Create app.py

app.py

@@ -0,0 +1,118 @@
+import gradio as gr
+from transformers import GPTNeoForCausalLM, AutoTokenizer
+import torch
+
+try:
+    # Load the GPT-Neo model and tokenizer
+    model_name = "EleutherAI/gpt-neo-1.3B"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = GPTNeoForCausalLM.from_pretrained(model_name)
+    
+    # Set device
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    
+except Exception as e:
+    print(f"Error loading model: {e}")
+    raise
+
+def generate_text(prompt, max_length=100, temperature=0.7, top_p=0.9):
+    """
+    Generate text using GPT-Neo model with error handling
+    """
+    try:
+        if not prompt or len(prompt.strip()) == 0:
+            return "Error: Please enter a prompt."
+        
+        # Tokenize input
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
+        
+        # Generate text
+        with torch.no_grad():
+            output = model.generate(
+                input_ids,
+                max_length=max_length,
+                temperature=temperature,
+                top_p=top_p,
+                do_sample=True,
+                pad_token_id=tokenizer.eos_token_id
+            )
+        
+        # Decode output
+        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+        return generated_text
+    
+    except RuntimeError as e:
+        return f"Memory Error: {str(e)}. Try reducing max_length."
+    except Exception as e:
+        return f"Error generating text: {str(e)}"
+
+# Create Gradio interface
+with gr.Blocks(title="GPT-Neo Text Generation") as demo:
+    gr.Markdown("# GPT-Neo 1.3B Text Generation")
+    gr.Markdown("Generate creative text using the EleutherAI GPT-Neo 1.3B model")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(
+                label="Enter your prompt",
+                placeholder="Start typing your prompt...",
+                lines=3
+            )
+            
+            with gr.Row():
+                max_length_slider = gr.Slider(
+                    minimum=10,
+                    maximum=200,
+                    value=100,
+                    step=10,
+                    label="Max Length"
+                )
+            
+            with gr.Row():
+                temperature_slider = gr.Slider(
+                    minimum=0.1,
+                    maximum=2.0,
+                    value=0.7,
+                    step=0.1,
+                    label="Temperature"
+                )
+                
+                top_p_slider = gr.Slider(
+                    minimum=0.1,
+                    maximum=1.0,
+                    value=0.9,
+                    step=0.05,
+                    label="Top P"
+                )
+            
+            generate_button = gr.Button("Generate Text", variant="primary")
+        
+        with gr.Column():
+            output_text = gr.Textbox(
+                label="Generated Text",
+                lines=10,
+                interactive=False
+            )
+    
+    # Connect button click to generation function
+    generate_button.click(
+        fn=generate_text,
+        inputs=[prompt_input, max_length_slider, temperature_slider, top_p_slider],
+        outputs=output_text
+    )
+    
+    # Add examples
+    gr.Examples(
+        examples=[
+            ["Once upon a time"],
+            ["The future of AI is"],
+            ["In a galaxy far away"],
+            ["Machine learning is"],
+        ],
+        inputs=prompt_input,
+        label="Example Prompts"
+    )
+
+if __name__ == "__main__":
+    demo.launch()