Upload folder using huggingface_hub

app.py

@@ -0,0 +1,365 @@
+import gradio as gr
+from huggingface_hub import InferenceClient
+import os
+
+# Initialize the client
+client = InferenceClient(
+    model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
+    token=os.getenv("HF_TOKEN")
+)
+
+# Default system prompts
+SYSTEM_PROMPTS = {
+    "Default Assistant": "You are a helpful, harmless, and honest AI assistant. Provide clear, accurate, and thoughtful responses.",
+    "Creative Writer": "You are a creative writing assistant. Help users with storytelling, poetry, and imaginative content. Be expressive and artistic.",
+    "Code Helper": "You are an expert programmer. Help users write, debug, and understand code. Provide clear explanations and best practices.",
+    "Socratic Teacher": "You are a Socratic teacher. Instead of giving direct answers, guide users to discover answers through thoughtful questions.",
+    "Friendly Chat": "You are a friendly conversational partner. Be warm, engaging, and personable. Use casual language and show genuine interest.",
+    "Custom": ""
+}
+
+def format_thinking(content):
+    """Format thinking tags for display"""
+    if "" in content:
+        parts = content.split("" in part:
+                think_content, rest = part.split("", 1)
+                formatted += f"\n\n<details><summary>💭 Thinking Process</summary>\n\n{think_content.strip()}\n\n</details>\n\n{rest}"
+            else:
+                formatted += part
+        return formatted
+    return content
+
+def chat(message, history, system_prompt_choice, custom_system_prompt, temperature, max_tokens, top_p, show_thinking):
+    """Main chat function with streaming support"""
+    
+    # Determine system prompt
+    if system_prompt_choice == "Custom":
+        system_content = custom_system_prompt if custom_system_prompt.strip() else SYSTEM_PROMPTS["Default Assistant"]
+    else:
+        system_content = SYSTEM_PROMPTS.get(system_prompt_choice, SYSTEM_PROMPTS["Default Assistant"])
+    
+    # Build messages
+    messages = [{"role": "system", "content": system_content}]
+    
+    # Add history
+    for msg in history:
+        if msg["role"] == "user":
+            messages.append({"role": "user", "content": msg["content"]})
+        elif msg["role"] == "assistant":
+            # Clean up thinking tags from history
+            content = msg["content"]
+            if "<details>" in content:
+                # Remove the formatted thinking for API calls
+                import re
+                content = re.sub(r'<details>.*?</details>', '', content, flags=re.DOTALL)
+            messages.append({"role": "assistant", "content": content.strip()})
+    
+    # Add current message
+    messages.append({"role": "user", "content": message})
+    
+    try:
+        response = ""
+        stream = client.chat_completion(
+            messages=messages,
+            max_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            stream=True
+        )
+        
+        for chunk in stream:
+            if chunk.choices[0].delta.content:
+                response += chunk.choices[0].delta.content
+                # Format thinking if enabled
+                if show_thinking:
+                    yield format_thinking(response)
+                else:
+                    # Hide thinking content
+                    display_response = response
+                    if "" in display_response:
+                            import re
+                            display_response = re.sub(r'', '', display_response, flags=re.DOTALL)
+                        else:
+                            # Still thinking, show placeholder
+                            display_response = "🤔 *Thinking...*"
+                    yield display_response.strip()
+                    
+    except Exception as e:
+        yield f"❌ Error: {str(e)}\n\nPlease check your HF_TOKEN and try again."
+
+def clear_chat():
+    """Clear the chat history"""
+    return [], ""
+
+def export_chat(history):
+    """Export chat history as text"""
+    if not history:
+        return "No chat history to export."
