Update app.py

app.py

@@ -1,70 +1,359 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import tensorflow as tf
+import numpy as np
+import os
+import json
+from model import VedaProgrammingLLM, create_veda_model
+from tokenizer import VedaTokenizer
+from train import VedaTrainer
 
+# Global variables for model and tokenizer
+model = None
+tokenizer = None
+trainer = None
 
-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 load_or_create_model():
+    """Load existing model or create new one"""
+    global model, tokenizer, trainer
+    
+    model_path = "veda_model"
+    
+    if os.path.exists(os.path.join(model_path, "config.json")):
+        print("Loading existing model...")
+        
+        # Load config
+        with open(os.path.join(model_path, "config.json"), 'r') as f:
+            config = json.load(f)
+        
+        # Load tokenizer
+        tokenizer = VedaTokenizer()
+        tokenizer.load(os.path.join(model_path, "tokenizer.json"))
+        
+        # Create model
+        model = create_veda_model(
+            vocab_size=tokenizer.vocabulary_size,
+            max_length=config.get('max_length', 256),
+            model_size="small"
+        )
+        
+        # Build model
+        dummy_input = tf.zeros((1, config.get('max_length', 256)), dtype=tf.int32)
+        model(dummy_input)
+        
+        # Load weights
+        model.load_weights(os.path.join(model_path, "model_weights.h5"))
+        print("Model loaded successfully!")
+    else:
+        print("No existing model found. Creating and training new model...")
+        trainer = VedaTrainer(
+            data_path="programming.txt",
+            vocab_size=5000,
+            max_length=256,
+            batch_size=8,
+            model_size="small"
+        )
+        trainer.train(epochs=5, save_path=model_path)
+        model = trainer.model
+        tokenizer = trainer.tokenizer
+        print("Model trained and ready!")
 
-    messages = [{"role": "system", "content": system_message}]
+def generate_code(
+    prompt: str,
+    max_tokens: int,
+    temperature: float,
+    top_k: int,
+    top_p: float
+) -> str:
+    """Generate code based on prompt"""
+    global model, tokenizer
+    
+    if model is None or tokenizer is None:
+        return "Error: Model not loaded. Please wait for initialization."
+    
+    try:
+        # Encode prompt
+        prompt_tokens = tokenizer.encode(prompt)
+        
+        if len(prompt_tokens) == 0:
+            return "Error: Could not tokenize prompt."
+        
+        # Generate
+        generated_tokens = model.generate(
+            prompt_tokens,
+            max_new_tokens=int(max_tokens),
+            temperature=float(temperature),
+            top_k=int(top_k),
+            top_p=float(top_p)
+        )
+        
+        # Decode
+        generated_text = tokenizer.decode(generated_tokens)
+        
+        return generated_text
+    
+    except Exception as e:
+        return f"Error generating code: {str(e)}"
 
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    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
+def train_model(
+    training_data: str,
+    epochs: int,
+    learning_rate: float
+) -> str:
+    """Train or fine-tune the model"""
+    global model, tokenizer, trainer
+    
+    try:
+        # Save training data
+        with open("programming.txt", 'w', encoding='utf-8') as f:
+            f.write(training_data)
+        
+        # Create trainer
+        trainer = VedaTrainer(
+            data_path="programming.txt",
+            vocab_size=5000,
+            max_length=256,
+            batch_size=8,
+            model_size="small"
+        )
+        
+        # Train
+        history = trainer.train(epochs=int(epochs), save_path="veda_model")
+        
+        # Update global references
+        model = trainer.model
+        tokenizer = trainer.tokenizer
+        
+        # Get final metrics
+        final_loss = history.history['loss'][-1]
+        final_acc = history.history.get('accuracy', [0])[-1]
+        
+        return f"""Training completed successfully!
+        
+Final Loss: {final_loss:.4f}
+Final Accuracy: {final_acc:.4f}
+Epochs: {epochs}
+Vocabulary Size: {tokenizer.vocabulary_size}
+"""
+    
+    except Exception as e:
+        return f"Error during training: {str(e)}"
 
-        response += token
-        yield response
+def get_model_info() -> str:
+    """Get information about the current model"""
+    global model, tokenizer
+    
+    if model is None:
+        return "No model loaded."
+    
+    config = model.get_config()
+    
+    info = f"""
+**Veda Programming LLM**
 
+- Vocabulary Size: {config['vocab_size']}
+- Max Sequence Length: {config['max_length']}
+- Model Dimension: {config['d_model']}
+- Number of Heads: {config['num_heads']}
+- Number of Layers: {config['num_layers']}
+- Feed Forward Dimension: {config['ff_dim']}
 
