Create app.py
Browse files
app.py
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import streamlit as st
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load the Biomistral 7b model and tokenizer
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model_name = "biomistral/Biomistral-7b"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
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# Define the text generation function
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def generate_text(prompt, max_length=500, num_return_sequences=1, temperature=0.7):
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input_ids = tokenizer.encode(prompt, return_tensors="pt")
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output = model.generate(
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input_ids,
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max_length=max_length,
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num_return_sequences=num_return_sequences,
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temperature=temperature,
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pad_token_id=tokenizer.eos_token_id,
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)
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generated_text = tokenizer.batch_decode(output, skip_special_tokens=True)
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return generated_text
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# Streamlit app
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def main():
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st.title("Doctor Chatbot (Powered by Biomistral 7b)")
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st.write("Welcome to the Doctor Chatbot. Please describe your symptoms or ask a medical question, and I'll provide a response.")
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user_input = st.text_area("Enter your symptoms or question:")
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if user_input:
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with st.spinner("Generating response..."):
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generated_text = generate_text(user_input)
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st.write(generated_text[0])
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if __name__ == "__main__":
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main()
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