Create gradio_app.py

gradio_app.py

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+import gradio as gr
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+config = PeftConfig.from_pretrained("clemsadand/quote_generator")
+base_model = AutoModelForCausalLM.from_pretrained("gpt2")
+model = PeftModel.from_pretrained(base_model, "clemsadand/quote_generator")
+
+tokenizer = AutoTokenizer.from_pretrained("gpt2")
+
+def generate_quote(input_text):
+    input_tensor = tokenizer(input_text, return_tensors="pt")
+    output = model.generate(input_tensor["input_ids"], attention_mask=input_tensor["attention_mask"],
+                          max_length=64, num_beams=5, no_repeat_ngram_size=2,
+                          early_stopping=True, pad_token_id=tokenizer.eos_token_id, do_sample=True, temperature=0.7)
+    output = tokenizer.decode(output[0], ski_special_tokens=True, clean_up_tokenization_spaces=True)
+    return ".".join(output.split(".")[1:-1]).replace("\n", "").replace("->:", "") + "."
+
+input_text = "Generate a quote about kindness with the keywords compassion, empathy, help, generosity, care"
+# print(generate_quote(input_text))
+
+input_textbox = gr.Textbox(label="Prompt", placeholder="Generate a quote about kindness with the keywords compassion, empathy, help, generosity, care", lines=2)
+output_textbox = gr.Textbox(label="Generated quote", placeholder="", lines=4)
+
+demo = gr.Interface(fn=generate_quote, inputs=input_textbox, outputs=output_textbox)
+
+demo.launch()