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()