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app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the GPT-2 News Generation Model
+model_name = "AventIQ-AI/gpt2-news-article-generation"
+generator = pipeline("text-generation", model=model_name)
+
+# Predefined headline suggestions
+headline_suggestions = [
+    "Breaking: Stock Market Hits Record High",
+    "Scientists Discover New Treatment for Alzheimer's",
+    "Tech Giants Compete in AI Race",
+    "Severe Weather Warnings Issued Across the Country",
+    "New Law Passed to Improve Cybersecurity Standards"
+]
+
+def generate_news_article(headline, max_length=250):
+    """Generate a news article based on the given headline."""
+    response = generator(headline, max_length=max_length, num_return_sequences=1)
+    return response[0]["generated_text"]
+
+# Create Gradio UI
+with gr.Blocks(theme="default") as demo:
+    gr.Markdown("## 📰 GPT-2 News Article Generator")
+    gr.Markdown("🔍 Enter a **news headline**, and the model will generate a news article based on it.")
+
+    headline_input = gr.Textbox(placeholder="Enter a news headline...", label="News Headline")
+    suggestion_dropdown = gr.Dropdown(choices=headline_suggestions, label="💡 Select a Sample Headline (Optional)")
+    generate_button = gr.Button("📝 Generate Article")
+    output_box = gr.Textbox(label="Generated News Article", interactive=False)
+
+    # Function to update input field with selected suggestion
+    def update_headline(suggestion):
+        return suggestion
+
+    suggestion_dropdown.change(update_headline, inputs=[suggestion_dropdown], outputs=[headline_input])
+    generate_button.click(generate_news_article, inputs=[headline_input], outputs=[output_box])
+
+demo.launch()

requirements.txt

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