Update app.py

app.py

@@ -7,9 +7,6 @@ matplotlib.use('Agg') # Set backend for Matplotlib to avoid GUI conflicts with G
 import matplotlib.pyplot as plt
 import time # For profiling if needed
 
-# --- Add Gemini Import ---
-from google import genai
-
 # --- Module Imports ---
 from gradio_utils import get_url_user_token
 
@@ -163,44 +160,6 @@ def update_analytics_plots_figures(token_state_value, date_filter_option, custom
         return [error_msg] + placeholder_figs
 
 
-# --- Gemini Test Function (simple as requested, with prompt input) ---
-def call_gemini_api_direct_with_prompt(user_prompt):
-    """
-    Calls the Gemini API with the provided prompt and API key.
-    This function performs a direct API call without explicit try-except blocks
-    for the API interaction itself, as per the request.
-    """
-    # WARNING: Hardcoding API keys is not recommended for production. 
-    # Consider using environment variables or a secure config management system.
-    api_key = "AIzaSyCSfIw2kWWO1c_oa7WQ33h11O6N8egNoqk" # User provided API key
-    
-    if not user_prompt:
-        logging.warning("Gemini API call: Prompt is empty.")
-        return "Per favore, inserisci un prompt."
-
-    try:
-        # Configure the genai library with the API key
-        
-        # Specify the model name as requested
-        model_name = "gemini-2.5-flash-preview-05-20" 
-        
-        client = genai.Client(api_key="AIzaSyCSfIw2kWWO1c_oa7WQ33h11O6N8egNoqk")
-
-        response = client.models.generate_content(
-            model=model_name, contents="Explain how AI works in a few words"
-        )
-                
-        # Log the response text (optional, for server-side debugging)
-        logging.info(f"Gemini API call successful. Prompt: . Response: '{response.text[:100]}...'")
-        
-        return response.text
-    except Exception as e:
-        # Catching exceptions here to provide a user-friendly message in the UI
-        # instead of letting the Gradio app crash or show a raw error.
-        logging.error(f"Error calling Gemini API: {e}", exc_info=True)
-        return f"Errore durante la chiamata API Gemini: {str(e)}"
-
-
 # --- Gradio UI Blocks ---
 with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
                title="LinkedIn Organization Dashboard") as app:
@@ -234,37 +193,6 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
             sync_status_html_output = gr.HTML("<p style='text-align:center;'>Stato sincronizzazione...</p>")
             dashboard_display_html = gr.HTML("<p style='text-align:center;'>Caricamento dashboard...</p>")
 
-            # --- Gemini API Test Section ---
-            gr.Markdown("---") # Visual separator
-            gr.Markdown("## 🧪 Test Gemini API (Semplice)")
-            gr.Markdown("Inserisci un prompt e clicca il pulsante per testare la chiamata all'API Gemini.")
-            
-            with gr.Row():
-                gemini_test_prompt_input = gr.Textbox(
-                    label="Prompt per Gemini:", 
-                    value="Explain how AI works in a few words", 
-                    lines=2,
-                    scale=3
-                )
-                gemini_test_btn = gr.Button("🚀 Esegui Test Gemini", scale=1, variant="secondary")
-            
-            gemini_output_textbox = gr.Textbox(
-                label="Risposta da Gemini:", 
-                interactive=False, 
-                lines=5, 
-                show_label=True,
-                placeholder="La risposta di Gemini apparirà qui..."
-            )
-
-            # Connect the button to the Gemini API call function
-            gemini_test_btn.click(
-                fn=call_gemini_api_direct_with_prompt,
-                inputs=[gemini_test_prompt_input],
-                outputs=[gemini_output_textbox],
-                api_name="test_gemini_simple" # Optional: for API access if needed by Gradio client
-            )
-            # --- End Gemini API Test Section ---
-
             org_urn_display.change(
                 fn=initial_load_sequence,
                 inputs=[url_user_token_display, org_urn_display, token_state],
@@ -615,6 +543,4 @@ if __name__ == "__main__":
     except ImportError:
         logging.error("Matplotlib non è installato. I grafici non verranno generati.")
 
-    # Ensure you have the google-generativeai library installed:
-    # pip install google-generativeai
-    app.launch(server_name="0.0.0.0", server_port=7860, debug=True)
+    app.launch(server_name="0.0.0.0", server_port=7860, debug=True)