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app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the GPT-2 next-line prediction model
+model_name = "AventIQ-AI/gpt2-lmheadmodel-next-line-prediction-model"
+generator = pipeline("text-generation", model=model_name)
+
+def generate_next_line(prompt):
+    """Generates the next line of text based on the input prompt."""
+    if not prompt.strip():
+        return "⚠️ Please enter a prompt."
+
+    response = generator(prompt, max_length=len(prompt.split()) + 10, num_return_sequences=1)
+    return response[0]["generated_text"]
+
+# Example Inputs
+example_prompts = [
+    "Once upon a time, a young explorer discovered",
+    "The AI revolutionized the way people interacted with",
+    "In the distant future, humans and robots lived together in",
+    "The detective examined the crime scene and found a"
+]
+
+# Create Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 📝 Next-Line Prediction with GPT-2")
+    gr.Markdown("Enter a sentence, and the model will predict the next line!")
+
+    with gr.Row():
+        input_text = gr.Textbox(label="✍️ Enter your text:", placeholder="Once upon a time...")
+
+    generate_button = gr.Button("🔮 Generate Next Line")
+    output_text = gr.Textbox(label="📜 Generated Text:")
+
+    gr.Markdown("### ✨ Example Inputs")
+    example_buttons = [gr.Button(example) for example in example_prompts]
+
+    for btn in example_buttons:
+        btn.click(fn=lambda text=btn.value: text, outputs=input_text)
+
+    generate_button.click(generate_next_line, inputs=input_text, outputs=output_text)
+
+# Launch the Gradio app
+demo.launch()

requirements.txt

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