Create app.py

app.py

@@ -0,0 +1,62 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load tokenizer and model (simulating EvoTransformer with GPT-2-like architecture)
+model_name = "gpt2"  # Replace with fine-tuned EvoTransformer model on Hugging Face if available
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+model.eval()
+
+# Mock EvoTransformer architecture traits
+architecture = {
+    "layers": 6,
+    "heads": 8,
+    "ffn_dim": 2048,
+    "parameters": "58M"
+}
+
+def generate_response(user_input, max_length=100):
+    # Tokenize input with a conversational prompt
+    prompt = f"User: {user_input} Assistant: "
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
+    input_ids = inputs["input_ids"]
+
+    # Generate response
+    with torch.no_grad():
+        outputs = model.generate(
+            input_ids,
+            max_length=max_length,
+            num_return_sequences=1,
+            do_sample=True,
+            top_p=0.9,
+            temperature=0.7,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    
+    # Decode response
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    response = response[len(prompt):].strip()
+    
+    # Format output with architecture details
+    arch_info = (
+        f"Model Architecture:\n"
+        f"- Layers: {architecture['layers']}\n"
+        f"- Attention Heads: {architecture['heads']}\n"
+        f"- FFN Dimension: {architecture['ffn_dim']}\n"
+        f"- Parameters: {architecture['parameters']}"
+    )
+    
+    return f"**Response**: {response}\n\n**{arch_info}**"
+
+# Gradio interface
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=gr.Textbox(lines=2, placeholder="Type your message here..."),
+    outputs="markdown",
+    title="EvoTransformer Chat Demo",
+    description="Chat with a simplified EvoTransformer model, designed to evolve Transformer architectures. Enter a message to get a response and view model details."
+)
+
+if __name__ == "__main__":
+    iface.launch()