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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
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