Create app.py

app.py

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+import torch
+import gradio as gr
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+# Load the tokenizer and the model
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+model = GPT2LMHeadModel.from_pretrained('gpt2')
+
+# Load the best model weights
+model.load_state_dict(torch.load('best_model.pth', map_location=torch.device('cpu')))
+
+# Set the model to evaluation mode
+model.eval()
+
+# Define the text generation function
+def generate_text(prompt, max_length=50, num_return_sequences=1):
+    inputs = tokenizer(prompt, return_tensors='pt')
+    outputs = model.generate(
+        inputs.input_ids,
+        max_length=max_length,
+        num_return_sequences=num_return_sequences,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        temperature=1.0
+    )
+    return [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.inputs.Textbox(lines=2, placeholder="Enter your prompt here..."),
+        gr.inputs.Slider(minimum=10, maximum=200, default=50, label="Max Length"),
+        gr.inputs.Slider(minimum=1, maximum=5, default=1, label="Number of Sequences")
+    ],
+    outputs=gr.outputs.Textbox(),
+    title="GPT-2 Text Generator",
+    description="Enter a prompt to generate text using GPT-2.",
+)
+
+# Launch the Gradio interface
+interface.launch()