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| import streamlit as st | |
| from transformers import pipeline | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained("medicalai/ClinicalGPT-base-zh") | |
| model = AutoModelForCausalLM.from_pretrained("medicalai/ClinicalGPT-base-zh") | |
| import os | |
| # Initialize the Hugging Face pipelines for multiple models | |
| models = { | |
| "ClinicalGPT-base-zh": pipeline("text-generation", model="ClinicalGPT-base-zh") | |
| } | |
| # Function to get medical diagnosis using all models | |
| def get_medical_response(patient_name, age, sex, symptoms, xray_mri=None, medical_reports=None): | |
| # Prepare the input message with the provided patient details | |
| message_content = f"Patient Details:\nName: {patient_name}\nAge: {age}\nSex: {sex}\nSymptoms: {symptoms}" | |
| # If X-ray/MRI file is provided, include it | |
| if xray_mri: | |
| message_content += f"\nX-ray/MRI: {xray_mri}" # File path or additional info | |
| # If medical reports file is provided, include it | |
| if medical_reports: | |
| message_content += f"\nMedical Reports: {medical_reports}" # File path or additional info | |
| # Dictionary to store results from each model | |
| model_results = {} | |
| # Iterate over each model and get the response | |
| for model_name, model_pipeline in models.items(): | |
| try: | |
| result = model_pipeline(message_content, max_length=300) | |
| model_results[model_name] = result[0]['generated_text'] | |
| except Exception as e: | |
| model_results[model_name] = f"Error: {str(e)}" # Return the error message if something goes wrong | |
| return model_results | |
| # Streamlit UI | |
| def main(): | |
| st.title("Medical Diagnosis Assistant") | |
| # Collect patient details | |
| patient_name = st.text_input("Patient Name") | |
| age = st.number_input("Age", min_value=0) | |
| sex = st.radio("Sex", options=["Male", "Female", "Other"]) | |
| symptoms = st.text_area("Medical Symptoms") | |
| # Optional file inputs | |
| xray_mri = st.file_uploader("Upload X-ray/MRI Image (Optional)", type=["jpg", "jpeg", "png", "dcm", "pdf"]) | |
| medical_reports = st.file_uploader("Upload Medical Reports (Optional)", type=["pdf", "txt", "docx"]) | |
| if st.button("Submit"): | |
| # Get medical diagnosis using all models | |
| model_results = get_medical_response(patient_name, age, sex, symptoms, xray_mri.name if xray_mri else None, medical_reports.name if medical_reports else None) | |
| # Display the results for each model | |
| for model_name, diagnosis in model_results.items(): | |
| st.subheader(f"Diagnosis from {model_name}:") | |
| st.text_area(f"Diagnosis - {model_name}", diagnosis, height=300) | |
| if __name__ == "__main__": | |
| main() | |