Upload ./gradio_app.py with huggingface_hub

gradio_app.py

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+"""
+Gradio Web UI for Vadakayil LLM
+Upload this to Hugging Face Spaces to run interactively
+"""
+
+import gradio as gr
+import torch
+import json
+import os
+from pathlib import Path
+
+# Try to import local modules
+try:
+    from model import TinyLLM
+    from tokenizer import Tokenizer
+    LOCAL_MODE = True
+except ImportError:
+    LOCAL_MODE = False
+
+
+def load_model():
+    """Load the trained model."""
+    # Check if running on Spaces
+    if os.path.exists("model.pt"):
+        model_path = "model.pt"
+        tokenizer_path = "tokenizer.json"
+    else:
+        # Local path
+        model_path = "./output/vadakayil_model/model.pt"
+        tokenizer_path = "./output/vadakayil_model/tokenizer.json"
+    
+    # Load tokenizer
+    tokenizer = Tokenizer.load(tokenizer_path)
+    
+    # Load model config
+    with open("config.json" if os.path.exists("config.json") else "./output/vadakayil_model/config.json") as f:
+        config = json.load(f)
+    
+    # Create model
+    model = TinyLLM(
+        vocab_size=config.get("vocab_size", 74),
+        d_model=config.get("d_model", 128),
+        num_heads=config.get("num_heads", 2),
+        num_layers=config.get("num_layers", 2),
+        d_ff=config.get("d_ff", 256),
+        max_seq_len=config.get("max_seq_len", 512),
+        dropout=0.1,
+        pad_token_id=0
+    )
+    
+    # Load weights
+    checkpoint = torch.load(model_path, map_location="cpu", weights_only=False)
+    model.load_state_dict(checkpoint["model_state_dict"])
+    model.eval()
+    
+    return model, tokenizer
+
+
+def generate_text(prompt, max_tokens, temperature, top_k):
+    """Generate text from prompt."""
+    if not hasattr(generate_text, 'model'):
+        generate_text.model, generate_text.tokenizer = load_model()
+    
+    model = generate_text.model
+    tokenizer = generate_text.tokenizer
+    
+    # Encode prompt
+    input_ids = tokenizer.encode(prompt, add_special_tokens=False)
+    input_ids = torch.tensor([input_ids], dtype=torch.long)
+    
+    # Get EOS token
+    eos_token_id = tokenizer.token_to_id.get(tokenizer.eos_token, None)
+    
+    # Generate
+    with torch.no_grad():
+        output_ids = model.generate(
+            input_ids,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            top_k=top_k if top_k > 0 else None,
+            eos_token_id=eos_token_id
+        )
+    
+    # Decode
+    generated_text = tokenizer.decode(output_ids[0].tolist(), skip_special_tokens=True)
+    return generated_text
+
+
+# Create Gradio Interface
+with gr.Blocks(title="Vadakayil LLM", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🧘 Vadakayil LLM
+    
+    A tiny character-level LLM trained on Capt Ajit Vadakayil's writings about:
+    - Mach 0.3 and fluid dynamics
+    - Consciousness and Vedic philosophy  
+    - Silent Kalki Revolution
+    - Evidence and Witness
+    
+    **Model**: [mountainrock/vadakayil-llm-tiny](https://huggingface.co/mountainrock/vadakayil-llm-tiny)
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(
+                label="Enter your question",
+                placeholder="What is Mach 0.3?",
+                lines=3
+            )
+            
+            with gr.Accordion("Advanced Settings", open=False):
+                max_tokens = gr.Slider(50, 300, value=150, step=10, label="Max Tokens")
+                temperature = gr.Slider(0.1, 2.0, value=0.8, step=0.1, label="Temperature")
+                top_k = gr.Slider(0, 100, value=50, step=5, label="Top-K (0 = disabled)")
+            
+            generate_btn = gr.Button("Generate", variant="primary")
+        
+        with gr.Column():
+            output_text = gr.Textbox(label="Generated Answer", lines=5)
+    
+    # Example prompts
+    gr.Examples(
+        examples=[
+            "What is Mach 0.3 and why is it significant?",
+            "Why is Mach 0.3 called the Paradox Rekha?",
+            "What is the Silent Kalki Revolution of Consciousness?",
+            "What does the movie Thondi Muthalum Driksakshiyum represent?",
+            "What is the paradox of holding on versus letting go?",
+        ],
+        inputs=prompt_input
+    )
+    
+    generate_btn.click(
+        fn=generate_text,
+        inputs=[prompt_input, max_tokens, temperature, top_k],
+        outputs=output_text
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()