app.py

app.py

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+import streamlit as st
+from importlib.machinery import PathFinder
+import io
+import netrc
+import pickle
+import sys
+import pandas as pd
+import numpy as np
+import streamlit as st
+# let's import sentence transformer
+import sentence_transformers
+import torch
+#######################################
+
+st.markdown(
+    f"""
+<style>
+    .reportview-container .main .block-container{{
+        max-width: 90%;
+        padding-top: 5rem;
+        padding-right: 5rem;
+        padding-left: 5rem;
+        padding-bottom: 5rem;
+    }}
+    img{{
+    	max-width:40%;
+    	margin-bottom:40px;
+    }}
+</style>
+""",
+    unsafe_allow_html=True,
+)
+
+# # let's load the saved model
+loaded_model = pickle.load(open('XpathFinder1.sav', 'rb'))
+
+
+# Containers
+header_container = st.container()
+mod_container = st.container()
+
+# Header
+with header_container:
+
+    # different levels of text you can include in your app
+    st.title("Xpath Finder App")
+
+
+# model container
+with mod_container:
+
+    # collecting input from user
+    prompt = st.text_input("Enter your description below ...")
+
+    # Loading e data
+    data = (pd.read_csv("/content/SBERT_data.csv")
+            ).drop(['Unnamed: 0'], axis=1)
+
+    data['prompt'] = prompt
+    data.rename(columns={'target_text': 'sentence2',
+                'prompt': 'sentence1'}, inplace=True)
+    data['sentence2'] = data['sentence2'].astype('str')
+    data['sentence1'] = data['sentence1'].astype('str')
+
+    # let's pass the input to the loaded_model with torch compiled with cuda
+    if prompt:
+        # let's get the result
+        simscore = PathFinder.predict([prompt])
+
+        from sentence_transformers import CrossEncoder
+        XpathFinder = CrossEncoder("cross-encoder/stsb-roberta-base")
+        sentence_pairs = []
+        for sentence1, sentence2 in zip(data['sentence1'], data['sentence2']):
+          sentence_pairs.append([sentence1, sentence2])
+
+        # sorting the df to get highest scoring xpath_container
+        data['SBERT CrossEncoder_Score'] = XpathFinder.predict(sentence_pairs)
+        most_acc = data.head(5)
+        # predictions
+        st.write("Highest Similarity score: ", simscore)
+        st.text("Is this one of these the Xpath you're looking for?")
+        st.write(st.write(most_acc["input_text"]))