Create app.py

app.py

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+# Required Libraries
+import re
+import streamlit as st
+from transformers import pipeline
+from sentence_transformers import SentenceTransformer, util
+
+# Load pre-trained models
+qa_pipeline = pipeline("question-answering")
+sentence_model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
+
+# Load the Chanakya Script
+chanakya_text = """
+BOOK I. Concerning Discipline.
+The end of Sciences; association with the aged; restraint of
+the organs of sense; the creation of ministers; the creation of
+councillors and priests; ascertaining by temptations purity or
+impurity in the character of ministers; the institution of spies.
+Protection of parties for or against one's own cause in one's own
+state; winning over the factions for or against an enemy's cause in
+an enemy's state; the business of council meeting; the mission of
+envoys; protection of princes; the conduct of a prince kept under
+restraint; treatment of a prince kept under restraint; the duties of a
+king; duty towards the harem; personal safety.
+"""
+
+# Preprocess Text
+def preprocess_text(text):
+    sentences = re.split(r'[.;]', text)  # Split into sentences
+    return [sentence.strip() for sentence in sentences if sentence.strip()]
+
+# Embed the sentences for similarity matching
+def get_embeddings(sentences):
+    return sentence_model.encode(sentences, convert_to_tensor=True)
+
+# Match Query with Closest Text
+def get_closest_text(query, sentences, embeddings):
+    query_embedding = sentence_model.encode(query, convert_to_tensor=True)
+    scores = util.pytorch_cos_sim(query_embedding, embeddings)
+    closest_idx = scores.argmax().item()
+    return sentences[closest_idx]
+
+# Preprocess and embed the script
+sentences = preprocess_text(chanakya_text)
+embeddings = get_embeddings(sentences)
+
+# Streamlit App
+st.title("Chanakya GPT")
+st.write("Ask questions about Chanakya's teachings!")
+
+user_query = st.text_input("Enter your question:")
+if user_query:
+    closest_sentence = get_closest_text(user_query, sentences, embeddings)
+    st.write(f"**Chanakya Says:** {closest_sentence}")
+
+    # Using Hugging Face QA model to refine the answer
+    answer = qa_pipeline(question=user_query, context=closest_sentence)
+    st.write(f"**Refined Answer:** {answer['answer']}")