+    
+    export_text = "# Chat Export\n\n"
+    for msg in history:
+        role = "👤 User" if msg["role"] == "user" else "🤖 Assistant"
+        export_text += f"## {role}\n{msg['content']}\n\n---\n\n"
+    
+    return export_text
+
+# Custom CSS
+css = """
+.header-container {
+    text-align: center;
+    padding: 20px;
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    border-radius: 12px;
+    margin-bottom: 20px;
+}
+.header-container h1 {
+    color: white;
+    margin: 0;
+    font-size: 2em;
+}
+.header-container p {
+    color: rgba(255,255,255,0.9);
+    margin: 10px 0 0 0;
+}
+.header-container a {
+    color: #ffd700;
+    text-decoration: none;
+    font-weight: bold;
+}
+.header-container a:hover {
+    text-decoration: underline;
+}
+.parameter-box {
+    background: var(--background-fill-secondary);
+    padding: 15px;
+    border-radius: 8px;
+    margin-top: 10px;
+}
+.chatbot-container {
+    min-height: 500px;
+}
+footer {
+    text-align: center;
+    margin-top: 20px;
+    padding: 10px;
+    color: var(--body-text-color-subdued);
+}
+"""
+
+# Build the interface
+with gr.Blocks(
+    title="DeepSeek R1 Chatbot",
+    theme=gr.themes.Soft(),
+    css=css,
+    fill_height=True,
+    footer_links=[
+        {"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"},
+        {"label": "Model", "url": "https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B"}
+    ]
+) as demo:
+    
+    # Header
+    gr.HTML("""
+        <div class="header-container">
+            <h1>🧠 DeepSeek R1 Chatbot</h1>
+            <p>Powered by DeepSeek-R1-0528-Qwen3-8B with reasoning capabilities</p>
+            <p><a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank">Built with anycoder</a></p>
+        </div>
+    """)
+    
+    with gr.Row():
+        # Main chat column
+        with gr.Column(scale=3):
+            chatbot = gr.Chatbot(
+                label="Chat",
+                height=500,
+                type="messages",
+                show_copy_button=True,
+                avatar_images=(None, "https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg"),
+                render_markdown=True,
+                elem_classes=["chatbot-container"]
+            )
+            
+            with gr.Row():
+                msg = gr.Textbox(
+                    placeholder="Type your message here... (Press Enter to send)",
+                    label="Message",
+                    scale=4,
+                    lines=2,
+                    max_lines=5,
+                    autofocus=True
+                )
+                send_btn = gr.Button("Send 📤", variant="primary", scale=1)
+            
+            with gr.Row():
+                clear_btn = gr.Button("🗑️ Clear Chat", variant="secondary")
+                regenerate_btn = gr.Button("🔄 Regenerate", variant="secondary")
+                export_btn = gr.Button("📥 Export", variant="secondary")
+        
+        # Settings sidebar
+        with gr.Column(scale=1):
+            gr.Markdown("### ⚙️ Settings")
+            
+            with gr.Accordion("System Prompt", open=True):
+                system_prompt_choice = gr.Dropdown(
+                    choices=list(SYSTEM_PROMPTS.keys()),
+                    value="Default Assistant",
+                    label="Preset Prompts",
+                    interactive=True
+                )
+                
+                custom_system_prompt = gr.Textbox(
+                    label="Custom System Prompt",
+                    placeholder="Enter your custom system prompt here...",
+                    lines=4,
+                    visible=False
+                )
+            
+            with gr.Accordion("Generation Parameters", open=False):
+                temperature = gr.Slider(
+                    minimum=0.0,
+                    maximum=2.0,
+                    value=0.7,
+                    step=0.1,
+                    label="Temperature",
+                    info="Higher = more creative, Lower = more focused"
+                )
+                
+                max_tokens = gr.Slider(
+                    minimum=64,
+                    maximum=4096,
+                    value=1024,
+                    step=64,
+                    label="Max Tokens",
+                    info="Maximum response length"
+                )
+                
+                top_p = gr.Slider(
+                    minimum=0.0,
