Update app.py

app.py

@@ -1,76 +1,111 @@
 import streamlit as st
+import requests
+import yfinance as yf
 import pandas as pd
-import feedparser
+import datetime
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
+from plotly import graph_objs as go
 
-# Load the trained model and tokenizer
-MODEL_NAME = "rahilv/financial-sentiment-model"
-model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-
-LABEL_MAP = {0: "Bullish", 1: "Bearish", 2: "Neutral"}
-
-# RSS Feed URLs
-rss_feeds = {
-    "Benzinga": ["https://www.benzinga.com/feeds/news", "https://www.benzinga.com/feeds/analysis"],
-    "Yahoo News": ["https://finance.yahoo.com/rss/topstories"]
-}
-
-def fetch_rss_feed(url):
-    """Fetch articles from an RSS feed."""
-    feed = feedparser.parse(url)
-    articles = []
-    for entry in feed.entries:
-        articles.append({"title": entry.title, "link": entry.link})
-    return articles
-
-def analyze_sentiment(text):
-    """Analyze sentiment of a given text."""
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=128)
+# Load the Hugging Face model and tokenizer
+@st.cache_resource
+def load_model():
+    model_name = "rahilv/financial-sentiment-model"
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    return model, tokenizer
+
+sentiment_model, sentiment_tokenizer = load_model()
+
+LABEL_MAP = {0: "bullish", 1: "bearish", 2: "neutral"}
+
+# Function to fetch stock news
+def get_stock_news(ticker):
+    api_key = 'd651109fae5346cbbb6812912c801e73'
+    url = f'https://newsapi.org/v2/everything?q={ticker}&apiKey={api_key}'
+
+    response = requests.get(url)
+    if response.status_code == 200:
+        articles = response.json().get('articles', [])
+        return articles
+    else:
+        st.error(f"Error fetching news: {response.status_code}")
+        return []
+
+# Function to analyze sentiment
+def classify_sentiment(news_title):
+    inputs = sentiment_tokenizer(news_title, return_tensors="pt", truncation=True, padding="max_length", max_length=128)
     with torch.no_grad():
-        outputs = model(**inputs)
+        outputs = sentiment_model(**inputs)
         predictions = torch.argmax(outputs.logits, dim=-1).item()
     return LABEL_MAP[predictions]
 
-def main():
-    st.set_page_config(page_title="Financial News Sentiment", layout="wide")
+# Function to fetch stock data from Yahoo Finance
+def fetch_stock_data(ticker):
+    stock = yf.Ticker(ticker)
+    end_date = datetime.date.today()
+    start_date = end_date - datetime.timedelta(days=365)  # 1 year of data
+    data = stock.history(start=start_date, end=end_date)
+    return data
+
+# Streamlit UI
+st.title("Stock Analysis and Sentiment App")
+
+# User input for stock ticker symbol
+ticker_symbol = st.text_input("Enter a Stock Ticker Symbol (e.g., AAPL, TSLA, GOOGL):")
+
+if ticker_symbol:
+    st.subheader(f"Analysis for {ticker_symbol.upper()}")
+
+    # Fetch news
+    articles = get_stock_news(ticker_symbol)
 
-    st.title("\U0001F4C8 Financial News Sentiment Analysis")
-    st.write("Analyze sentiment from the latest financial news articles.")
+    if articles:
+        # Create a DataFrame for chart points
+        news_points = []
+        
+        st.write("## Stock Price Chart")
+        # Fetch and plot stock data
+        stock_data = fetch_stock_data(ticker_symbol)
+        
+        if not stock_data.empty:
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(x=stock_data.index, y=stock_data['Close'], mode='lines', name='Close Price'))
 
-    # Sidebar: Feed Selection
-    st.sidebar.header("Settings")
-    selected_feeds = st.sidebar.multiselect("Select RSS Feeds:", options=rss_feeds.keys(), default=list(rss_feeds.keys()))
+            for article in articles:
+                date = article['publishedAt'][:10]  # Extract date
+                title = article['title']
+                sentiment = classify_sentiment(title)
+                news_points.append({'date': date, 'title': title, 'sentiment': sentiment})
+                
+                color = 'green' if sentiment == 'bullish' else 'red' if sentiment == 'bearish' else 'gray'
+                if date in stock_data.index:
+                    fig.add_trace(go.Scatter(x=[date], y=[stock_data['Close'][date]],
+                                             mode='markers', marker=dict(color=color, size=10)))
 
-    if not selected_feeds:
-        st.warning("Please select at least one RSS feed.")
-        return
+            fig.update_layout(title=f"{ticker_symbol.upper()} Stock Price & News Sentiment", xaxis_title="Date", yaxis_title="Price")
+            st.plotly_chart(fig)
 
-    st.sidebar.markdown("---")
-    st.sidebar.info(
-        "This app fetches financial news from RSS feeds and analyzes the sentiment (Bullish, Bearish, Neutral) of news titles."
-    )
+        else:
+            st.write("No stock data available.")
 
-    # Fetch and Display Articles
-    st.header("\U0001F4F0 Latest Financial News")
+        st.write("## News Analysis")
+        for point in news_points:
+            color = 'green' if point['sentiment'] == 'bullish' else 'red' if point['sentiment'] == 'bearish' else 'gray'
+            st.markdown(f"<div style='border-left: 5px solid {color}; padding: 10px; margin: 10px 0;'>"
+                        f"<b>{point['title']}</b><br>{point['date']}</div>", unsafe_allow_html=True)
 
-    articles = []
-    for feed_name in selected_feeds:
-        for url in rss_feeds[feed_name]:
-            articles.extend(fetch_rss_feed(url))
+        # Recommendation based on sentiment
+        sentiments = [p['sentiment'] for p in news_points]
+        recommendation = "hold"
 
-    if not articles:
-        st.write("No articles found.")
-        return
+        if sentiments.count('bullish') > sentiments.count('bearish'):
+            recommendation = "buy"
+        elif sentiments.count('bearish') > sentiments.count('bullish'):
+            recommendation = "sell"
 
-    # Display articles in a clean layout
-    for article in articles:
-        sentiment = analyze_sentiment(article["title"])
-        st.subheader(article["title"])
-        st.write(f"Sentiment: **{sentiment}**")
-        st.markdown(f"[Read more]({article['link']})", unsafe_allow_html=True)
-        st.markdown("---")
+        color_map = {"buy": "green", "sell": "red", "hold": "gray"}
+        st.markdown(f"### Recommendation: <span style='color: {color_map[recommendation]}; font-size: 1.5em;'>{recommendation.upper()}</span>", unsafe_allow_html=True)
 
-if __name__ == "__main__":
-    main()
+    else:
+        st.write("No news articles found.")