Upload gradio_interface.py

gradio_interface.py

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+#inference Gradio
+
+import gradio as gr
+import torch
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+# Load the fine-tuned model and tokenizer
+model_path = 'brunosan/GPT2-impactscience'
+tokenizer_path = 'brunosan/GPT2-impactscience'
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+tokenizer = GPT2Tokenizer.from_pretrained(tokenizer_path)
+model = GPT2LMHeadModel.from_pretrained(model_path).to(device)
+
+# Define the generation function
+def generate_text(prompt):
+    #remove trailing space if any
+    prompt = prompt.rstrip()
+    input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
+    attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=device)
+    outputs = model.generate(input_ids=input_ids, attention_mask=attention_mask,
+                              max_length=100, num_beams=9,
+                              no_repeat_ngram_size=2,
+                              temperature=1.0, do_sample=True,
+                              top_p=0.95, top_k=50)
+
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
+
+# Create a Gradio interface
+input_text = gr.inputs.Textbox(lines=2, label="Enter the starting text")
+output_text = gr.outputs.Textbox(label="Generated Text")
+
+interface = gr.Interface(fn=generate_text, inputs=input_text, outputs=output_text,
+             title="GPT-2 Impact Science Text Generator", description="Generate text using a fine-tuned GPT-2 model onthe Impact Science book.")
+
+if __name__ == "__main__":
+    interface.launch()