Deploy Gradio app with multiple files

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+import torch
+
+
+class VibeThinker:
+    def __init__(self, model_path):
+        self.model_path = model_path
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.model_path,
+            low_cpu_mem_usage=True,
+            torch_dtype=torch.bfloat16,
+            device_map="auto"
+        )
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True)
+
+    def infer_text(self, messages):
+        text = self.tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+        )
+        model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device)
+
+        generation_config = dict(
+            max_new_tokens=4096,
+            do_sample=True,
+            temperature=0.6,
+            top_p=0.95,
+            top_k=-1
+        )
+        
+        generated_ids = self.model.generate(
+            **model_inputs,
+            generation_config=GenerationConfig(**generation_config)
+        )
+        
+        generated_ids = [
+            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+        ]
+
+        response = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        return response
+
+
+# Initialize the model
+print("Loading VibeThinker model...")
+vibe_model = VibeThinker('WeiboAI/VibeThinker-1.5B')
+print("Model loaded successfully!")
+
+
+def respond(message, history):
+    """
+    Generate response for the chatbot.
+    
+    Args:
+        message: The user's current message
+        history: List of previous conversation messages in [user, assistant] format
+    """
+    # Convert history to messages format
+    messages = []
+    for user_msg, assistant_msg in history:
+        messages.append({"role": "user", "content": user_msg})
+        messages.append({"role": "assistant", "content": assistant_msg})
+    
+    # Add current message
+    messages.append({"role": "user", "content": message})
+    
+    # Generate response
+    response = vibe_model.infer_text(messages)
+    
+    return response
+
+
+# Create the Gradio interface
+with gr.Blocks(
+    theme=gr.themes.Soft(),
+    css="""
+    .header-link { text-decoration: none; color: inherit; }
+    .header-link:hover { text-decoration: underline; }
+    """
+) as demo:
+    gr.Markdown(
+        """
+        # 💭 VibeThinker Chatbot
+        Chat with [WeiboAI/VibeThinker-1.5B](https://huggingface.co/WeiboAI/VibeThinker-1.5B) - a powerful conversational AI model.
+        
+        <a href="https://huggingface.co/spaces/akhaliq/anycoder" class="header-link">Built with anycoder</a>
+        """
+    )
+    
+    chatbot = gr.ChatInterface(
+        fn=respond,
+        type="messages",
+        title="",
+        description="Ask me anything! I'm powered by VibeThinker.",
+        examples=[
+            "What is the meaning of life?",
+            "Explain quantum computing in simple terms",
+            "Write a short poem about artificial intelligence",
+            "How can I improve my productivity?",
+        ],
+        cache_examples=False,
+        retry_btn=None,
+        undo_btn=None,
+        clear_btn="Clear Chat",
+    )
+    
+    gr.Markdown(
+        """
+        ### About VibeThinker
+        VibeThinker is a 1.5B parameter conversational AI model designed for engaging and thoughtful conversations.
+        The model uses temperature sampling (0.6) for balanced creativity and coherence.
+        """
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+transformers
+torch
+accelerate
+bitsandbytes
+sentencepiece
+protobuf