+Total Parameters: {model.count_params():,}
 """
-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)",
-        ),
-    ],
-)
+    return info
+
+# Create Gradio interface
+def create_interface():
+    """Create the Gradio interface"""
+    
+    with gr.Blocks(
+        title="Veda Programming LLM",
+        theme=gr.themes.Soft()
+    ) as demo:
+        
+        gr.Markdown("""
+        # 🕉️ Veda Programming LLM
+        
+        A TensorFlow-based language model for code generation.
+        
+        Train on your own code or use the pre-trained model to generate programming code.
+        """)
+        
+        with gr.Tabs():
+            # Generation Tab
+            with gr.TabItem("💻 Code Generation"):
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        prompt_input = gr.Textbox(
+                            label="Code Prompt",
+                            placeholder="def fibonacci(",
+                            lines=5,
+                            value="def calculate_sum("
+                        )
+                        
+                        with gr.Row():
+                            max_tokens = gr.Slider(
+                                minimum=10,
+                                maximum=500,
+                                value=100,
+                                step=10,
+                                label="Max New Tokens"
+                            )
+                            temperature = gr.Slider(
+                                minimum=0.1,
+                                maximum=2.0,
+                                value=0.7,
+                                step=0.1,
+                                label="Temperature"
+                            )
+                        
+                        with gr.Row():
+                            top_k = gr.Slider(
+                                minimum=1,
+                                maximum=100,
+                                value=50,
+                                step=1,
+                                label="Top-K"
+                            )
+                            top_p = gr.Slider(
+                                minimum=0.1,
+                                maximum=1.0,
+                                value=0.9,
+                                step=0.05,
+                                label="Top-P (Nucleus)"
+                            )
+                        
+                        generate_btn = gr.Button(
+                            "🚀 Generate Code",
+                            variant="primary"
+                        )
+                    
+                    with gr.Column(scale=1):
+                        output_code = gr.Code(
+                            label="Generated Code",
+                            language="python",
+                            lines=15
+                        )
+                
+                generate_btn.click(
+                    fn=generate_code,
+                    inputs=[prompt_input, max_tokens, temperature, top_k, top_p],
+                    outputs=output_code
+                )
+            
+            # Training Tab
+            with gr.TabItem("🎓 Training"):
+                gr.Markdown("""
+                ### Train the Model
+                
+                Paste your programming code below to train or fine-tune the model.
+                """)
+                
+                training_data = gr.Textbox(
+                    label="Training Data (Python Code)",
+                    placeholder="Paste your Python code here...",
+                    lines=15,
+                    value='''def hello_world():
+    print("Hello, World!")
+    return True
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+def add_numbers(a, b):
+    return a + b
 
+class MyClass:
+    def __init__(self, value):
+        self.value = value
+    
+    def get_value(self):
+        return self.value
+'''
+                )
+                
+                with gr.Row():
+                    epochs_input = gr.Slider(
+                        minimum=1,
+                        maximum=50,
+                        value=10,
+                        step=1,
+                        label="Epochs"
+                    )
+                    lr_input = gr.Slider(
+                        minimum=0.00001,
+                        maximum=0.01,
+                        value=0.0001,
+                        step=0.00001,
+                        label="Learning Rate"
+                    )
+                
+                train_btn = gr.Button(
+                    "🎯 Start Training",
+                    variant="primary"
+                )
+                
+                training_output = gr.Textbox(
+                    label="Training Output",
+                    lines=10
+                )
+                
+                train_btn.click(
+                    fn=train_model,
+                    inputs=[training_data, epochs_input, lr_input],
+                    outputs=training_output
+                )
+            
+            # Model Info Tab
+            with gr.TabItem("ℹ️ Model Info"):
+                model_info = gr.Markdown()
+                refresh_btn = gr.Button("🔄 Refresh Info")
+                refresh_btn.click(
+                    fn=get_model_info,
+                    outputs=model_info
+                )
+            
+            # Examples Tab
+            with gr.TabItem("📝 Examples"):
+                gr.Markdown("""
+                ### Example Prompts
+                
+                Try these prompts for code generation:
+                
+                1. `def fibonacci(`
+                2. `class Calculator:`
+                3. `def binary_search(arr, target):`
+                4. `async def fetch_data(`
+                5. `def quicksort(arr):`
+                6. `class LinkedList:`
+                """)
+                
+                gr.Examples(
+                    examples=[
+                        ["def fibonacci(", 100, 0.7, 50, 0.9],
+                        ["class Calculator:", 150, 0.8, 40, 0.95],
+                        ["def binary_search(arr, target):", 100, 0.6, 50, 0.9],
+                        ["def bubble_sort(", 100, 0.7, 50, 0.9],
+                    ],
+                    inputs=[prompt_input, max_tokens, temperature, top_k, top_p],
+                    outputs=output_code,
+                    fn=generate_code
+                )
+        
+        gr.Markdown("""
+        ---
+        **Veda Programming LLM** - Built with TensorFlow & Gradio
+        
+        *Note: This is a demonstration model. For production use, train on larger datasets.*
+        """)
+    
+    return demo
 
+# Main execution
 if __name__ == "__main__":
-    demo.launch()
+    print("Initializing Veda Programming LLM...")
+    load_or_create_model()
+    
+    print("Starting Gradio interface...")
+    demo = create_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )