Updated app

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
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+B5T0Y3HsLuJvW5iB4YlcNHlsdu87kGJ55tukmi8mxdAQ4Q7e2RCOFvu396j3x+UC
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+rU7m2Ys6xt0nUW7/vGT1M0NPAgMBAAGjQjBAMA4GA1UdDwEB/wQEAwIBBjAPBgNV
+HRMBAf8EBTADAQH/MB0GA1UdDgQWBBR5tFnme7bl5AFzgAiIyBpY9umbbjANBgkq
+hkiG9w0BAQsFAAOCAgEAVR9YqbyyqFDQDLHYGmkgJykIrGF1XIpu+ILlaS/V9lZL
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+emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
+-----END CERTIFICATE-----

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/Finance_Stock_Prediction_v1.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.12 (Finance_Stock_Prediction_v1)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/dataSources.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="DataSourceManagerImpl" format="xml" multifile-model="true">
+    <data-source source="LOCAL" name="sentiment_data" uuid="797dab22-bf41-4c84-8d62-7031ba070b53">
+      <driver-ref>sqlite.xerial</driver-ref>
+      <synchronize>true</synchronize>
+      <jdbc-driver>org.sqlite.JDBC</jdbc-driver>
+      <jdbc-url>jdbc:sqlite:$PROJECT_DIR$/sentiment_data.db</jdbc-url>
+      <working-dir>$ProjectFileDir$</working-dir>
+    </data-source>
+  </component>
+</project>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.9 (pythonProject)" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.12 (Finance_Stock_Prediction_v1)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Finance_Stock_Prediction_v1.iml" filepath="$PROJECT_DIR$/.idea/Finance_Stock_Prediction_v1.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

