first commit

app.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import joblib
+import ast
+
+st.set_page_config(page_title="App for predicting RNA-Sq prediction")
+
+def load_lr_model():
+    return joblib.load('logistic_regression_model.pkl')
+
+model = load_lr_model()
+
+st.title('Model Serving Web App for predicting the Tumor in RNA sequence')
+st.caption('Get RNA-sq Predictions From The Latest Model.')
+
+# Create a horizontal line, and then a new container.
+st.markdown("---")
+
+with st.container():
+
+    uploaded_file = st.file_uploader("Upload RNA-sq txt file", type="txt")
+
+    if uploaded_file is not None:
+        rna_seq = uploaded_file.getvalue().decode("utf-8").splitlines()
+
+        trigger = st.button('Make Prediction')
+
+        if trigger:
+            st.info("Loading the data for predictions")
+            data = ast.literal_eval(rna_seq[0])
+            data = np.asarray(list(data)).reshape(1, -1)
+            predicted_labels = model.predict(data)
+            if int(predicted_labels[0]) == 1:
+                st.success("Tumor found in the RNA-Sq :thumbsup:")
+            else:
+                st.error('Tumor not found in the RNA-Sq :thumbsdown:')
+
+# Add text about supported tissues for tumor prediction.
+st.subheader("Supported Tissues for Tumor Prediction")
+tissue_list = [
+    "Head/Neck", "Lung", "Kidney", "Colon", "Liver", "Breast", "Prostate"
+]
+st.write(", ".join(tissue_list) + ".")

requirements.txt

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+numpy
+pandas
+joblib