Update app.py

app.py

@@ -1,80 +1,35 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
 import os
-import requests
-
-# Set up the inference API client
-hf_client = InferenceClient("meta-llama/Meta-Llama-3.1-70B-Instruct", token=os.getenv("HF_TOKEN"))
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    system_prefix = """
-If the input language is Korean, respond in Korean. If it's English, respond in English.
-Do not output in both languages simultaneously. Always respond in Korean to Korean questions and in English to English questions.
-    """
-    
-    messages = [{"role": "system", "content": f"{system_prefix} {system_message}"}]
-
-    # Ensure alternating user/assistant messages
-    for user_msg, assistant_msg in history:
-        messages.append({"role": "user", "content": user_msg})
-        if assistant_msg:  # Only add assistant message if it exists
-            messages.append({"role": "assistant", "content": assistant_msg})
-
-    # Add the current user message
-    messages.append({"role": "user", "content": message})
-
-    response = ""
+import sys
+import streamlit as st
+from tempfile import NamedTemporaryFile
 
+def main():
     try:
-        for message in hf_client.chat_completion(
-            messages,
-            max_tokens=max_tokens,
-            stream=True,
-            temperature=temperature,
-            top_p=top_p,
-        ):
-            token = message.choices[0].delta.content
-            if token is not None:
-                response += token.strip("")
-            yield response
+        # Get the code from secrets
+        code = os.environ.get("MAIN_CODE")
+        
+        if not code:
+            st.error("⚠️ The application code wasn't found in secrets. Please add the MAIN_CODE secret.")
+            return
+        
+        # Create a temporary Python file
+        with NamedTemporaryFile(suffix='.py', delete=False, mode='w') as tmp:
+            tmp.write(code)
+            tmp_path = tmp.name
+        
+        # Execute the code
+        exec(compile(code, tmp_path, 'exec'), globals())
+        
+        # Clean up the temporary file
+        try:
+            os.unlink(tmp_path)
+        except:
+            pass
+            
     except Exception as e:
-        yield f"An error occurred: {str(e)}"
-
-
-theme = "Nymbo/Nymbo_Theme"
-
-css = """
-footer {
-    visibility: hidden;
-}
-"""
-
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="""
-You are an AI assistant.
-""", label="System Prompt"),
-        gr.Slider(minimum=1, maximum=2000, 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)",
-        ),
-    ],
-    theme=theme,  # Apply theme
-    css=css  # Apply CSS
-)
+        st.error(f"⚠️ Error loading or executing the application: {str(e)}")
+        import traceback
+        st.code(traceback.format_exc())
 
 if __name__ == "__main__":
-    demo.launch()
+    main()