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Create app.py
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app.py
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import gradio as gr
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from huggingface_hub import InferenceClient
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# Initialize the Hugging Face Inference Client
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client = InferenceClient()
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# Function to stream simplified text as it is generated
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def simplify_text(input_text, audience):
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prompt = f"Simplify the following text for a '{audience}' audience:\n\n{input_text}"
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messages = [
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{"role": "user", "content": prompt}
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]
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# Create a stream to receive generated content
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stream = client.chat.completions.create(
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model="Qwen/QwQ-32B-Preview",
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messages=messages,
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temperature=0.7,
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max_tokens=1024,
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top_p=0.8,
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stream=True
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)
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# Stream content as it is generated
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simplified_output = ""
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for chunk in stream:
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simplified_output += chunk.choices[0].delta.content
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yield simplified_output # Yield incremental content to display immediately
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# Create Gradio interface with the modified layout
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with gr.Blocks() as app:
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gr.Markdown("## Contextual Text Simplifier")
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gr.Markdown("Simplify complex text into context-appropriate language for specific audiences using AI.")
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with gr.Row():
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# First column for input components
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with gr.Column():
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text_input = gr.Textbox(
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lines=6,
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label="Input Text",
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placeholder="Paste the text you want to simplify here.",
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elem_id="full_width"
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)
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audience = gr.Dropdown(
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choices=["Children", "General Public", "Professionals"],
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label="Target Audience",
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value="General Public"
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)
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simplify_button = gr.Button("Simplify Text")
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# Second column for output
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with gr.Column():
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gr.Markdown("### Simplified Text") # This acts as the label for the output
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output_markdown = gr.Markdown()
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# Link button to function with inputs and outputs
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simplify_button.click(fn=simplify_text, inputs=[text_input, audience], outputs=output_markdown)
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# Run the Gradio app
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app.launch(debug=True)
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