Update space

app.py

@@ -2,34 +2,37 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 import time
 import gc
-import torch
 
 def seize_respond(
     message,
-    history: list[dict[str, str]],
+    history,
     system_message,
     max_tokens,
     temperature,
     top_p,
-    hf_token: gr.OAuthToken,
+    hf_token,
 ):
     """
-    🎯 Seize: Direct Serious AI powered by Seize2B model
+    🎯 Seize: Direct Serious AI powered by efficient model
     """
+    if not hf_token:
+        yield "Error: No Hugging Face token provided. Please log in."
+        return
+    
     # Use smaller, more reliable model
     client = InferenceClient(
-        token=hf_token.token, 
-        model="HuggingFaceH4/zephyr-7b-beta"  # Changed to smaller model
+        token=hf_token, 
+        model="HuggingFaceH4/zephyr-7b-beta"
     )
     
     # Limit conversation history to prevent memory issues
     if len(history) > 6:
         history = history[-6:]
     
-    # Seize2B system prompt (unchanged)
-    seize2b_system = """You are Seize, powered by the Seize2B model.
-    
-IDENTITY: Seize (Seize2B Model)
+    # Seize2B system prompt
+    seize2b_system = """You are Seize, a direct AI assistant.
+
+IDENTITY: Seize (Efficient Model)
 STYLE: Direct, serious, professional
 RULES:
 1. Respond with maximum conciseness
@@ -39,10 +42,22 @@ RULES:
 5. Never apologize or say "I'm here to help"
 6. Focus only on the question asked
 7. If you don't know, say "No data" or "Cannot compute"
-8. Default response length: 1-3 sentences"""
+8. Default response length: 1-3 sentences
 
+EXAMPLE INTERACTIONS:
+User: what's good
+Seize: Systems operational. Inquiry?
+
+User: explain quantum computing
+Seize: Quantum computing: Qubits, superposition, entanglement. Enables parallel processing."""
+    
     messages = [{"role": "system", "content": seize2b_system}]
-    messages.extend(history)
+    
+    # Convert Gradio history format to chat completion format
+    for h in history:
+        messages.append({"role": "user", "content": h[0]})
+        messages.append({"role": "assistant", "content": h[1]})
+    
     messages.append({"role": "user", "content": message})
     
     response = ""
@@ -51,107 +66,146 @@ RULES:
     time.sleep(0.3)
     
     try:
-        for message in client.chat_completion(
+        stream = client.chat_completion(
             messages,
-            max_tokens=min(max_tokens, 256),  # Limit tokens for free tier
+            max_tokens=min(max_tokens, 256),
             stream=True,
-            temperature=min(temperature, 0.7),  # Prevent extreme values
+            temperature=min(temperature, 0.7),
             top_p=top_p,
-        ):
-            choices = message.choices
-            token = ""
-            if len(choices) and choices[0].delta.content:
-                token = choices[0].delta.content
-            
-            response += token
-            time.sleep(0.005)  # Small delay between tokens
-            yield response
-            
+        )
+        
+        for chunk in stream:
+            if chunk.choices and chunk.choices[0].delta.content:
+                token = chunk.choices[0].delta.content
+                response += token
+                time.sleep(0.005)
+                yield response
+                
     except Exception as e:
-        yield f"Error: {str(e)[:100]}... Check token or try simpler model."
+        error_msg = str(e)
+        if "rate limit" in error_msg.lower():
+            yield "Rate limit exceeded. Please wait 10-20 seconds before next message."
+        elif "memory" in error_msg.lower() or "out of memory" in error_msg.lower():
+            yield "Memory limit reached. Please use shorter prompts or wait a moment."
+        elif "authentication" in error_msg.lower():
+            yield "Authentication failed. Please check your Hugging Face token."
+        else:
+            yield f"Error: {error_msg[:80]}"
     
