MCP ready

app.py

@@ -52,7 +52,7 @@ from transformers import pipeline
 
 pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto")
 
-@spaces.GPU(enable_queue=True)
+@spaces.GPU()
 def get_llm_idea(user_prompt):
     agent_maker_sys = f"""
 You are an AI whose job is to help users create their own chatbot whose personality will reflect the character and scene atmosphere from an image described by users.
@@ -86,6 +86,25 @@ Here's another example to help you, but only provide one on the end: If a user t
 
 
 def infer(image_in):
+    """
+    Generate a system prompt idea for a language model based on the content of an input image.
+
+    This function performs two steps:
+    1. It uses a vision-language model (Kosmos-2) to generate a descriptive caption of the input image.
+    2. It then uses a text generation pipeline (Zephyr-7B) to create a chatbot configuration from that caption,
+       including a title, system prompt, and example user message.
+
+    Args:
+        image_in (str): The filepath to an image representing a character, scene, or setting.
+
+    Returns:
+        Tuple[str, str]: 
+            - The generated caption describing the image.
+            - A suggested LLM system prompt structure including:
+                - A chatbot title
+                - A system message defining the bot’s personality
+                - An example user input message
+    """
     gr.Info("Getting image description...")
     """
     if cap_type == "Fictional" :
@@ -185,4 +204,4 @@ with gr.Blocks(css=css) as demo:
         ]
     )
 
-demo.queue().launch(show_api=False, show_error=True)
+demo.queue().launch(show_api=False, show_error=True, ssr_mode=False, mcp_server=True)