add fingraph v.10 code

README.md

@@ -1,14 +1,26 @@
 ---
-title: Fingraph
-emoji: 🏢
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.31.0
-app_file: app.py
-pinned: false
-license: mit
+title: Fingraph | sdk: gradio | sdk_version: 5.31.0 |license: mit
 short_description: The fortune teller of financial trends.
----
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Financial Analyst Agent (LLaMA 3.1 + LangChain Style)
+
+A lightweight, interactive HuggingFace Space that fetches financial news articles, summarizes them using LLaMA 3.1, and analyzes stock trends.
+
+## Features
+- Google News scraping from Reuters
+- Article summarization via LLM
+- Trend analysis on recent stock prices
+- All wrapped in a clean Gradio interface
+
+## How It Works
+1. Enter a finance-related search term (e.g. "tech stocks").
+2. Enter a stock symbol (e.g. "AAPL").
+3. The app fetches relevant news and summarizes it.
+4. It pulls recent Yahoo Finance data and analyzes it using the LLM.
+
+## Setup
+pip install -r requirements.txt
+python app.py
+
+Built with ❤️ using Hugging Face, Gradio, and LLaMA 3.1
+---

app.py

@@ -1,7 +1,71 @@
+import requests
+import pandas as pd
+from bs4 import BeautifulSoup
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 
-def greet(name):
-    return "Hello " + name + "!!"
+model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype="auto")
+llm_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+def run_llm(prompt: str):
+    result = llm_pipeline(prompt)[0]["generated_text"]
+    return result.replace(prompt, "")
+
+def fetch_financial_news(query="markets", max_articles=3):
+    url = f"https://www.google.com/search?q={query}+site:reuters.com&tbm=nws"
+    headers = {"User-Agent": "Mozilla/5.0"}
+    response = requests.get(url, headers=headers)
+    soup = BeautifulSoup(response.text, "html.parser")
+    links = []
+    for g in soup.find_all('a', href=True):
+        href = g['href']
+        if "reuters.com" in href and len(links) < max_articles:
+            links.append(href.split("&")[0].replace("/url?q=", ""))
+    return links
+
+def summarize_news_article(url):
+    try:
+        r = requests.get(url, headers={"User-Agent": "Mozilla/5.0"})
+        soup = BeautifulSoup(r.text, "html.parser")
+        paragraphs = soup.find_all('p')
+        text = "\n".join(p.get_text() for p in paragraphs[:10])
+        prompt = f"You are a financial analyst. Summarize the key points from this article:\n\n{text}\n\nReturn a concise summary suitable for investors."
+        return run_llm(prompt)
+    except Exception as e:
+        return f"Failed to summarize article: {e}"
+
+def analyze_stock_data(symbol="AAPL"):
+    try:
+        url = f"https://query1.finance.yahoo.com/v7/finance/download/{symbol}?period1=1682899200&period2=1685577600&interval=1d&events=history"
+        df = pd.read_csv(url)
+        df["Date"] = pd.to_datetime(df["Date"])
+        closing_prices = df[["Date", "Close"]].tail(10)
+        data_str = closing_prices.to_string(index=False)
+        prompt = f"You're a financial analyst. Given the following recent closing prices of {symbol}, analyze the trend and summarize in plain English:\n\n{data_str}"
+        return run_llm(prompt)
+    except Exception as e:
+        return f"Failed to fetch stock data: {e}"
+
+def analyze(query, stock_symbol):
+    output = ""
+    output += "📈 Fetching Financial News...\n"
+    urls = fetch_financial_news(query)
+    for url in urls:
+        output += f"\n📰 {url}\n"
+        output += summarize_news_article(url) + "\n"
+    output += "\n📊 Analyzing Stock Trends...\n"
+    output += analyze_stock_data(stock_symbol)
+    return output
+
+gr.Interface(
+    fn=analyze,
+    inputs=[
+        gr.Textbox(label="Financial News Topic", value="tech stocks"),
+        gr.Textbox(label="Stock Symbol", value="AAPL")
+    ],
+    outputs=gr.Textbox(label="Financial Summary", lines=20),
+    title="🧠 Financial Analyst Agent (LLaMA 3.1 + LangChain Style)",
+    description="Summarizes financial news and stock data using LLaMA 3.1 + LangChain-style prompts."
+).launch()