Update app.py

app.py

@@ -162,12 +162,24 @@ description_text = """
 
 This dashboard allows you to explore the sentiment of news articles related to major tech companies (Apple, Tesla, Microsoft, Meta, Alphabet) and compare it with their stock prices.
 
-- **Multiple companies per news**: Some news articles mention more than one company. Each news item is associated with the relevant companies in the dataset.
 - **Dataset structure**: The dataset includes a company column; each row corresponds to a news item for a specific company.
 - **Sentiment aggregation**: Select a time aggregation level (Month or Year) to see how sentiment evolves over time.
 - **NASDAQ comparison**: Selecting "NASDAQ" shows the general market sentiment alongside the company-specific sentiment.
 - **Visual insights**: Top-left graph shows average sentiment score and closing price for the selected company.
 """
+# --- Findings from thesis (specific companies and years) ---
+findings_text = """
+### Key Findings
+
+- Some news articles refer to multiple companies, e.g., the same article may mention Apple and Tesla.  
+- Merging news with stock prices allows analyzing correlations between sentiment and stock movements for each company.  
+- **Apple (2018, 2019, 2022):** Sentiment trends generally align with closing prices, showing similar monthly patterns.  
+- **Tesla (2018, 2019, 2022):** More volatility observed; sentiment aligns with stock movement but is more sensitive to news on Elon Musk’s actions.  
+- **Microsoft, Meta, Alphabet:** Across periods, sentiment trends follow stock prices with moderate correlation.  
+- The custom sentiment model is more aligned with actual stock movements compared to FinBERT, which is more influenced by word positivity/negativity.  
+- Aggregating sentiment by month or year helps identify broader trends while reducing noise from daily fluctuations.  
+- Including “NASDAQ” as a general market reference allows comparison of individual companies’ sentiment with overall market sentiment.
+"""
 
 # --- Input options ---
 companies = sorted(df['Company'].unique().tolist()) + ["NASDAQ"]
@@ -184,7 +196,7 @@ with gr.Blocks() as demo:
         dropdown_companies = gr.Dropdown(
             choices=companies,
             value=None,
-            multiselect=True,
+            multiselect=False,
             label="Select Companies"
         )
 
@@ -206,6 +218,9 @@ with gr.Blocks() as demo:
     # Output
     data_table = gr.Dataframe(label="Sentiment Table", type="pandas")
     sentiment_plot = gr.Plot(label="Sentiment Trend")
+    
+    # Findings section
+    gr.Markdown(findings_text)
 
 
     submit_btn.click(