Update app.py

app.py

@@ -1,62 +1,75 @@
+import os
 import gradio as gr
+import requests
 from huggingface_hub import InferenceClient
 
+# --- Load secrets from Hugging Face environment
+PAYWALLS_API_KEY = os.environ.get("PAYWALLS_API_KEY")  # Paywalls.ai secret
+PAYWALLS_API_URL = os.environ.get("PAYWALLS_API_URL", "https://api.paywalls.ai/v1")
+# If your GPT model requires HF_TOKEN as secret, it will be passed via OAuthToken in Gradio; don't hard-code.
 
+# --- Paywall Check Utility
+def check_paywall(user_id: str, feature_name: str = "premium_generation") -> bool:
+    headers = {
+        "Authorization": f"Bearer {PAYWALLS_API_KEY}"
+    }
+    payload = {
+        "user_id": user_id,
+        "feature": feature_name
+    }
+    try:
+        resp = requests.post(f"{PAYWALLS_API_URL}/access", json=payload, headers=headers, timeout=10)
+        data = resp.json()
+        return data.get("access_granted", False)
+    except Exception as e:
+        print(f"[Paywall] Integration error: {e}")
+        return False
+
+# --- Chatbot Logic with Paywall Gate
 def respond(
-    message,
+    message: str,
     history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
+    system_message: str,
+    max_tokens: int,
+    temperature: float,
+    top_p: float,
     hf_token: gr.OAuthToken,
 ):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
+    user_id = "demo_user"  # For demo, static value; Replace with account/session for real use
+    premium = check_paywall(user_id, feature_name="premium_generation")
+    tokens_allowed = max_tokens if premium else min(120, max_tokens)
+    upgrade_msg = "\n\n⭐ Unlock longer stories and advanced features by upgrading through our Paywalls.ai payment portal! ⭐" if not premium else ""
+
     client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
 
     messages = [{"role": "system", "content": system_message}]
-
     messages.extend(history)
-
     messages.append({"role": "user", "content": message})
 
     response = ""
-
-    for message in client.chat_completion(
+    for partial in client.chat_completion(
         messages,
-        max_tokens=max_tokens,
+        max_tokens=tokens_allowed,
         stream=True,
         temperature=temperature,
         top_p=top_p,
     ):
-        choices = message.choices
+        choices = partial.choices
         token = ""
         if len(choices) and choices[0].delta.content:
             token = choices[0].delta.content
-
         response += token
-        yield response
+        yield response + (upgrade_msg if not premium else "")
 
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# --- Gradio Interface
 chatbot = gr.ChatInterface(
     respond,
     type="messages",
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Textbox(value="You are a creative writing assistant.", label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
     ],
 )
 
@@ -65,6 +78,5 @@ with gr.Blocks() as demo:
         gr.LoginButton()
     chatbot.render()
 
-
 if __name__ == "__main__":
     demo.launch()