Update to load fine-tuned model from Hugging Face Hub

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load the fine-tuned model and tokenizer
+model_name = "./fine-tuned-distilgpt2"  # Path to the saved fine-tuned model
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def generate_copy(book_title, book_author, book_genre, book_themes, book_description):
+    prompt = f"""You are a marketing copywriter for an online bookstore.
+Given the following book details, write three compelling landing page headlines and a short product description:
+
+TITLE: {book_title}
+AUTHOR: {book_author}
+GENRE: {book_genre}
+KEY THEMES: {book_themes}
+DESCRIPTION: {book_description}
+
+Landing Page Copy:
+"""
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+    output = model.generate(
+        inputs, 
+        max_length=200, 
+        temperature=0.7, 
+        top_p=0.9, 
+        do_sample=True
+    )
+    result = tokenizer.decode(output[0], skip_special_tokens=True)
+    final_output = result.split("Landing Page Copy:")[-1].strip()
+    return final_output
+
+# Gradio UI
+title_input = gr.Textbox(label="Book Title")
+description_input = gr.Textbox(label="Book Description")
+author_input = gr.Textbox(label="Author")
+genre_input = gr.Textbox(label="Genre")
+themes_input = gr.Textbox(label="Key Themes")
+
+demo = gr.Interface(
+    fn=generate_copy, 
+    inputs=[title_input, author_input, genre_input, themes_input, description_input], 
+    outputs="text",
+    title="Dynamic Landing Page Copy Generator",
+    description="Enter book details and get compelling marketing copy."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+transformers
+torch
+datasets