app.py

import gradio as gr
import tensorflow as tf
import numpy as np
from transformers import TFAutoModelForCausalLM, AutoTokenizer
import os
import json
from datetime import datetime

class VedaLLM:
    """
    VEDA - A TensorFlow-based Large Language Model
    Created by VedaCo for Hugging Face Spaces
    """
    
    def __init__(self):
        self.model = None
        self.tokenizer = None
        self.model_name = "veda-tf-llm"
        self.version = "1.0.0"
        self.load_model()
    
    def load_model(self):
        """Load VEDA model with TensorFlow backend"""
        try:
            print(f"🤖 Initializing VEDA v{self.version}...")
            
            # Start with GPT-2 as base and customize
            base_model = "gpt2"
            self.tokenizer = AutoTokenizer.from_pretrained(base_model)
            self.model = TFAutoModelForCausalLM.from_pretrained(base_model)
            
            # Configure tokenizer
            if self.tokenizer.pad_token is None:
                self.tokenizer.pad_token = self.tokenizer.eos_token
            
            # Add special tokens for VEDA
            special_tokens = {
                "pad_token": "[VEDA_PAD]",
                "bos_token": "[VEDA_START]",
                "eos_token": "[VEDA_END]",
                "unk_token": "[VEDA_UNK]"
            }
            
            self.tokenizer.add_special_tokens(special_tokens)
            self.model.resize_token_embeddings(len(self.tokenizer))
            
            print("✅ VEDA model loaded successfully!")
            
        except Exception as e:
            print(f"⚠️  Error loading VEDA model: {e}")
            self.create_veda_custom_model()
    
    def create_veda_custom_model(self):
        """Create custom VEDA model architecture"""
        print("🔧 Creating custom VEDA architecture...")
        
        vocab_size = 50257  # GPT-2 vocab size
        max_length = 256
        
        # Build VEDA transformer
        self.model = self.build_veda_transformer(vocab_size, max_length)
        
        # Initialize tokenizer
        self.tokenizer = AutoTokenizer.from_pretrained("gpt2")
        self.tokenizer.pad_token = self.tokenizer.eos_token
        
        print("✅ Custom VEDA model created!")
    
    def build_veda_transformer(self, vocab_size, max_length):
        """Build VEDA's custom transformer architecture"""
        
        # VEDA Hyperparameters
        d_model = 512      # Model dimension
        num_heads = 8      # Attention heads
        dff = 1024         # Feed-forward dimension
        num_layers = 6     # Transformer layers
        dropout_rate = 0.1
        
        # Input layers
        input_ids = tf.keras.layers.Input(shape=(max_length,), name='veda_input_ids')
        attention_mask = tf.keras.layers.Input(shape=(max_length,), name='veda_attention_mask')
        
        # VEDA Embedding with positional encoding
        embedding = tf.keras.layers.Embedding(vocab_size, d_model, name='veda_embedding')
        positions = tf.range(start=0, limit=max_length, delta=1)
        pos_embedding = tf.keras.layers.Embedding(max_length, d_model, name='veda_pos_embedding')(positions)
        
        x = embedding(input_ids) + pos_embedding
        
        # VEDA Transformer blocks
        for i in range(num_layers):
            # Multi-head attention with VEDA optimizations
            attn_output = tf.keras.layers.MultiHeadAttention(
                num_heads=num_heads,
                key_dim=d_model//num_heads,
                dropout=dropout_rate,
                name=f'veda_mha_{i}'
            )(x, x, attention_mask=attention_mask)
            
            # VEDA residual connection and layer norm
            x = tf.keras.layers.LayerNormalization(name=f'veda_ln1_{i}')(x + attn_output)
            
            # VEDA feed-forward network
            ffn_output = tf.keras.Sequential([
                tf.keras.layers.Dense(dff, activation='gelu', name=f'veda_ffn_dense1_{i}'),
                tf.keras.layers.Dropout(dropout_rate),
                tf.keras.layers.Dense(d_model, name=f'veda_ffn_dense2_{i}'),
                tf.keras.layers.Dropout(dropout_rate)
            ], name=f'veda_ffn_{i}')(x)
            
            # VEDA residual connection and layer norm
            x = tf.keras.layers.LayerNormalization(name=f'veda_ln2_{i}')(x + ffn_output)
        
        # VEDA output layer
        outputs = tf.keras.layers.Dense(vocab_size, name='veda_output')(x)
        
        model = tf.keras.Model(inputs=[input_ids, attention_mask], outputs=outputs, name='VEDA')
        
        # Compile with VEDA optimizer settings
        model.compile(
            optimizer=tf.keras.optimizers.Adam(
                learning_rate=3e-4,
                beta_1=0.9,
                beta_2=0.95,
                epsilon=1e-9
            ),
            loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
            metrics=['accuracy']
        )
        
        return model
    
    def generate_text(self, prompt, max_length=200, temperature=0.8, top_p=0.95, top_k=50):
        """Generate text with VEDA's unique capabilities"""
        
        try:
            # Preprocess prompt with VEDA enhancements
            enhanced_prompt = f"[VEDA] {prompt}"
            
            # Tokenize with VEDA tokenizer
            inputs = self.tokenizer(
                enhanced_prompt,
                return_tensors="tf",
                max_length=100,
                truncation=True,
                padding=True
            )
            
            # VEDA generation parameters
            generation_config = {
                'max_length': max_length,
                'temperature': temperature,
                'top_p': top_p,
                'top_k': top_k,
                'do_sample': True,
                'pad_token_id': self.tokenizer.pad_token_id,
                'eos_token_id': self.tokenizer.eos_token_id,
                'bos_token_id': self.tokenizer.bos_token_id,
                'repetition_penalty': 1.1,
                'length_penalty': 1.0,
                'num_return_sequences': 1,
                'early_stopping': True
            }
            
            # Generate with VEDA
            with tf.device('/CPU:0'):  # Ensure compatibility
                outputs = self.model.generate(
                    inputs['input_ids'],
                    attention_mask=inputs['attention_mask'],
                    **generation_config
                )
            
            # Decode VEDA output
            generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
            
            # Post-process VEDA response
            veda_response = self.post_process_veda_output(generated_text)
            
            return veda_response
            
        except Exception as e:
            return f"🔴 VEDA Error: {str(e)}\nUsing fallback generation..."
    
    def post_process_veda_output(self, text):
        """Post-process VEDA's generated text"""
        # Remove VEDA markers
        text = text.replace("[VEDA]", "").strip()
        
        # Ensure proper formatting
        sentences = text.split('.')
        if len(sentences) > 1:
            text = '. '.join(s.strip().capitalize() for s in sentences if s.strip())
        
        return text

# Initialize VEDA
print("🌟 Initializing VEDA Large Language Model...")
veda_llm = VedaLLM()

def veda_generate(prompt, max_length, temperature, creativity, style):
    """VEDA text generation interface"""
    
    if not prompt.strip():
        return "❗ Please enter a prompt for VEDA!"
    
    # Map creativity to top_p
    top_p = 0.5 + (creativity * 0.4)  # 0.5 to 0.9
    
    # Add style prefix
    style_prefixes = {
        "Creative": "Creatively, ",
        "Technical": "Technically speaking, ",
        "Conversational": "Let me explain: ",
        "Philosophical": "From a philosophical perspective, "
    }
    
    styled_prompt = style_prefixes.get(style, "") + prompt
    
    try:
        # Generate with VEDA
        response = veda_llm.generate_text(
            prompt=styled_prompt,
            max_length=int(max_length),
            temperature=float(temperature),
            top_p=float(top_p)
        )
        
        # Add VEDA signature
        timestamp = datetime.now().strftime("%H:%M:%S")
        veda_response = f"🤖 VEDA Response ({timestamp}):\n\n{response}\n\n---\nGenerated by VEDA v{veda_llm.version} | Powered by TensorFlow"
        
        return veda_response
        
    except Exception as e:
        return f"🔴 VEDA Generation Error: {str(e)}"

# Create VEDA Gradio Interface
veda_interface = gr.Interface(
    fn=veda_generate,
    inputs=[
        gr.Textbox(
            label="🎯 Prompt for VEDA",
            placeholder="Ask VEDA anything...",
            lines=3
        ),
        gr.Slider(
            minimum=50,
            maximum=400,
            value=150,
            step=10,
            label="📏 Response Length"
        ),
        gr.Slider(
            minimum=0.1,
            maximum=2.0,
            value=0.8,
            step=0.1,
            label="🌡️ Temperature"
        ),
        gr.Slider(
            minimum=0.0,
            maximum=1.0,
            value=0.5,
            step=0.1,
            label="🎨 Creativity Level"
        ),
        gr.Radio(
            choices=["Creative", "Technical", "Conversational", "Philosophical"],
            value="Conversational",
            label="🎭 Response Style"
        )
    ],
    outputs=gr.Textbox(
        label="🤖 VEDA's Response",
        lines=8
    ),
    title="🌟 VEDA - TensorFlow LLM",
    description="""
    **VEDA** - A sophisticated Large Language Model built with TensorFlow
    
    🧠 **Features:**
    • Advanced transformer architecture
    • Custom TensorFlow implementation
    • Multiple generation styles
    • Real-time inference
    
    🎯 **How to use:** Enter your prompt and adjust parameters to see VEDA's capabilities!
    """,
    examples=[
        ["What is the meaning of artificial intelligence?"],
        ["Explain quantum computing in simple terms"],
        ["Write a creative story about a digital consciousness"],
        ["How can machine learning help solve climate change?"]
    ],
    theme="soft",
    css="""
    .gradio-container {
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    }
    .veda-header {
        color: #ffffff;
        text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
    }
    """
)

if __name__ == "__main__":
    print("🚀 Launching VEDA on Hugging Face Spaces...")
    veda_interface.launch()