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app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+model_name = "AventIQ-AI/gpt2-book-article-recommendation"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def recommend_titles(alphabet, num_recommendations=5):
+    """Generate book/article recommendations based on an input alphabet."""
+    input_text = alphabet.strip()
+    if not input_text:
+        return ["⚠️ Please enter a valid letter."]
+    
+    input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device)
+    
+    with torch.no_grad():
+        outputs = model.generate(input_ids, max_length=15, num_return_sequences=num_recommendations, do_sample=True)
+    
+    return [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
+
+# Example Inputs
+example_inputs = ["A", "B", "C", "D", "E"]
+
+# Create Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("## 📚 AI-Powered Book & Article Recommendation")
+    gr.Markdown("Enter a letter, and the AI will suggest relevant book or article titles!")
+    
+    with gr.Row():
+        alphabet_input = gr.Textbox(label="🔠 Enter a Letter:", placeholder="Example: A")
+        num_recommendations = gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Number of Recommendations")
+    
+    recommend_button = gr.Button("📖 Get Recommendations")
+    output_text = gr.Textbox(label="📄 Recommended Titles:", lines=6)
+    
+    gr.Markdown("### 🎯 Example Inputs")
+    example_buttons = [gr.Button(example) for example in example_inputs]
+    
+    for btn in example_buttons:
+        btn.click(fn=lambda letter=btn.value: letter, outputs=alphabet_input)
+    
+    recommend_button.click(recommend_titles, inputs=[alphabet_input, num_recommendations], outputs=output_text)
+
+demo.launch()

requirements.txt

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+torch
+transformers
+gradio
+sentencepiece
+torchvision
+huggingface_hub
+pillow
+numpy