Update app.py

app.py

@@ -203,51 +203,52 @@ def show_company_data(selected_companies, aggregation="Day"):
     )
 
     return None, fig_strat, fig_price
-# --- MARKDOWN DESCRITTIVO ---
+import gradio as gr
+
+# --- Markdown descrittivo ---
 description_text = """
 ### Portfolio Strategy Comparison Dashboard
-
 This dashboard allows you to compare the performance of three sentiment models in driving trading strategies for Microsoft and Tesla.
-
 - **Strategy logic**: Each model's score (or regression value) is used as a buy/sell signal. 
   - If the score exceeds 0.8 → buy
   - If the score is below -0.8 → sell
   - Otherwise → no trade
   - For the regression model, thresholds are +1 and -1.
-
 - **Dataset and preprocessing**:  
   - Closing prices and daily percent changes are calculated for each company.  
   - News articles mentioning Microsoft or Tesla are merged with the price data.  
   - Negative/Down scores are multiplied by -1, Neutral scores set to 0.  
   - Daily strategy value = daily percent change × stock price.  
   - Cumulative value = sum of daily strategy values over time.  
-
 - **Model comparison**:  
   - Regression is fine-tuned separately for Tesla and Microsoft.  
   - FinBERT is used as a baseline.  
   - The custom model incorporates actual stock movements and company-specific signals.
-
 - **Results overview**:  
   - Tesla: Regression often performs better, though some losses occur.  
   - Microsoft: Regression closely follows market trends; FinBERT is less accurate.  
   - Regression aligns better with real stock movements by interpreting news contextually.
-
 - **Caveats**:  
   - Multiple news per day may generate buy/sell signals that cancel each other.  
   - Strategy uses next-day price changes; no multi-day logic is applied.  
   - Simplified testing, but useful to compare model behavior.
-
 """
 
+# --- INPUT OPTIONS ---
+companies = ["Microsoft", "Tesla, Inc."]
+
+# --- FUNZIONE DI ESECUZIONE ---
+def show_strategy(selected_companies, aggregation):
+    # Qui chiami la funzione del tuo modello per generare i grafici e il dataframe
+    # Ad esempio: return df, fig_strategies, fig_prices
+    return None, None, None  # sostituire con output reali
 
-# --- INTERFACCIA GRADIO CON DROPDOWN SOTTO IL TESTO ---
+# --- INTERFACCIA GRADIO ---
 with gr.Blocks() as demo:
     gr.Markdown("# Portfolio Strategy Dashboard")
-
-    # Markdown in alto
     gr.Markdown(description_text)
 
-    # Input sotto il Markdown
+    # Input
     with gr.Row():
         dropdown_companies = gr.Dropdown(
             choices=companies,
@@ -255,18 +256,24 @@ with gr.Blocks() as demo:
             multiselect=True,
             label="Select Companies"
         )
-
         radio_aggregation = gr.Radio(
             choices=["Day", "Month", "Year"],
             value="Day",
             label="Aggregation Level"
         )
-
-    # Output sotto gli input
-    gr.Dataframe(label="Data Preview", type="pandas")
-    gr.Plot(label="Strategies")
-    gr.Plot(label="Prices")
+        submit_btn = gr.Button("Submit")  # bottone per inviare
+
+    # Output
+    data_table = gr.Dataframe(label="Data Preview", type="pandas")
+    strategies_plot = gr.Plot(label="Strategies")
+    prices_plot = gr.Plot(label="Prices")
+
+    # Collega bottone agli output
+    submit_btn.click(
+        fn=show_strategy,
+        inputs=[dropdown_companies, radio_aggregation],
+        outputs=[data_table, strategies_plot, prices_plot]
+    )
 
 demo.launch()
-
-
+`