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app.py

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+import streamlit as st
+import textwrap
+import pandas as pd
+import time
+from sentence_transformers import SentenceTransformer, util
+from annoy import AnnoyIndex
+
+footer = """
+<p style='text-align: center; color: gray;'>Made with inspiration by Abhijeet Singh</p>
+"""
+
+shlok_keys = ['Title', 'Chapter', 'Verse', 'Hindi Anuvad' , 'Enlgish Translation']
+max_line_length = 100  # Adjust as needed
+
+@st.cache_resource
+def load_data():
+   hn_filepath = 'Gita.xlsx'
+   return pd.read_excel(hn_filepath)
+
+
+@st.cache_resource
+def load_hn_model():
+  return SentenceTransformer('all-mpnet-base-v2')
+
+hn_model = load_hn_model()
+
+@st.cache_resource
+def build_embeddings(hn_data):
+   return [hn_model.encode(hn_data['Enlgish Translation'][i], convert_to_tensor=True).numpy() for i in range(len(hn_data))]
+
+@st.cache_resource
+def build_annoy_index(shloka_embeddings):
+   embedding_size = len(shloka_embeddings[0])
+   annoy_index = AnnoyIndex(embedding_size, metric='angular')
+   for i, embedding in enumerate(shloka_embeddings):
+       annoy_index.add_item(i, embedding)
+   annoy_index.build(18) # 18 trees for faster search
+   return annoy_index
+
+def wrap_text(text):
+   pass
+# st.write("shree ganeshay namah")
+
+hn_data = load_data()
+
+
+
+shloka_embeddings = build_embeddings(hn_data)
+annoy_index = build_annoy_index(shloka_embeddings)
+
+st.title("GitaShlok Bhagavad Gita Assistant")
+
+st.markdown(footer, unsafe_allow_html=True)
+
+st.markdown(
+    """
+    <style>
+        .reportview-container {
+            width: 90%;
+        }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+st.markdown(
+    """
+    <style>
+        .streamlit-text-container {
+            white-space: pre-line;
+        }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+query = st.text_input("Ask any question related to the Bhagavad Gita: ")
+
+if st.button('Ask'):
+
+    query_embedding = hn_model.encode(query, convert_to_tensor=True).numpy()
+
+    # Use Annoy Index for efficient similarity search
+    similar_indices = annoy_index.get_nns_by_vector(query_embedding, 18)
+
+    # Process and display similar Shlokas
+    similarities = []
+    for curr_index in similar_indices:
+        similarity = util.cos_sim(query_embedding, shloka_embeddings[curr_index])
+        curr_shlok_details = {key: hn_data[key][curr_index] for key in hn_data}
+        similarities.append({"shlok_details": curr_shlok_details, "similarity": similarity})
+
+    # Get the most similar Shloka
+    top_result = sorted(similarities, key=lambda x: x["similarity"], reverse=True)[0]
+    top_shlok_details = top_result["shlok_details"]
+    adhyay_number = top_shlok_details['Chapter'].split(" ")[1]
+    shlok_number = top_shlok_details['Verse'].split(" ")[1].split(".")[1]
+
+    st.write("------------------------------")
+    st.write(f"{top_shlok_details['Chapter']} , Shloka : {shlok_number}")
+
+    
+    
+    
+    wrapped_text = textwrap.fill(top_shlok_details['Enlgish Translation'], width=max_line_length)
+    wrapped_hindi_text=textwrap.fill(top_shlok_details['Hindi Anuvad'], width=max_line_length)
+
+    placeholder = st.empty()
+
+    prev_text=''
+    for char in wrapped_text:
+      prev_text=prev_text+char
+      placeholder.text(prev_text)
+      time.sleep(0.01)  # Adjust the sleep duration as needed
+    st.write("\n------------------------------")
+
+    hindi_placeholder = st.empty()
+
+    hindi_text=''
+    for char in wrapped_hindi_text :
+      hindi_text=hindi_text+char
+      hindi_placeholder.text(hindi_text)
+      time.sleep(0.005)  # Adjust the sleep duration as needed
+    st.write("\n------------------------------")
+
+
+