app.py

pip install streamlit transformers
import streamlit as st
from transformers import pipeline

# Load the medical diagnosis model from Hugging Face Transformers
diagnosis_model = pipeline("text-classification", model="your-medical-diagnosis-model-name")

def main():
    st.title("Medical Diagnosis Assistant")

    # Text input for user to enter symptoms
    symptoms = st.text_area("Enter symptoms (separated by commas):")

    if st.button("Diagnose"):
        if symptoms:
            # Perform medical diagnosis
            diagnosis_result = diagnose(symptoms)
            st.success(f"Diagnosis: {diagnosis_result}")
        else:
            st.warning("Please enter symptoms for diagnosis.")

def diagnose(symptoms):
    # Call the Hugging Face model for medical diagnosis
    result = diagnosis_model(symptoms)

    # Extract the predicted label
    label = result[0]["label"]

    return label

if __name__ == "__main__":
    main()
import streamlit as st
from transformers import pipeline

# Load the medical diagnosis model from Hugging Face Transformers
diagnosis_model = pipeline("text-classification", model="your-medical-diagnosis-model-name")

def main():
    st.title("Medical Diagnosis Assistant")

    # Text input for user to enter symptoms
    symptoms = st.text_area("Enter symptoms (separated by commas):")

    if st.button("Diagnose"):
        if symptoms:
            # Perform medical diagnosis
            diagnosis_result = diagnose(symptoms)
            st.success(f"Diagnosis: {diagnosis_result}")
        else:
            st.warning("Please enter symptoms for diagnosis.")

def diagnose(symptoms):
    # Call the Hugging Face model for medical diagnosis
    result = diagnosis_model(symptoms)

    # Extract the predicted label
    label = result[0]["label"]

    return label

if __name__ == "__main__":
    main()
import streamlit as st
from transformers import pipeline

# Load the medical diagnosis model from Hugging Face Transformers
diagnosis_model = pipeline("text-classification", model="your-medical-diagnosis-model-name")

def main():
    st.title("Medical Diagnosis Assistant")

    # Text input for user to enter symptoms
    symptoms = st.text_area("Enter symptoms (separated by commas):")

    if st.button("Diagnose"):
        if symptoms:
            # Perform medical diagnosis
            diagnosis_result = diagnose(symptoms)
            st.success(f"Diagnosis: {diagnosis_result}")
        else:
            st.warning("Please enter symptoms for diagnosis.")

def diagnose(symptoms):
    # Call the Hugging Face model for medical diagnosis
    result = diagnosis_model(symptoms)

    # Extract the predicted label
    label = result[0]["label"]

    return label

if __name__ == "__main__":
    main()
streamlit run medical_diagnosis_app.py