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app.py

@@ -0,0 +1,360 @@
+#!/usr/bin/env python3
+"""
+OpenLLM Demo App - Works without external model dependencies
+"""
+
+import gradio as gr
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import json
+import random
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class DemoGPT(nn.Module):
+    """Demo GPT model for testing"""
+    
+    def __init__(self, vocab_size=1000, n_layer=2, n_head=4, n_embd=128):
+        super().__init__()
+        self.config = type('Config', (), {
+            'vocab_size': vocab_size,
+            'n_layer': n_layer,
+            'n_head': n_head,
+            'n_embd': n_embd,
+            'block_size': 256
+        })()
+        
+        self.transformer = nn.ModuleDict(dict(
+            wte = nn.Embedding(vocab_size, n_embd),
+            wpe = nn.Embedding(256, n_embd),
+            drop = nn.Dropout(0.1),
+            h = nn.ModuleList([nn.TransformerEncoderLayer(
+                d_model=n_embd,
+                nhead=n_head,
+                dim_feedforward=4 * n_embd,
+                dropout=0.1,
+                batch_first=True
+            ) for _ in range(n_layer)]),
+            ln_f = nn.LayerNorm(n_embd),
+        ))
+        self.lm_head = nn.Linear(n_embd, vocab_size)
+        
+        # Initialize with random weights
+        self.apply(self._init_weights)
+    
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+    
+    def forward(self, idx, targets=None):
+        b, t = idx.size()
+        pos = torch.arange(0, t, dtype=torch.long, device=idx.device).unsqueeze(0)
+        
+        tok_emb = self.transformer.wte(idx)
+        pos_emb = self.transformer.wpe(pos)
+        x = self.transformer.drop(tok_emb + pos_emb)
+        
+        for block in self.transformer.h:
+            x = block(x)
+        x = self.transformer.ln_f(x)
+        
+        if targets is not None:
+            logits = self.lm_head(x)
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
+        else:
+            logits = self.lm_head(x[:, [-1], :])
+            loss = None
+        
+        return logits, loss
+
+class DemoInferenceEngine:
+    """Demo inference engine that works without external models"""
+    
+    def __init__(self):
+        self.models = {}
+        self.current_model = None
+        
+        # Demo model configurations
+        self.model_configs = {
+            "demo-4k": {
+                "name": "Demo Model (4k steps)",
+                "description": "Demo model simulating 4,000 training steps",
+                "steps": 4000
+            },
+            "demo-6k": {
+                "name": "Demo Model (6k steps)", 
+                "description": "Demo model simulating 6,000 training steps",
+                "steps": 6000
+            },
+            "demo-7k": {
+                "name": "Demo Model (7k steps)",
+                "description": "Demo model simulating 7,000 training steps", 
+                "steps": 7000
+            },
+            "demo-8k": {
+                "name": "Demo Model (8k steps)",
+                "description": "Demo model simulating 8,000 training steps",
+                "steps": 8000
+            },
+            "demo-9k": {
+                "name": "Demo Model (9k steps)",
+                "description": "Demo model simulating 9,000 training steps",
+                "steps": 9000
+            }
+        }
+        
+        logger.info("🚀 Demo OpenLLM Inference Engine initialized")
+    
+    def load_model(self, model_id: str) -> bool:
+        """Load a demo model"""
+        try:
+            config = self.model_configs.get(model_id)
+            if not config:
+                logger.error(f"❌ Unknown model ID: {model_id}")
+                return False
+            
+            logger.info(f"📥 Loading demo model: {model_id}")
+            
+            # Create a demo model
+            model = DemoGPT()
+            model.eval()
+            self.models[model_id] = model
+            self.current_model = model_id
+            
+            logger.info(f"✅ Successfully loaded demo model: {model_id}")
+            return True
+            
+        except Exception as e:
+            logger.error(f"❌ Failed to load demo model {model_id}: {e}")
+            return False
+    
+    def generate_text(self, prompt: str, max_length: int = 100, 
+                     temperature: float = 0.7, top_k: int = 50, 
+                     top_p: float = 0.9) -> str:
+        """Generate demo text"""
+        if not self.current_model or self.current_model not in self.models:
+            return "❌ No model loaded. Please select a model first."
+        
+        try:
+            model = self.models[self.current_model]
+            config = self.model_configs[self.current_model]
+            
+            # Create demo response based on prompt and parameters
+            demo_responses = [
+                f"Based on your prompt '{prompt[:50]}...', here's a demo response from the {config['name']} model. This is a simulated output that demonstrates how the interface would work with real models.",
+                f"The {config['name']} model (trained for {config['steps']} steps) would generate: '{prompt}' followed by additional context and continuation text.",
+                f"Demo generation with temperature={temperature}, top_k={top_k}, top_p={top_p}: The model processes your input and produces coherent text based on the training patterns it has learned.",
+                f"Simulated response from {config['name']}: Your prompt '{prompt}' is interesting. Let me provide a thoughtful continuation that builds upon your input while maintaining context and relevance."
+            ]
+            
+            # Select response based on parameters
+            response = random.choice(demo_responses)
+            
+            # Add some variation based on parameters
+            if temperature > 1.0:
+                response += " (Higher temperature makes responses more creative and varied)"
+            elif temperature < 0.5:
+                response += " (Lower temperature produces more focused and deterministic output)"
+            
+            if max_length > 200:
+                response += " With a longer generation length, the model would continue with more detailed elaboration and context."
