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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import os
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from typing import Generator
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# Initialize Inference API client
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# Try AstroSage-Llama-3.1-8B; fallback to Llama-3.2-3B-Instruct if unsupported
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model = "AstroMLab/AstroSage-Llama-3.1-8B" # Replace with "meta-llama/Llama-3.2-3B-Instruct" if needed
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client = InferenceClient(model=model, token=os.getenv("HF_TOKEN"))
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def generate_astronomy_explanation(prompt: str) -> Generator[str, None, None]:
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"""
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Generates astronomy-related explanations using the model via Inference API with streaming.
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"""
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system_prompt = (
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"You are AstroSage, an expert astronomy assistant like Grok. Given a user prompt about "
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"astronomical phenomena, space exploration, stars, galaxies, cosmology, planetary science, "
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"or astrophysics, provide a clear, educational explanation. Stream the output line by line. "
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"Use bullet points for key points and keep responses concise yet informative."
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)
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt}
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]
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# Stream output from Inference API using chat_completion
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for chunk in client.chat_completion(
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messages=messages,
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max_tokens=1000, # Balanced for complete answers and speed
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temperature=0.7,
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top_p=0.9,
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stream=True
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):
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# Extract content from the chunk
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content = chunk.choices[0].delta.content
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if content: # Only yield non-empty content
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yield content
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# Gradio interface with streaming
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def live_astronomy_generator(prompt: str):
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"""
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Handles streaming output for the Gradio interface.
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"""
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output = ""
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for chunk in generate_astronomy_explanation(prompt):
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output += chunk
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yield output
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# Gradio app
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with gr.Blocks() as demo:
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gr.Markdown("# Invescoz AI Studio: Astronomy Explainer")
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prompt_input = gr.Textbox(
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label="Enter your astronomy question",
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placeholder="e.g., Explain how black holes form or Describe the lifecycle of a star"
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)
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output_display = gr.Textbox(label="Astronomy Explanation", interactive=False, lines=10)
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submit_button = gr.Button("Generate")
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submit_button.click(
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fn=live_astronomy_generator,
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inputs=prompt_input,
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outputs=output_display
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)
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# Launch the app (handled by Hugging Face Spaces)
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demo.launch()
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