Update app.py

app.py

@@ -52,6 +52,8 @@ import numpy as np
 from gradio_client import Client
 from huggingface_hub import InferenceClient
 
+SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret')
+
 # This will trigger downloading model
 print("Downloading if not downloaded Coqui XTTS V2")
 
@@ -612,7 +614,11 @@ latent_map["Pirate"] = get_latents("voices/pirate_by_coqui.wav")
 latent_map["Thera"] = get_latents("voices/thera-1.wav")
 
 # Define the main function for the API endpoint that takes the input text and chatbot role
-def generate_story_and_speech(input_text, chatbot_role):
+def generate_story_and_speech(secret_token, input_text, chatbot_role):
+    if secret_token != SECRET_TOKEN:
+        raise gr.Error(
+            f'Invalid secret token. Please fork the original space if you want to use it for yourself.')
+        
     # Initialize a list of lists for history with the user input as the first entry
     history = [[input_text, None]]
     story_sentences = get_sentence(history, chatbot_role)  # get_sentence function generates text
@@ -645,7 +651,7 @@ def generate_story_and_speech(input_text, chatbot_role):
 # Create a Gradio Interface using only the `generate_story_and_speech()` function and the 'json' output type
 demo = gr.Interface(
     fn=generate_story_and_speech,
-    inputs=[gr.Textbox(placeholder="Enter your text here"), gr.Dropdown(choices=ROLES, label="Select Chatbot Role")],
+    inputs=[gr.Text(label='Secret Token'),gr.Textbox(placeholder="Enter your text here"), gr.Dropdown(choices=ROLES, label="Select Chatbot Role")],
     outputs="json"
 )