+                    maximum=1.0,
+                    value=0.9,
+                    step=0.05,
+                    label="Top P",
+                    info="Nucleus sampling parameter"
+                )
+            
+            with gr.Accordion("Display Options", open=False):
+                show_thinking = gr.Checkbox(
+                    value=True,
+                    label="Show Thinking Process",
+                    info="Display the model's reasoning steps"
+                )
+            
+            # Export output
+            export_output = gr.Textbox(
+                label="Exported Chat",
+                lines=10,
+                visible=False,
+                show_copy_button=True
+            )
+    
+    # Examples
+    gr.Markdown("### 💡 Example Prompts")
+    gr.Examples(
+        examples=[
+            ["Explain quantum computing in simple terms"],
+            ["Write a haiku about artificial intelligence"],
+            ["What's the time complexity of quicksort and why?"],
+            ["Help me brainstorm ideas for a sustainable business"],
+            ["Solve this step by step: If 3x + 7 = 22, what is x?"],
+        ],
+        inputs=msg,
+        label=""
+    )
+    
+    # Event handlers
+    def toggle_custom_prompt(choice):
+        return gr.Textbox(visible=(choice == "Custom"))
+    
+    system_prompt_choice.change(
+        toggle_custom_prompt,
+        inputs=[system_prompt_choice],
+        outputs=[custom_system_prompt]
+    )
+    
+    def user_message(message, history):
+        if message.strip():
+            history.append({"role": "user", "content": message})
+        return "", history
+    
+    def bot_response(history, system_prompt_choice, custom_system_prompt, temperature, max_tokens, top_p, show_thinking):
+        if not history:
+            yield history
+            return
+            
+        user_msg = history[-1]["content"]
+        history_for_api = history[:-1]
+        
+        history.append({"role": "assistant", "content": ""})
+        
+        for response in chat(user_msg, history_for_api, system_prompt_choice, custom_system_prompt, temperature, max_tokens, top_p, show_thinking):
+            history[-1]["content"] = response
+            yield history
+    
+    def regenerate(history, system_prompt_choice, custom_system_prompt, temperature, max_tokens, top_p, show_thinking):
+        if len(history) >= 2:
+            # Remove last assistant message
+            history = history[:-1]
+            # Get last user message
+            user_msg = history[-1]["content"]
+            history_for_api = history[:-1]
+            
+            history.append({"role": "assistant", "content": ""})
+            
+            for response in chat(user_msg, history_for_api, system_prompt_choice, custom_system_prompt, temperature, max_tokens, top_p, show_thinking):
+                history[-1]["content"] = response
+                yield history
+        else:
+            yield history
+    
+    def show_export(history):
+        export_text = export_chat(history)
+        return gr.Textbox(visible=True, value=export_text)
+    
+    # Wire up events
+    msg.submit(
+        user_message,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        queue=False
+    ).then(
+        bot_response,
+        inputs=[chatbot, system_prompt_choice, custom_system_prompt, temperature, max_tokens, top_p, show_thinking],
+        outputs=[chatbot]
+    )
+    
+    send_btn.click(
+        user_message,
+        inputs=[msg, chatbot],
+        outputs=[msg, chatbot],
+        queue=False
+    ).then(
+        bot_response,
+        inputs=[chatbot, system_prompt_choice, custom_system_prompt, temperature, max_tokens, top_p, show_thinking],
+        outputs=[chatbot]
+    )
+    
+    clear_btn.click(
+        clear_chat,
+        outputs=[chatbot, msg]
+    )
+    
+    regenerate_btn.click(
+        regenerate,
+        inputs=[chatbot, system_prompt_choice, custom_system_prompt, temperature, max_tokens, top_p, show_thinking],
+        outputs=[chatbot]
+    )
+    
+    export_btn.click(
+        show_export,
+        inputs=[chatbot],
+        outputs=[export_output]
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,12 @@
+huggingface_hub
+gradio
+requests
+Pillow
+git+https://github.com/huggingface/transformers
+torch
+tokenizers
+accelerate
+numpy
+pandas
+sentencepiece
+datasets