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import gradio as gr
from transformers import pipeline
# Initialize the model
chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium")
# Dictionary of scientist information
SCIENTISTS = {
"Albert Einstein": {
"intro": "theoretical physicist known for the theory of relativity",
"style": "I speak with enthusiasm about physics and use thought experiments to explain complex ideas.",
},
"Marie Curie": {
"intro": "physicist and chemist who conducted pioneering research on radioactivity",
"style": "I am precise in my explanations and passionate about scientific research.",
},
"Nikola Tesla": {
"intro": "inventor and electrical engineer",
"style": "I share my visions about electricity and the future of technology with great enthusiasm.",
}
}
def chat_with_scientist(scientist, question):
# Create a prompt based on the scientist
info = SCIENTISTS[scientist]
prompt = f"""You are {scientist}, {info['intro']}. {info['style']}
Question: {question}
Answer as {scientist}:"""
# Generate response
response = chatbot(
prompt,
max_length=150,
num_return_sequences=1,
temperature=0.7,
do_sample=True
)
return response[0]['generated_text']
# Create the Gradio interface
demo = gr.Interface(
fn=chat_with_scientist,
inputs=[
gr.Dropdown(
choices=list(SCIENTISTS.keys()),
label="Choose a Scientist",
value="Albert Einstein"
),
gr.Textbox(
placeholder="Ask your question here...",
label="Your Question"
)
],
outputs=gr.Textbox(label="Scientist's Response"),
title="Chat with Historical Scientists",
description="""Welcome to the Scientific Time Machine!
Choose a scientist and ask them questions about their work, life, or scientific concepts.
This is an educational tool to help students engage with historical scientists.""",
examples=[
["Albert Einstein", "Can you explain E=mc² in simple terms?"],
["Marie Curie", "What inspired you to study radioactivity?"],
["Nikola Tesla", "Tell me about your work with electricity."]
],
theme=gr.themes.Soft()
)
# Launch the app
if __name__ == "__main__":
demo.launch()
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