App.py

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+import gradio as gr
+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+import plotly.express as px
+import datetime
+import time
+import nltk
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+from textblob import TextBlob
+import ssl
+import certifi
+import sqlite3
+
+# Configure SSL to use certifi certificates
+ssl._create_default_https_context = lambda: ssl.create_default_context(cafile=certifi.where())
+
+# Download VADER lexicon
+try:
+    nltk.download('vader_lexicon')
+    print("✅ VADER lexicon downloaded successfully!")
+except Exception as e:
+    print(f"❌ Error downloading VADER lexicon: {str(e)}")
+    raise
+
+# Initialize VADER sentiment analyzer
+sia = SentimentIntensityAnalyzer()
+
+FINVIZ_URL = 'https://finviz.com/quote.ashx?t='
+session = requests.Session()
+session.headers.update({
+    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
+})
+
+
+def get_news(ticker):
+    """ Fetch stock news from FinViz with error handling """
+    try:
+        url = FINVIZ_URL + ticker
+        for attempt in range(3):
+            response = session.get(url)
+            if response.status_code == 200:
+                break
+            time.sleep(5)
+
+        if response.status_code != 200:
+            return None, f"❌ Error: Received status code {response.status_code}"
+
+        html = BeautifulSoup(response.text, "html.parser")
+        news_table = html.find('table', class_='fullview-news-outer')
+
+        if not news_table:
+            return None, "❌ News table not found!"
+
+        return news_table, None
+    except Exception as e:
+        return None, f"❌ Error fetching stock news: {str(e)}"
+
+
+def parse_news(news_table):
+    """ Extracts and parses stock news headlines from FinViz """
+    parsed_news = []
+    today_string = datetime.datetime.today().strftime('%Y-%m-%d')
+
+    for row in news_table.find_all('tr'):
+        try:
+            news_container = row.find('div', class_='news-link-container')
+            link = news_container.find('a', class_='tab-link-news') if news_container else row.find('a')
+
+            if not link or not link.get_text().strip():
+                continue
+            text = link.get_text().strip()
+
+            date_td = row.find('td', align='right')
+            date_scrape = date_td.text.strip().split() if date_td and date_td.text.strip() else []
+            if len(date_scrape) == 1:
+                date, time_ = today_string, date_scrape[0]
+            else:
+                date, time_ = datetime.datetime.strptime(date_scrape[0], '%b-%d-%y').strftime('%Y-%m-%d'), date_scrape[
+                    1]
+
+            time_ = datetime.datetime.strptime(time_, '%I:%M%p').strftime('%H:%M')
+            parsed_news.append([date, time_, text])
+        except Exception:
+            continue
+
+    df = pd.DataFrame(parsed_news, columns=['date', 'time', 'headline'])
+    df['datetime'] = pd.to_datetime(df['date'] + ' ' + df['time'], format="%Y-%m-%d %H:%M", errors='coerce')
+    return df.dropna(subset=['datetime'])
+
+
+def analyze_sentiment(text):
+    """ Combines VADER and TextBlob for better sentiment accuracy """
+    vader_score = sia.polarity_scores(text)['compound']
+    textblob_score = TextBlob(text).sentiment.polarity  # -1 to 1 range
+    return (vader_score + textblob_score) / 2  # Averaging both
+
+
+def score_news(df):
+    """ Applies sentiment analysis using both VADER and TextBlob """
+    df['sentiment_score'] = df['headline'].apply(analyze_sentiment)
+    return df[['datetime', 'headline', 'sentiment_score']]
+
+
+def save_to_db(df, ticker):
+    """ Stores sentiment analysis results in SQLite for historical tracking """
+    conn = sqlite3.connect("sentiment_data.db")
+    df.to_sql(f"{ticker}_news", conn, if_exists="append", index=False)
+    conn.close()
+
+
+def plot_sentiment(df, ticker, interval):
+    """ Generates sentiment trend plots with correct filtering """
+    if df.empty:
+        return None
+
+    df['datetime'] = pd.to_datetime(df['datetime'])
+    df = df.set_index('datetime')
+
+    # ✅ Resample only within the available range
+    df = df.resample(interval).mean(numeric_only=True).dropna()
+
+    # ✅ Use rolling average to smooth the graph
+    df['rolling_avg'] = df['sentiment_score'].rolling(5, min_periods=1).mean()
+
+    fig = px.line(df, x=df.index, y=['sentiment_score', 'rolling_avg'],
+                  labels={"value": "Sentiment Score"},
+                  title=f"{ticker} {interval.capitalize()} Sentiment Trends")
+    return fig
+
+
+def analyze_stock_sentiment(ticker, days):
+    """ Fetches news, analyzes sentiment, and filters by user-selected date range. """
+    if not ticker:
+        return "❌ Please enter a stock ticker!", None, None, None
+
+    print(f"📅 Selected Days: {days}")  # Debugging
+
+    news_table, error = get_news(ticker.upper())
+    if error:
+        return error, None, None, None
+
+    df_news = parse_news(news_table)
+
+    # ✅ Convert `days` to an integer and filter news based on the correct time range
+    days = int(days)
+    today = datetime.datetime.today()
+    start_date = today - datetime.timedelta(days=days)
+
+    print(f"🔍 Filtering News from {start_date} to {today}")  # Debugging
+
+    df_news['datetime'] = pd.to_datetime(df_news['datetime'])
+    df_news = df_news[df_news['datetime'] >= start_date]
+
+    if df_news.empty:
+        return f"⚠️ No news found for {ticker.upper()} in the last {days} days.", None, None, None
+
+    df_scored = score_news(df_news).sort_values(by="datetime", ascending=False)
+
+    print(f"📊 Filtered News Count: {len(df_scored)}")  # Debugging
+
+    save_to_db(df_scored, ticker.upper())
+
+    fig_hourly = plot_sentiment(df_scored, ticker, interval='h')
+    fig_daily = plot_sentiment(df_scored, ticker, interval='D')
+
+    return f"✅ Analysis for {ticker.upper()} (Last {days} Days) Complete!", df_scored, fig_hourly, fig_daily
+
+
+# Gradio Interface
+with gr.Blocks(title="📈 Stock News Sentiment Analyzer") as iface:
+    with gr.Row():
+        gr.Markdown("## 📈Stock News Sentiment Analyzer")
+        gr.Markdown("Analyze stock news sentiment using VADER and TextBlob.")
+
+    ticker_dropdown = gr.Dropdown(choices=["AAPL", "TSLA", "AMZN", "MSFT"], label="Stock Ticker")
+    days_slider = gr.Slider(minimum=1, maximum=30, step=1, label="Days of News History", value=7, interactive=True)
+    status = gr.Textbox(label="Status", interactive=False)
+
+    with gr.Row():
+        submit_btn = gr.Button("Analyze", variant="primary")
+        clear_btn = gr.Button("Clear", variant="secondary")
+
+    table = gr.Dataframe(label="Sentiment Analysis", interactive=False)
+    hourly_plot = gr.Plot(label="Hourly Sentiment Scores")
+    daily_plot = gr.Plot(label="Daily Sentiment Scores")
+
+    submit_btn.click(fn=analyze_stock_sentiment, inputs=[ticker_dropdown, days_slider],
+                     outputs=[status, table, hourly_plot, daily_plot])
+    clear_btn.click(fn=lambda: ("", None, None, None), inputs=None, outputs=[status, table, hourly_plot, daily_plot])
+
+iface.launch(share=True)

requirements.txt

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+gradio
+requests
+beautifulsoup4
+pandas
+plotly
+nltk
+textblob
+certifi
+sqlite3