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import streamlit as st |
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from transformers import AutoModel, AutoTokenizer |
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from transformers import LlamaTokenizer, LlamaForCausalLM |
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from biomistral import BioMistral |
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tokenizer = LlamaTokenizer.from_pretrained("BioMistral/BioMistral-7B") |
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model = LlamaForCausalLM.from_pretrained("BioMistral/BioMistral-7B") |
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biomistral = BioMistral(model=model, tokenizer=tokenizer) |
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tokenizer = AutoTokenizer.from_pretrained("BioMistral/BioMistral-7B") |
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model = AutoModel.from_pretrained("BioMistral/BioMistral-7B") |
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def generate_response(input_text): |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=200, do_sample=True, top_p=0.95, top_k=50, num_return_sequences=1) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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return response |
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def main(): |
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st.title("Doctor AI App") |
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symptoms = st.text_area("Enter your symptoms", height=150) |
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medical_history = st.text_area("Enter your medical history", height=150) |
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allergies = st.text_area("Enter your allergies (if any)", height=150) |
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if st.button("Get Diagnosis and Recommendations"): |
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input_text = f"Based on the following information:\n\nSymptoms: {symptoms}\nMedical History: {medical_history}\nAllergies: {allergies}\n\nPlease provide a diagnosis and recommend medications or treatments." |
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response = generate_response(input_text) |
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st.write(response) |
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if __name__ == "__main__": |
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main() |
