"
        )

        generate_btn = gr.Button(
            "🚀 Generate Report",
            variant="primary"
        )

        clear_btn = gr.Button(
            "🔄 Clear",
            variant="secondary"
        )

    # Output section
    with gr.Row(elem_classes=["output-container"]):
        output = gr.HTML(label="📊 Financial Report")

    # Event handlers
    generate_btn.click(
        fn=generate_report,
        inputs=[ticker_input, exchange_input],
        outputs=output
    )

    clear_btn.click(
        lambda: ["", "US", ""],
        outputs=[ticker_input, exchange_input, output]
    )

    ticker_input.submit(
        fn=generate_report,
        inputs=[ticker_input, exchange_input],
        outputs=output
    )


if __name__ == "__main__":
    iface.launch(
        share=True,
        show_error=True
    )