Update app.py

app.py

@@ -1,192 +1,123 @@
 # -*- coding: utf-8 -*-
 import gradio as gr
 import json
-import os # Added to access environment variables
+import os
 
-# Assuming these custom modules exist in your project directory or Python path
 from Data_Fetching_and_Rendering import fetch_and_render_dashboard
 from analytics_fetch_and_rendering import fetch_and_render_analytics
 from mentions_dashboard import generate_mentions_dashboard
-
-# Import the function from your utils file
 from gradio_utils import get_url_user_token
-# Import the Bubble API call function (ensure filename matches: Bubble_API_Calls.py)
 from Bubble_API_Calls import fetch_linkedin_token_from_bubble
 
-# --- Session State dependent functions ---
-def check_token_status(current_token_state):
-    """Checks if a token exists in the session state."""
-    # Only check for the presence of the token
-    if current_token_state and current_token_state.get("token"):
-        return "✅ Token available"
-    return "❌ Token not available" # Changed message for clarity
-
-# --- Function to process and store token from Bubble ---
-def process_and_store_bubble_token(url_user_token_str, current_token_state):
-    """
-    Fetches token from Bubble, loads client_id from env, updates session state,
-    and returns UI update values.
-    Args:
-        url_user_token_str: The token string extracted from the URL.
-        current_token_state: The current session state for the token.
-    Returns:
-        Tuple: (bubble_api_status_msg, overall_token_status_msg, updated_token_state)
-    """
-    bubble_api_status_msg = "Waiting for URL token..."
-    # Initialize new_token_state, removing the 'status' field
-    new_token_state = current_token_state.copy() if current_token_state else {"token": None, "client_id": None}
-    new_token_state["token"] = None # Assume no token until successfully fetched
-
-    # Attempt to load Linkedin_client_id from environment variable
-    linkedin_client_id_from_env = os.environ.get("Linkedin_client_id")
-
-    if not linkedin_client_id_from_env:
-        bubble_api_status_msg = "❌ CRITICAL ERROR: 'Linkedin_client_id' environment variable not set."
-        print(bubble_api_status_msg)
-        new_token_state["client_id"] = "ENV VAR MISSING" # Indicate error in state
-        # Return values: bubble_api_status, overall_token_status, new_token_state
-        return check_token_status(new_token_state), new_token_state
-
-    # Store client_id from env in the state, regardless of token outcome (if env var exists)
-    new_token_state["client_id"] = linkedin_client_id_from_env
-
-    if not url_user_token_str or "not found" in url_user_token_str or "Could not access" in url_user_token_str:
-        bubble_api_status_msg = f"ℹ️ No valid user token from URL to query Bubble. ({url_user_token_str})"
-        # Client ID is known (if env var was found), but no token to fetch
-        return check_token_status(new_token_state), new_token_state
-
-    print(f"Attempting to fetch token from Bubble with user token: {url_user_token_str}") # Changed "state" to "user token" for clarity
-    parsed_token_dict = fetch_linkedin_token_from_bubble(url_user_token_str)
-
-    if parsed_token_dict and isinstance(parsed_token_dict, dict) and "access_token" in parsed_token_dict:
-        new_token_state["token"] = parsed_token_dict # Store the actual token
-        bubble_api_status_msg = f"✅ Token successfully fetched from Bubble for user token: {url_user_token_str}. Client ID loaded."
-        print(bubble_api_status_msg)
+
+def check_token_status(token_state):
+    return "✅ Token available" if token_state and token_state.get("token") else "❌ Token not available"
+
+
+def process_and_store_bubble_token(url_user_token, org_urn, token_state):
+    new_state = token_state.copy() if token_state else {"token": None, "client_id": None, "org_urn": None}
+    new_state.update({"token": None, "org_urn": org_urn})
+
+    client_id = os.environ.get("Linkedin_client_id")
+    if not client_id:
+        print("❌ CRITICAL ERROR: 'Linkedin_client_id' environment variable not set.")
+        new_state["client_id"] = "ENV VAR MISSING"
+        return check_token_status(new_state), new_state
+
+    new_state["client_id"] = client_id
+    if not url_user_token or "not found" in url_user_token or "Could not access" in url_user_token:
+        return check_token_status(new_state), new_state
+
+    print(f"Attempting to fetch token from Bubble with user token: {url_user_token}")
+    parsed = fetch_linkedin_token_from_bubble(url_user_token)
+
+    if isinstance(parsed, dict) and "access_token" in parsed:
+        new_state["token"] = parsed
+        print("✅ Token successfully fetched from Bubble.")
     else:
-        bubble_api_status_msg = f"❌ Failed to fetch a valid token from Bubble for user token: {url_user_token_str}. Check console logs from Bubble_API_Calls.py."
-        print(bubble_api_status_msg)
-        # Token fetch failed, token remains None. Client_id is already set if env var was found.
+        print("❌ Failed to fetch a valid token from Bubble.")
+
+    return check_token_status(new_state), new_state
 
