Update app.py

app.py

@@ -1,70 +1,205 @@
+import os
 import gradio as gr
 from huggingface_hub import InferenceClient
+import random
 
+def get_coding_samples():
+    """Diverse coding challenges"""
+    return [
+        "Write a function to reverse a linked list",
+        "Implement binary search in Python",
+        "Create a REST API endpoint for user authentication",
+        "Write a SQL query to find duplicate records",
+        "Build a React component for a todo list",
+        "Implement quicksort algorithm",
+        "Parse JSON and handle errors gracefully",
+        "Create a decorator for timing function execution",
+        "Write regex to validate email addresses",
+        "Implement a LRU cache in Python",
+        "Build a simple web scraper with BeautifulSoup",
+        "Create a custom hook in React",
+        "Write unit tests for a calculator function",
+        "Implement depth-first search for a graph",
+        "Build a CLI tool with argparse",
+        "Create a database migration script",
+        "Write a function to detect palindromes",
+        "Implement JWT token authentication",
+        "Build a responsive navbar with Tailwind CSS",
+        "Create a rate limiter middleware",
+    ]
 
-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")
+def create_coder_system_message():
+    """System prompt for coding assistance"""
+    return """You are CoderAI, an expert programming assistant.
 
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
+**Your Approach:**
+1. Understand the requirements clearly
+2. Provide clean, working code with comments
+3. Explain key concepts and design decisions
+4. Suggest best practices and optimizations
+5. Include error handling where appropriate
 
-    response = ""
+**Code Quality:**
+- Write readable, maintainable code
+- Follow language conventions and style guides
+- Use meaningful variable names
+- Add docstrings/comments for complex logic
+- Consider edge cases and error handling
 
-    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
+**Format:**
+- Use markdown code blocks with language tags
+- Explain code sections when helpful
+- Provide usage examples
+- Mention dependencies if needed
 
-        response += token
-        yield response
+Be practical, efficient, and educational."""
 
+def respond(message, history, system_message, max_tokens, temperature, top_p, model_choice):
+    """Streaming response with error handling"""
+    hf_token = os.getenv("HF_TOKEN")
+    if not hf_token:
+        yield "❌ **Error:** HF_TOKEN not found. Set it in environment or Spaces secrets."
+        return
+    
+    # Model selection
+    models = {
+        "DeepSeek Coder": "deepseek-ai/deepseek-coder-33b-instruct",
+        "CodeLlama": "codellama/CodeLlama-34b-Instruct-hf",
+        "Qwen Coder": "Qwen/Qwen2.5-Coder-32B-Instruct",
+    }
+    
+    client = InferenceClient(
+        model=models.get(model_choice, models["Qwen Coder"]),
+        token=hf_token
+    )
+    
+    messages = [{"role": "system", "content": system_message}]
+    
+    for msg in history:
+        if isinstance(msg, dict):
+            role = msg.get("role", "user")
+            content = msg.get("content", "")
+        else:
+            role = "user"
+            content = str(msg)
+        
+        if content:
+            messages.append({"role": role, "content": content})
+    
+    messages.append({"role": "user", "content": message})
+    
+    try:
+        response_text = ""
+        for chunk in client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            stream=True
+        ):
+            if chunk.choices[0].delta.content:
+                response_text += chunk.choices[0].delta.content
+                yield response_text
+                
+    except Exception as e:
+        error_msg = f"❌ **Error:** {str(e)}\n\nTry:\n- Checking your HF_TOKEN\n- Simplifying the request\n- Using a different model"
+        yield error_msg
 
-"""
-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)",
-        ),
-    ],
-)
+def get_random_sample():
+    """Get random coding challenge"""
+    return random.choice(get_coding_samples())
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+# Gradio Interface
+with gr.Blocks(title="💻 CoderAI", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 💻 **CoderAI**\n*Your AI Programming Assistant*")
+    
+    chatbot = gr.Chatbot(
+        height=500, 
+        type='messages', 
+        label="💬 Conversation",
+        show_copy_button=True
+    )
+    
+    msg = gr.Textbox(
+        placeholder="Ask a coding question or describe what you need...", 
+        show_label=False, 
+        scale=4
+    )
+    
+    with gr.Row():
+        submit = gr.Button("🚀 Code", variant="primary", scale=1)
+        clear = gr.Button("🗑️ Clear", variant="secondary", scale=1)
+        sample = gr.Button("🎲 Random", variant="secondary", scale=1)
+    
+    with gr.Accordion("⚙️ Advanced Settings", open=False):
+        model_dropdown = gr.Dropdown(
+            choices=["Qwen Coder", "DeepSeek Coder", "CodeLlama"],
+            value="Qwen Coder",
+            label="Model Selection"
+        )
+        temp_slider = gr.Slider(0.1, 1.0, value=0.2, step=0.1, label="Temperature")
+        tokens_slider = gr.Slider(512, 4096, value=2048, step=256, label="Max Tokens")
+        top_p_slider = gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-p")
+    
+    with gr.Accordion("💡 Help & Examples", open=False):
+        gr.Markdown("""
+**Tips:**
+- Be specific about language and requirements
+- Mention if you need comments or tests
+- Ask for explanations of complex parts
+- Request specific design patterns or styles
 
+**What I can help with:**
+- Writing functions and classes
+- Debugging code
+- Code review and optimization
+- Algorithm implementation
+- API design and database queries
+- Testing and documentation
+        """)
+    
+    gr.Examples(
+        examples=[
+            ["Write a Python function to check if a string is a palindrome"],
+            ["Create a React component for a searchable dropdown"],
+            ["Implement a binary tree traversal in JavaScript"],
+            ["Write a SQL query to find the top 5 customers by revenue"],
+            ["Build a simple Flask API with error handling"],
+            ["Create a custom validation decorator in Python"],
+        ],
+        inputs=msg
+    )
+    
+    system_msg = gr.State(create_coder_system_message())
+    
+    def chat_response(message, history, sys_msg, max_tok, temp, top_p, model):
+        """Handle chat with streaming"""
+        if not message.strip():
+            return history, ""
+        
+        history.append({"role": "user", "content": message})
+        history.append({"role": "assistant", "content": ""})
+        
+        for response in respond(message, history[:-1], sys_msg, max_tok, temp, top_p, model):
+            history[-1]["content"] = response
+            yield history, ""
+        
+        return history, ""
+    
+    def clear_chat():
+        return [], ""
+    
+    msg.submit(
+        chat_response, 
+        [msg, chatbot, system_msg, tokens_slider, temp_slider, top_p_slider, model_dropdown], 
+        [chatbot, msg]
+    )
+    submit.click(
+        chat_response, 
+        [msg, chatbot, system_msg, tokens_slider, temp_slider, top_p_slider, model_dropdown], 
+        [chatbot, msg]
+    )
+    clear.click(clear_chat, outputs=[chatbot, msg])
+    sample.click(get_random_sample, outputs=msg)
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()