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app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load Model
+model_name = "AventIQ-AI/gpt2-next-word-prediction"
+predictor = pipeline("text-generation", model=model_name)
+
+def predict_next_word(prompt):
+    result = predictor(prompt, max_length=len(prompt.split()) + 1, num_return_sequences=1)
+    return result[0]['generated_text']
+
+# Examples
+examples = [
+    ["Artificial intelligence is"],
+    ["The future of technology"],
+    ["Machine learning enables"],
+    ["Deep learning models are"],
+]
+
+# Gradio Interface
+def main():
+    with gr.Blocks(theme="soft") as demo:
+        gr.Markdown("""
+        # 🚀 Next-Word Prediction
+        Enter a partial sentence, and the model will predict the next word.
+        """)
+        
+        with gr.Row():
+            input_text = gr.Textbox(label="Enter a sentence", placeholder="Type here...")
+        
+        predict_btn = gr.Button("🔮 Predict Next Word")
+        output_text = gr.Textbox(label="Predicted Sentence", interactive=False)
+        
+        predict_btn.click(predict_next_word, inputs=input_text, outputs=output_text)
+        
+        gr.Examples(examples, inputs=input_text)
+        
+    demo.launch()
+
+if __name__ == "__main__":
+    main()

requirements.txt

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