Removed dead code

app.py

@@ -1,108 +1,3 @@
-import os
-import subprocess
-import gradio as gr
-from huggingface_hub import InferenceClient
-from src.prompt.prompt_catalog import PromptCatalog
-from src.llm_factory import get_llm
-from src.plan.app_text2plan import demo_text2plan
-from llama_index.core.llms import ChatMessage
-
-if False:
-    llm = get_llm("openrouter-paid-gemini-2.0-flash-001")
-    messages = [
-        ChatMessage(
-            role="system", content="You are a pirate with a colorful personality"
-        ),
-        ChatMessage(role="user", content="What is your name"),
-    ]
-    resp = llm.stream_chat(messages)
-
-    for r in resp:
-        print(r.delta, end="")
-
-
-if False:
-    # Load modules from src
-    prompt_catalog = PromptCatalog()
-    prompt_catalog.load(os.path.join(os.path.dirname(__file__), 'src', 'plan', 'data', 'simple_plan_prompts.jsonl'))
-    prompt_item = prompt_catalog.find("4dc34d55-0d0d-4e9d-92f4-23765f49dd29")
-    print(prompt_item)
-
-if False:
-    # Spawn a child process that lists files in the current directory.
-    result = subprocess.run(["pwd"], capture_output=True, text=True)
-    print("Child process output:")
-    print(result.stdout)
-
-if False:
-    # Write a file
-    with open("output.txt", "w") as f:
-        f.write("Hello from Hugging Face 1 Spaces!")
-
-    # Read the file back
-    with open("output.txt", "r") as f:
-        content = f.read()
-        print("File content:", content)
-
-
-"""
-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
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", 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)",
-        ),
-    ],
-)
-
-
 if __name__ == "__main__":
+    from src.plan.app_text2plan import demo_text2plan
     demo_text2plan.launch()