ayushsinha commited on
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  1. app.py +44 -0
  2. requirements.txt +8 -0
app.py ADDED
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+ import gradio as gr
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+ from transformers import pipeline
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+
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+ # Load the GPT-2 next-line prediction model
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+ model_name = "AventIQ-AI/gpt2-lmheadmodel-next-line-prediction-model"
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+ generator = pipeline("text-generation", model=model_name)
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+
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+ def generate_next_line(prompt):
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+ """Generates the next line of text based on the input prompt."""
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+ if not prompt.strip():
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+ return "⚠️ Please enter a prompt."
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+
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+ response = generator(prompt, max_length=len(prompt.split()) + 10, num_return_sequences=1)
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+ return response[0]["generated_text"]
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+
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+ # Example Inputs
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+ example_prompts = [
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+ "Once upon a time, a young explorer discovered",
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+ "The AI revolutionized the way people interacted with",
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+ "In the distant future, humans and robots lived together in",
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+ "The detective examined the crime scene and found a"
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+ ]
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+
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+ # Create Gradio UI
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+ with gr.Blocks() as demo:
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+ gr.Markdown("## 📝 Next-Line Prediction with GPT-2")
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+ gr.Markdown("Enter a sentence, and the model will predict the next line!")
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+
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+ with gr.Row():
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+ input_text = gr.Textbox(label="✍️ Enter your text:", placeholder="Once upon a time...")
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+
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+ generate_button = gr.Button("🔮 Generate Next Line")
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+ output_text = gr.Textbox(label="📜 Generated Text:")
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+
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+ gr.Markdown("### ✨ Example Inputs")
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+ example_buttons = [gr.Button(example) for example in example_prompts]
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+
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+ for btn in example_buttons:
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+ btn.click(fn=lambda text=btn.value: text, outputs=input_text)
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+
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+ generate_button.click(generate_next_line, inputs=input_text, outputs=output_text)
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+
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+ # Launch the Gradio app
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+ demo.launch()
requirements.txt ADDED
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+ torch
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+ transformers
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+ gradio
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+ sentencepiece
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+ torchvision
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+ huggingface_hub
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+ pillow
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+ numpy