Upload app.py

app.py

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+# import streamlit as st
+# from pymongo import MongoClient
+# import pandas as pd
+
+# # Initialize MongoDB client
+# client = MongoClient('mongodb+srv://vazeswaroop:[email protected]/')
+# db = client['word_classification_db']
+# collection = db['word_classification']
+
+# # Function to save word and its category
+# def save_word(word, category):
+#     collection.insert_one({"word": word, "category": category})
+
+# # Function to load words
+# def load_words():
+#     return pd.DataFrame(list(collection.find({}, {'_id': 0})))
+
+# # Streamlit UI
+# st.title('Word Classification')
+
+# # Input fields for word and category
+# word = st.text_input('Enter Word')
+# category = st.selectbox('Select Category', ['Theme', 'Subtheme', 'Keywords'])
+
+# if st.button('Save Word'):
+#     save_word(word, category)
+#     st.success(f'Word "{word}" saved under category "{category}"!')
+
+# if st.button('View All Entries'):
+#     df = load_words()
+#     st.dataframe(df)
+
+# # Close the MongoDB connection when the app is done
+# client.close()
+import streamlit as st
+from pymongo import MongoClient
+import pandas as pd
+
+# Initialize MongoDB client
+client = MongoClient('mongodb+srv://vazeswaroop:[email protected]/')
+db = client['word_classification_db']
+collection = db['word_classification']
+
+# Function to save word and its category
+def save_word(word, category):
+    collection.insert_one({"word": word, "category": category})
+
+# Function to load words from the DB
+def load_words():
+    return pd.DataFrame(list(collection.find({}, {'_id': 0})))
+
+# Streamlit UI
+st.title('Word Classification and Prompt Matching')
+
+# Input fields for word and category
+word = st.text_input('Enter Word')
+category = st.selectbox('Select Category', ['Theme', 'Subtheme', 'Keywords'])
+
+if st.button('Save Word'):
+    save_word(word, category)
+    st.success(f'Word "{word}" saved under category "{category}"!')
+
+if st.button('View All Entries'):
+    df = load_words()
+    st.dataframe(df)
+
+# Prompt Input for Matching
+st.header('Prompt Matching')
+prompt = st.text_area('Enter a prompt to check for matches')
+
+if st.button('Check Prompt for Matches'):
+    df = load_words()
+    
+    # Separate the words by categories
+    keywords = df[df['category'] == 'Keywords']['word'].tolist()
+    themes = df[df['category'] == 'Theme']['word'].tolist()
+    subthemes = df[df['category'] == 'Subtheme']['word'].tolist()
+
+    # Function to count matches
+    def count_matches(prompt, words_list):
+        return [word for word in words_list if word in prompt]
+
+    # Get the matches
+    matched_keywords = count_matches(prompt, keywords)
+    matched_themes = count_matches(prompt, themes)
+    matched_subthemes = count_matches(prompt, subthemes)
+
+    # Display the count and matched words
+    st.write(f"Number of Keywords matched: {len(matched_keywords)}")
+    st.write(f"Matched Keywords: {matched_keywords}")
+    st.write(f"Number of Themes matched: {len(matched_themes)}")
+    st.write(f"Matched Themes: {matched_themes}")
+    st.write(f"Number of Subthemes matched: {len(matched_subthemes)}")
+    st.write(f"Matched Subthemes: {matched_subthemes}")
+
+# Close the MongoDB connection when the app is done
+client.close()