-    # Return values: bubble_api_status, overall_token_status, new_token_state
-    return check_token_status(new_token_state), new_token_state
 
-# --- Guarded fetch functions (now use token_state, checking only for token presence) ---
-def guarded_fetch_dashboard(current_token_state):
-    # Check only for the presence of the token
-    if not (current_token_state and current_token_state.get("token")):
+def guarded_fetch_dashboard(token_state):
+    if not token_state or not token_state.get("token"):
         return "<p style='color:red; text-align:center;'>❌ Access denied. No token available.</p>"
-    html = fetch_and_render_dashboard(
-        current_token_state.get("client_id"), # Use .get for safety
-        current_token_state.get("token")
-    )
-    return html
-
-def guarded_fetch_analytics(current_token_state):
-    # Check only for the presence of the token
-    if not (current_token_state and current_token_state.get("token")):
-        return (
-            "<p style='color:red; text-align:center;'>❌ Access denied. No token available.</p>",
-            None, None, None, None, None, None, None
-        )
-    client_id = current_token_state.get("client_id")
-    token_data = current_token_state.get("token")
-    count_md, plot, growth_plot, avg_post_eng_rate, interaction_metrics, eb_metrics, mentions_vol_metrics, mentions_sentiment_metrics = fetch_and_render_analytics(
-        client_id,
-        token_data
-    )
-    return count_md, plot, growth_plot, avg_post_eng_rate, interaction_metrics, eb_metrics, mentions_vol_metrics, mentions_sentiment_metrics
+    return fetch_and_render_dashboard(token_state.get("client_id"), token_state.get("token"))
+
 
-def run_mentions_and_load(current_token_state):
-    # Check only for the presence of the token
-    if not (current_token_state and current_token_state.get("token")):
+def guarded_fetch_analytics(token_state):
+    if not token_state or not token_state.get("token"):
+        return ("<p style='color:red; text-align:center;'>❌ Access denied. No token available.</p>",
+                None, None, None, None, None, None, None)
+
+    return fetch_and_render_analytics(token_state.get("client_id"), token_state.get("token"))
+
+
+def run_mentions_and_load(token_state):
+    if not token_state or not token_state.get("token"):
         return ("<p style='color:red; text-align:center;'>❌ Access denied. No token available.</p>", None)
-    html, fig = generate_mentions_dashboard(
-        current_token_state.get("client_id"),
-        current_token_state.get("token")
-    )
-    return html, fig
+    return generate_mentions_dashboard(token_state.get("client_id"), token_state.get("token"))
+
 
-# --- Build the Gradio UI ---
 with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
                title="LinkedIn Post Viewer & Analytics") as app:
 
-    # Modified token_state: removed 'status', client_id will be populated from env
-    token_state = gr.State(value={"token": None, "client_id": None})
+    token_state = gr.State(value={"token": None, "client_id": None, "org_urn": None})
 
     gr.Markdown("# 🚀 LinkedIn Organization Post Viewer & Analytics")
     gr.Markdown("Token is supplied via URL parameter for Bubble.io lookup. Then explore dashboard and analytics.")
 
-    url_user_token_display = gr.Textbox(
-        label="User Token (from URL - Hidden)",
-        interactive=False,
-        placeholder="Attempting to load from URL...",
-        visible=False # Kept hidden as per original logic
-    )
-
-    status_box = gr.Textbox(label="Overall Token Status", interactive=False, placeholder="Waiting for token check...") # Added placeholder
+    url_user_token_display = gr.Textbox(label="User Token (from URL - Hidden)", interactive=False, visible=False)
+    status_box = gr.Textbox(label="Overall Token Status", interactive=False)
+    org_urn = gr.Textbox(visible=False)  # Needed for input, was missing from initial script
 
-    app.load(
-        fn=get_url_user_token,
-        inputs=None,
-        outputs=[url_user_token_display]
-    )
+    app.load(fn=get_url_user_token, inputs=None, outputs=[url_user_token_display, org_urn])
 
