Updated app

app.py

@@ -110,35 +110,22 @@ def save_to_db(df, ticker):
 
 
 def plot_sentiment(df, ticker, interval):
-    """ Generates sentiment trend plots while ensuring correct date range. """
+    """ Generates sentiment trend plots with correct filtering """
     if df.empty:
         return None
 
     df['datetime'] = pd.to_datetime(df['datetime'])
     df = df.set_index('datetime')
 
-    # ✅ Ensure the graph strictly uses only the filtered range
-    min_date = df.index.min()
-    max_date = df.index.max()
-    print(f"📅 Graph Showing Data from {min_date} to {max_date}")  # Debugging
+    # ✅ Resample only within the available range
+    df = df.resample(interval).mean(numeric_only=True).dropna()
 
-    df_filtered = df.loc[min_date:max_date]
-
-    if interval == 'h':
-        df_grouped = df_filtered.resample('h').mean(numeric_only=True).dropna()
-    elif interval == 'D':
-        df_grouped = df_filtered.resample('D').mean(numeric_only=True).dropna()
-    else:
-        df_grouped = df_filtered
-
-    df_grouped['rolling_avg'] = df_grouped['sentiment_score'].rolling(5, min_periods=1).mean()
-
-    fig = px.line(df_grouped, x=df_grouped.index, y='sentiment_score',
-                  labels={"sentiment_score": "Sentiment Score"},
-                  title=f"{ticker} Sentiment Trends ({interval.capitalize()})")
-
-    fig.add_scatter(x=df_grouped.index, y=df_grouped['rolling_avg'], mode='lines', name='Rolling Avg')
+    # ✅ 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
 
 
@@ -155,6 +142,7 @@ def analyze_stock_sentiment(ticker, days):
 
     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)
@@ -164,12 +152,13 @@ def analyze_stock_sentiment(ticker, days):
     df_news['datetime'] = pd.to_datetime(df_news['datetime'])
     df_news = df_news[df_news['datetime'] >= start_date]
 
-    print(f"📊 Filtered News Count: {len(df_news)}")  # Debugging
-
     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')
@@ -178,14 +167,31 @@ def analyze_stock_sentiment(ticker, days):
     return f"✅ Analysis for {ticker.upper()} (Last {days} Days) Complete!", df_scored, fig_hourly, fig_daily
 
 
-# ✅ Gradio UI
-iface = gr.Interface(
-    fn=analyze_stock_sentiment,
-    inputs=[gr.Textbox(label="Stock Ticker"), gr.Slider(1, 15, 7, label="Days of News History")],
-    outputs=[gr.Textbox(label="Status"), gr.Dataframe(), gr.Plot(), gr.Plot()]
-)
+# 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])
 
 # ✅ Keeps the App Running on Hugging Face
 if __name__ == "__main__":
     print("🚀 App is running on Hugging Face Spaces...")
     iface.launch(server_name="0.0.0.0", server_port=7860)
+
+