Update app.py

app.py

@@ -154,55 +154,64 @@ def show_sentiment(selected_companies=None, aggregation="Day", selected_year="Al
 import gradio as gr
 
 # Markdown descrittivo adattato al tuo dashboard
+import gradio as gr
+
+# --- Markdown descrittivo ---
 description_text = """
-### How This Dashboard Works
+### Dynamic Sentiment Dashboard
 
-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. 
+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, allowing you to analyze sentiment per company.
-- **Dataset structure**: The dataset includes a company column and is organized so that each row corresponds to a news item for a specific company. This ensures accurate aggregation of sentiment scores while avoiding duplicate rows.
-- **Sentiment aggregation**: You can select a time aggregation level (Month or Year) to see how sentiment evolves over time.
+- **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**: The top-left graph shows the average sentiment score and the corresponding closing price of the selected company. Similar trends are often observed between sentiment and stock performance.
-- **Multiple companies and years**: You can filter by company and year to focus on specific periods, or leave as "All" for a broader overview.
-
+- **Visual insights**: Top-left graph shows average sentiment score and closing price for the selected company.
 """
 
-# Lista di aziende e anni
+# --- Input options ---
 companies = sorted(df['Company'].unique().tolist()) + ["NASDAQ"]
 years = sorted(df['Year'].dropna().unique().tolist())
 
-# Interfaccia a colonne
+# --- Build Gradio Blocks ---
 with gr.Blocks() as demo:
+    # Markdown in alto
     gr.Markdown("# Dynamic Sentiment Dashboard")
+    gr.Markdown(description_text)
 
+    # Input sotto il Markdown
     with gr.Row():
-        # Colonna a sinistra: Markdown
-        with gr.Column(scale=1):
-            gr.Markdown(description_text)
-
-        # Colonna a destra: Input
-        with gr.Column(scale=2):
-            dropdown_companies = gr.Dropdown(
-                choices=companies,
-                value=None,
-                label="Select Companies (NASDAQ compares with general sentiment)",
-                multiselect=False
-            )
+        dropdown_companies = gr.Dropdown(
+            choices=companies,
+            value=None,
+            multiselect=True,
+            label="Select Companies"
+        )
 
-            radio_aggregation = gr.Radio(
-                choices=["Month", "Year"],
-                value="Month",
-                label="Aggregation Level"
-            )
+        radio_aggregation = gr.Radio(
+            choices=["Month", "Year"],
+            value="Month",
+            label="Aggregation Level"
+        )
 
-            dropdown_year = gr.Dropdown(
-                choices=["All"] + years,
-                value=2022,
-                label="Select Year"
-            )
+        dropdown_year = gr.Dropdown(
+            choices=["All"] + years,
+            value="All",
+            label="Select Year"
+        )
+
+    # Bottone submit
+    submit_btn = gr.Button("Submit")
+
+    # Output
+    data_table = gr.Dataframe(label="Sentiment Table", type="pandas")
+    sentiment_plot = gr.Plot(label="Sentiment Trend")
+
+
+    submit_btn.click(
+        fn=show_sentiment, 
+        inputs=[dropdown_companies, radio_aggregation, dropdown_year],
+        outputs=[data_table, sentiment_plot]
+    )
 
-    # Output sotto le colonne
-    gr.Dataframe(label="Sentiment Table", type="pandas")
-    gr.Plot(label="Sentiment Trend")
-demo.launch()
+demo.launch()