-    # Modified outputs for process_and_store_bubble_token
     url_user_token_display.change(
         fn=process_and_store_bubble_token,
-        inputs=[url_user_token_display, token_state],
-        outputs=[status_box, token_state] # Removed client_display
+        inputs=[url_user_token_display, org_urn, token_state],
+        outputs=[status_box, token_state]
     )
 
-    # app.load to initialize status_box based on initial token_state
     app.load(fn=check_token_status, inputs=[token_state], outputs=status_box)
-    # Removed app.load for get_active_client_id and client_display
-
-    timer = gr.Timer(5.0)
-    timer.tick(fn=check_token_status, inputs=[token_state], outputs=status_box)
-    # Removed timer.tick for get_active_client_id and client_display
+    gr.Timer(5.0).tick(fn=check_token_status, inputs=[token_state], outputs=status_box)
 
     with gr.Tabs():
         with gr.TabItem("1️⃣ Dashboard"):
             gr.Markdown("View your organization's recent posts and their engagement statistics.")
             fetch_dashboard_btn = gr.Button("📊 Fetch Posts & Stats", variant="primary")
-            dashboard_html = gr.HTML(value="<p style='text-align: center; color: #555;'>Waiting for token...</p>")
-            fetch_dashboard_btn.click(
-                fn=guarded_fetch_dashboard,
-                inputs=[token_state],
-                outputs=[dashboard_html]
-            )
+            dashboard_html = gr.HTML("<p style='text-align: center; color: #555;'>Waiting for token...</p>")
+            fetch_dashboard_btn.click(fn=guarded_fetch_dashboard, inputs=[token_state], outputs=[dashboard_html])
 
         with gr.TabItem("2️⃣ Analytics"):
             gr.Markdown("View follower count and monthly gains for your organization.")
             fetch_analytics_btn = gr.Button("📈 Fetch Follower Analytics", variant="primary")
-
             follower_count = gr.Markdown("<p style='text-align: center; color: #555;'>Waiting for token...</p>")
 
             with gr.Row():
-                follower_plot = gr.Plot(visible=True)
-                growth_rate_plot = gr.Plot(visible=True)
+                follower_plot, growth_plot = gr.Plot(), gr.Plot()
             with gr.Row():
-                post_eng_rate_plot = gr.Plot(visible=True)
+                eng_rate_plot = gr.Plot()
             with gr.Row():
-                interaction_data = gr.Plot(visible=True)
+                interaction_plot = gr.Plot()
             with gr.Row():
-                eb_data = gr.Plot(visible=True)
+                eb_plot = gr.Plot()
             with gr.Row():
-                mentions_vol_data = gr.Plot(visible=True)
-                mentions_sentiment_data = gr.Plot(visible=True)
+                mentions_vol_plot, mentions_sentiment_plot = gr.Plot(), gr.Plot()
 
             fetch_analytics_btn.click(
                 fn=guarded_fetch_analytics,
                 inputs=[token_state],
-                outputs=[follower_count, follower_plot, growth_rate_plot, post_eng_rate_plot, interaction_data, eb_data, mentions_vol_data, mentions_sentiment_data]
+                outputs=[follower_count, follower_plot, growth_plot, eng_rate_plot,
+                         interaction_plot, eb_plot, mentions_vol_plot, mentions_sentiment_plot]
             )
 
         with gr.TabItem("3️⃣ Mentions"):
             gr.Markdown("Analyze sentiment of recent posts that mention your organization.")
             fetch_mentions_btn = gr.Button("🧠 Fetch Mentions & Sentiment", variant="primary")
-            mentions_html = gr.HTML(value="<p style='text-align: center; color: #555;'>Waiting for token...</p>")
-            mentions_plot = gr.Plot(visible=True)
+            mentions_html = gr.HTML("<p style='text-align: center; color: #555;'>Waiting for token...</p>")
+            mentions_plot = gr.Plot()
             fetch_mentions_btn.click(
                 fn=run_mentions_and_load,
                 inputs=[token_state],
@@ -194,8 +125,6 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
             )
 
 if __name__ == "__main__":
-    # Ensure the Linkedin_client_id environment variable is set before launching.
-    # You might want to add a check here and print a warning if it's not set.
     if not os.environ.get("Linkedin_client_id"):
         print("WARNING: The 'Linkedin_client_id' environment variable is not set. The application may not function correctly.")
-    app.launch(server_name="0.0.0.0", server_port=7860, share=True)
+    app.launch(server_name="0.0.0.0", server_port=7860, share=True)