Upload app.py

app.py

@@ -1,81 +1 @@
-import gradio as gr
-import torch
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-
-# Load model and tokenizer
-def load_model():
-    try:
-        # Load the fine-tuned model
-        model = GPT2LMHeadModel.from_pretrained("aayushraina/gpt2shakespeare")
-        # Use the base GPT-2 tokenizer
-        tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-        model.eval()
-        print("Model and tokenizer loaded successfully!")
-        return model, tokenizer
-    except Exception as e:
-        print(f"Error loading model: {e}")
-        return None, None
-
-# Text generation function
-def generate_text(prompt, max_length=500, temperature=0.8, top_k=40, top_p=0.9):
-    if model is None or tokenizer is None:
-        return "Error: Model not loaded properly"
-        
-    try:
-        # Encode the input prompt
-        input_ids = tokenizer.encode(prompt, return_tensors='pt')
-        
-        # Generate text
-        with torch.no_grad():
-            output = model.generate(
-                input_ids,
-                max_length=max_length,
-                temperature=temperature,
-                top_k=top_k,
-                top_p=top_p,
-                do_sample=True,
-                pad_token_id=tokenizer.eos_token_id,
-                num_return_sequences=1
-            )
-        
-        # Decode and return the generated text
-        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
-        return generated_text
-    except Exception as e:
-        return f"Error during generation: {str(e)}"
-
-# Load model and tokenizer globally
-print("Loading model and tokenizer...")
-model, tokenizer = load_model()
-
-# Create Gradio interface
-demo = gr.Interface(
-    fn=generate_text,
-    inputs=[
-        gr.Textbox(label="Enter your prompt", placeholder="Start your text here...", lines=2),
-        gr.Slider(minimum=10, maximum=1000, value=500, step=10, label="Maximum Length"),
-        gr.Slider(minimum=0.1, maximum=2.0, value=0.8, step=0.1, label="Temperature"),
-        gr.Slider(minimum=1, maximum=100, value=40, step=1, label="Top-k"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.1, label="Top-p"),
-    ],
-    outputs=gr.Textbox(label="Generated Text", lines=10),
-    title="Shakespeare-style Text Generator",
-    description="""Generate Shakespeare-style text using a fine-tuned GPT-2 model.
-    
-    Parameters:
-    - Temperature: Higher values make the output more random, lower values more focused
-    - Top-k: Number of highest probability vocabulary tokens to keep for top-k filtering
-    - Top-p: Cumulative probability for nucleus sampling
-    """,
-    examples=[
-        ["First Citizen:", 500, 0.8, 40, 0.9],
-        ["To be, or not to be,", 500, 0.8, 40, 0.9],
-        ["Friends, Romans, countrymen,", 500, 0.8, 40, 0.9],
-        ["O Romeo, Romeo,", 500, 0.8, 40, 0.9],
-        ["Now is the winter of our discontent", 500, 0.8, 40, 0.9]
-    ]
-)
-
-# Launch the app
-if __name__ == "__main__":
-    demo.launch() 
+