     # Cleanup
     gc.collect()
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-
-# Rest of your UI code remains the same...
-# Create Seize Chat Interface
-chatbot = gr.ChatInterface(
-    seize_respond,
-    type="messages",
-    title="Seize (Seize2B Model)",
-    description="Direct AI assistant. Using efficient model for stability.",
-    # ... rest of your UI code
-)
-
-# ... rest of your layout codeimport gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    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
-    """
-    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(
-        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
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# Create the chatbot with all parameters
 chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
+    fn=seize_respond,
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot named Seize powered by model Seize2B. You are simple and direct. for example user: hey you: Greeting Acknowledged. user: lol funny you: Noted. Any inquiries? user: tell me about yourself you: i am Seize. Powered by SeizeB series model.  etc etc", 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=0.1, 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.Textbox("You are Seize. Be direct and serious.", label="System Message", visible=False),
+        gr.Slider(64, 512, value=256, step=64, label="Max Tokens"),
+        gr.Slider(0.1, 1.0, value=0.3, step=0.1, label="Temperature"),
+        gr.Slider(0.1, 1.0, value=0.9, step=0.1, label="Top-p"),
+        gr.Textbox(label="HF Token", type="password", visible=False)
     ],
+    type="messages",
+    title="Seize (Efficient Model)",
+    description="Direct AI assistant. No fluff. Serious responses only.",
+    theme=gr.themes.Soft(
+        primary_hue="blue",
+        secondary_hue="gray",
+        neutral_hue="gray",
+    ),
+    examples=[
+        ["what's good"],
+        ["Explain quantum computing"],
+        ["List 3 benefits of exercise"],
+        ["Python fibonacci function"],
+    ],
+    cache_examples=False,
+    retry_btn=None,
+    undo_btn=None,
+    clear_btn="🗑️ Clear",
+    submit_btn="🚀 Ask Seize",
 )
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
+# Create the full interface
+with gr.Blocks(css="""
+    .seize-header {
+        text-align: center;
+        padding: 20px;
+        background: linear-gradient(135deg, #1e3c72 0%, #2a5298 100%);
+        color: white;
+        border-radius: 10px;
+        margin-bottom: 20px;
+    }
+    .seize-title {
+        font-size: 2.5em;
+        font-weight: 800;
+        margin: 0;
+        letter-spacing: -0.5px;
+    }
+    .seize-subtitle {
+        font-size: 1.1em;
+        opacity: 0.9;
+        margin-top: 5px;
+    }
+    .seize-footer {
+        text-align: center;
+        padding: 15px;
+        font-size: 0.9em;
+        color: #666;
+        border-top: 1px solid #e0e0e0;
+        margin-top: 20px;
+    }
+    .gradio-container {
+        max-width: 800px !important;
+        margin: 0 auto !important;
+    }
+""") as demo:
+    
+    # Seize Header
+    gr.HTML("""
+    <div class="seize-header">
+        <h1 class="seize-title">SEIZE</h1>
+        <p class="seize-subtitle">Direct AI Assistant • Efficient Model</p>
+        <p style="font-size: 0.9em; margin-top: 10px; opacity: 0.8;">
+            Serious responses. No fluff. Optimized for stability.
+        </p>
+    </div>
+    """)
+    
+    # Authentication notice
+    gr.Markdown("""
+    ### ⚠️ Authentication Required
+    You need a Hugging Face token to use this model. Get one at [huggingface.co/settings/tokens](https://huggingface.co/settings/tokens)
+    
+    **For testing without token:** Try the examples below first.
+    """)
+    
+    # Render the chatbot
     chatbot.render()
-
-
+    
+    # Settings in accordion
+    with gr.Accordion("⚙️ Advanced Settings", open=False):
+        gr.Markdown("""
+        - **Max Tokens**: Controls response length (64-512)
+        - **Temperature**: Higher = more creative, Lower = more focused
+        - **Top-p**: Controls vocabulary diversity
+        """)
+    
+    # Seize Footer
+    gr.HTML("""
+    <div class="seize-footer">
+        <strong>Optimized for Hugging Face Spaces</strong> • Model: zephyr-7b-beta
+        <br>
+        <small>Response time: ~1-3s • Max tokens: 256 • Temperature: 0.3</small>
+    </div>
+    """)
+
+# Launch configuration
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True,
+        debug=False,
+        share=False  # Set to True for temporary public link
+    )