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Update app.py
Browse files
app.py
CHANGED
@@ -32,6 +32,7 @@ st.sidebar.markdown("__Tool Definition__")
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st.sidebar.markdown("This tool helps you identify possible diseases based on the symptoms you provide.")
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st.sidebar.markdown("This is my project for the KaggleX program.")
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st.sidebar.markdown("Please note that this tool is for informational purposes only. Always consult a healthcare professional for accurate medical advice.")
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# Initialize chat history
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if "messages" not in st.session_state:
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@@ -46,7 +47,7 @@ def preprocess_input(user_input):
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# Function to predict diseases based on user input
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def predict_diseases(user_clean_text):
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user_input_vector = tfidf_vectorizer.transform([
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predictions = model.predict(user_input_vector) # Make predictions using the trained model
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return predictions
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@@ -72,11 +73,7 @@ if st.button("Predict Disease"):
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else:
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st.warning("Please enter your symptoms before predicting.")
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# Display a warning message
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st.warning("Please note that this tool is for informational purposes only. Always consult a healthcare professional for accurate medical advice.")
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# Create FAQs section
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show_faqs = st.checkbox("Show FAQs")
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if show_faqs:
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st.markdown("## Frequently Asked Questions")
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st.markdown("**Q: How does this tool work?**")
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@@ -90,3 +87,4 @@ if show_faqs:
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# Add attribution
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st.markdown("Created with ❤️ by Joas")
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st.sidebar.markdown("This tool helps you identify possible diseases based on the symptoms you provide.")
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st.sidebar.markdown("This is my project for the KaggleX program.")
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st.sidebar.markdown("Please note that this tool is for informational purposes only. Always consult a healthcare professional for accurate medical advice.")
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show_faqs = st.sidebar.checkbox("Show FAQs")
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# Initialize chat history
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if "messages" not in st.session_state:
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# Function to predict diseases based on user input
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def predict_diseases(user_clean_text):
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user_input_vector = tfidf_vectorizer.transform([user_clean_text]) # Vectorize the cleaned user input
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predictions = model.predict(user_input_vector) # Make predictions using the trained model
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return predictions
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else:
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st.warning("Please enter your symptoms before predicting.")
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# Create FAQs section
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if show_faqs:
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st.markdown("## Frequently Asked Questions")
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st.markdown("**Q: How does this tool work?**")
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# Add attribution
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st.markdown("Created with ❤️ by Joas")
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