Update app.py

app.py

@@ -1,7 +1,70 @@
+
 import gradio as gr
-from huggingface_hub import InferenceClient
+import os
+import json
+from datetime import datetime, date
+from openai import OpenAI
+
+# ----------------------------------------------------------------------
+# Helper to read secrets from the HF Space environment
+# ----------------------------------------------------------------------
+def _secret(key: str, fallback: str = None) -> str:
+    val = os.getenv(key)
+    if val is not None:
+        return val
+    if fallback is not None:
+        return fallback
+    raise RuntimeError(f"Secret '{key}' not found. Please add it to your Space secrets.")
+
+# ----------------------------------------------------------------------
+# User Management
+# ----------------------------------------------------------------------
+def load_users():
+    """Load users from secrets or environment variables"""
+    users = {}
+    
+    # Try to load from JSON string
+    users_json = _secret("CHAT_USERS", "{}")
+    try:
+        users_data = json.loads(users_json)
+        for username, password in users_data.items():
+            users[username] = password
+    except:
+        pass
+    
+   
+    return users
+
+# Load users
+VALID_USERS = load_users()
+
+def authenticate_user(username, password):
+    """Authenticate user against the valid users dictionary"""
+    return username in VALID_USERS and VALID_USERS[username] == password
 
+# ----------------------------------------------------------------------
+# Configuration
+# ----------------------------------------------------------------------
+# Available models with their respective API configurations
+MODELS = {
+    "GPT-OSS-20B (Hugging Face)": {
+        "provider": "huggingface",
+        "model_name": "openai/gpt-oss-20b:nscale",
+        "api_url": "https://router.huggingface.co/v1"
+    },
+    "Nvidia": {
+        "provider": "openrouter", 
+        "model_name": "nvidia/nemotron-nano-9b-v2:free",
+        "api_url": "https://openrouter.ai/api/v1"
+    }
+}
 
+# Get model display names for dropdown
+MODEL_NAMES = list(MODELS.keys())
+
+# ----------------------------------------------------------------------
+# Core Chat Logic
+# ----------------------------------------------------------------------
 def respond(
     message,
     history: list[dict[str, str]],
@@ -9,62 +72,156 @@ def respond(
     max_tokens,
     temperature,
     top_p,
-    hf_token: gr.OAuthToken,
+    selected_model,
 ):
     """
-    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
+    Handle chat responses using the selected model
     """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+    try:
+        # Check expiration (optional - remove if not needed)
+        # end_date = datetime.strptime(_secret("END_DATE", "2026-12-31"), "%Y-%m-%d").date()
+        # if date.today() > end_date:
+        #     yield "Chatbot has expired."
+        #     return
+            
+        # Get model configuration
+        model_config = MODELS[selected_model]
+        provider = model_config["provider"]
+        
+        # Get API key based on provider
+        if provider == "huggingface":
+            api_key = _secret("HF_TOKEN")
+        else:  # openrouter
+            api_key = _secret("OPENROUTER_KEY")
+        
+        # Configure client
+        client = OpenAI(
+            base_url=model_config["api_url"],
+            api_key=api_key
+        )
 
-    messages = [{"role": "system", "content": system_message}]
+        # Prepare messages
+        messages = [{"role": "system", "content": system_message}]
+        messages.extend(history)
+        messages.append({"role": "user", "content": message})
 
-    messages.extend(history)
+        # Make the API call
+        response = client.chat.completions.create(
+            model=model_config["model_name"],
+            messages=messages,
+            max_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            stream=True,
+        )
 
-    messages.append({"role": "user", "content": message})
+        # Stream the response
+        full_response = ""
+        for chunk in response:
+            if chunk.choices[0].delta.content is not None:
+                token = chunk.choices[0].delta.content
+                full_response += token
+                yield full_response
+                
+    except Exception as e:
+        print(f"Error in respond function: {e}")
+        yield f"Error: {str(e)}"
 
-    response = ""
+# ----------------------------------------------------------------------
+# Custom Auth Function for Gradio
+# ----------------------------------------------------------------------
+def gradio_auth(username, password):
+    """Custom authentication function for Gradio"""
+    return authenticate_user(username, password)
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
+# ----------------------------------------------------------------------
+# UI Layout
+# ----------------------------------------------------------------------
+# Tips section
+tips_md = """
 
-        response += token
-        yield response
+"""
 
+# Footer
+footer_md = """
+---
+**Providers**: Hugging Face Inference API + OpenRouter, dipilih providers yang tidak menggunakan prompt untuk training data.  
 
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    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)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
+
+# Create the chat interface
+with gr.Blocks(
+    title="Multi-Model Chat Interface",
+    theme=gr.themes.Soft()
+) as demo:
+    
+    gr.Markdown("# AI Chat")
+    gr.Markdown("Model yang digunakan gratis.  Data tidak digunakan untuk training.")
+    
+    # Model selection and settings in sidebar
     with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+        gr.Markdown("### ⚙️ Configuration")
+        
+        # Model selection
+        selected_model = gr.Dropdown(
+            choices=MODEL_NAMES,
+            value=MODEL_NAMES[0],
+            label="Select Model",
+            info="Choose which AI model to use"
+        )
+        
+        # Display current user (if available)
+        current_user = gr.Textbox(
+            label="Current User",
+            value="Authenticated User",
+            interactive=False,
+            visible=False  # Hide by default, can set to True if you want to show
+        )
+        
+        # Advanced settings
+        with gr.Accordion("Advanced Settings", open=False):
+            system_message = gr.Textbox(
+                value="Respon dalam Bahasa Indonesia.",
+                label="System Message",
+                info="Controls the AI's behavior and personality"
+            )
+            max_tokens = gr.Slider(
+                minimum=1, maximum=8096, value=4096, step=1, 
+                label="Max New Tokens",
+                info="Jumlah token respon maksimum."
+            )
 
+    
+    # Main chat interface
+    chatbot = gr.ChatInterface(
+        respond,
+        type="messages",
+        additional_inputs=[
+            system_message,
+            max_tokens,
+            selected_model,
+        ],
+        examples=[
+            [""],
+            ["Explain quantum computing in simple terms"],
+            ["What are the advantages of using open-source AI models?"]
+        ],
+        cache_examples=False,
+    )
+    
+    # Tips and footer
+    gr.Markdown(tips_md)
+    gr.Markdown(footer_md)
 
+# ----------------------------------------------------------------------
+# Launch with Custom Auth
+# ----------------------------------------------------------------------
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(
+        auth=gradio_auth,  # Use our custom auth function
+        auth_message="Please login to access the chat interface",
+        server_name="0.0.0.0",
+        ssr_mode=False,
+        server_port=7860,
+        show_error=True
+    )