+            
+            return response
+            
+        except Exception as e:
+            error_msg = f"❌ Demo generation failed: {str(e)}"
+            logger.error(error_msg)
+            return error_msg
+
+# Initialize the demo inference engine
+inference_engine = DemoInferenceEngine()
+
+def load_model_info(model_id: str) -> str:
+    """Get information about a specific model"""
+    config = inference_engine.model_configs.get(model_id)
+    if config:
+        return f"**{config['name']}**\n\n{config['description']}"
+    return "❌ Model not found"
+
+def generate_text_interface(model_id: str, prompt: str, max_length: int, 
+                          temperature: float, top_k: int, top_p: float) -> str:
+    """Gradio interface function for text generation"""
+    try:
+        # Load model if not already loaded
+        if model_id not in inference_engine.models:
+            logger.info(f"🔄 Loading model: {model_id}")
+            success = inference_engine.load_model(model_id)
+            if not success:
+                return f"❌ Failed to load model: {model_id}"
+        
+        # Generate text
+        result = inference_engine.generate_text(
+            prompt=prompt,
+            max_length=max_length,
+            temperature=temperature,
+            top_k=top_k,
+            top_p=top_p
+        )
+        
+        return result
+        
+    except Exception as e:
+        error_msg = f"❌ Error in generation interface: {str(e)}"
+        logger.error(error_msg)
+        return error_msg
+
+# Create Gradio interface
+def create_interface():
+    """Create the Gradio interface"""
+    
+    with gr.Blocks(
+        title="🚀 OpenLLM Demo Space",
+        theme=gr.themes.Soft()
+    ) as interface:
+        
+        # Header
+        gr.Markdown("""
+        # 🚀 OpenLLM Demo Space
+        
+        Welcome to the OpenLLM Demo Space! This is a demonstration interface showing how the OpenLLM inference would work.
+        
+        ## 🎯 Demo Models
+        
+        We provide **5 different demo models** simulating varying training steps:
+        
+        | Model | Training Steps | Description |
+        |-------|---------------|-------------|
+        | **Demo 4k** | 4,000 | Early training stage simulation |
+        | **Demo 6k** | 6,000 | Improved coherence simulation |
+        | **Demo 7k** | 7,000 | Enhanced quality simulation |
+        | **Demo 8k** | 8,000 | Sophisticated understanding simulation |
+        | **Demo 9k** | 9,000 | Best performing model simulation |
+        
+        ---
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                # Model selection
+                model_dropdown = gr.Dropdown(
+                    choices=list(inference_engine.model_configs.keys()),
+                    value="demo-9k",
+                    label="🎯 Select Model",
+                    info="Choose the demo model to use"
+                )
+                
+                # Model information display
+                model_info = gr.Markdown(
+                    value=load_model_info("demo-9k"),
+                    label="📋 Model Information"
+                )
+                
+                # Update model info when selection changes
+                model_dropdown.change(
+                    fn=load_model_info,
+                    inputs=[model_dropdown],
+                    outputs=[model_info]
+                )
+            
+            with gr.Column(scale=2):
+                # Input prompt
+                prompt_input = gr.Textbox(
+                    lines=5,
+                    label="📝 Input Prompt",
+                    placeholder="Enter your text prompt here...",
+                    info="The text that will be used as input for generation"
+                )
+                
+                # Generation parameters
+                with gr.Row():
+                    max_length = gr.Slider(
+                        minimum=10,
+                        maximum=500,
+                        value=100,
+                        step=10,
+                        label="📏 Max Length",
+                        info="Maximum number of tokens to generate"
+                    )
+                    
+                    temperature = gr.Slider(
+                        minimum=0.1,
+                        maximum=2.0,
+                        value=0.7,
+                        step=0.1,
+                        label="🌡️ Temperature",
+                        info="Controls randomness (higher = more random)"
+                    )
+                
+                with gr.Row():
+                    top_k = gr.Slider(
+                        minimum=1,
+                        maximum=100,
+                        value=50,
+                        step=1,
+                        label="🔝 Top-K",
+                        info="Number of highest probability tokens to consider"
+                    )
+                    
+                    top_p = gr.Slider(
+                        minimum=0.1,
+                        maximum=1.0,
+                        value=0.9,
+                        step=0.1,
+                        label="📊 Top-P",
+                        info="Nucleus sampling parameter"
+                    )
+                
+                # Generate button
+                generate_btn = gr.Button(
+                    "🚀 Generate Text",
+                    variant="primary",
+                    size="lg"
+                )
+        
+        # Output
+        output_text = gr.Textbox(
+            lines=10,
+            label="🎯 Generated Text",
+            info="The generated text will appear here"
+        )
+        
+        # Connect the generate button
+        generate_btn.click(
+            fn=generate_text_interface,
+            inputs=[model_dropdown, prompt_input, max_length, temperature, top_k, top_p],
+            outputs=[output_text]
+        )
+        
+        # Footer
+        gr.Markdown("""
+        ---
+        
+        ## 🔧 Technical Details
+        
+        - **Architecture**: GPT-style transformer decoder (demo)
+        - **Model Size**: Small demo models for testing
+        - **Framework**: PyTorch with embedded demo code
+        - **Status**: Demo mode - shows interface functionality
+        
+        **This is a demo version showing the interface. Real models would be loaded from Hugging Face repositories.**
+        """)
+    
+    return interface
+
+# Create and launch the interface
+if __name__ == "__main__":
+    interface = create_interface()
+    interface.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        debug=True
+    )