Update app.py

app.py

@@ -1,42 +1,43 @@
-import streamlit as st
-import pickle
-import faiss
-import numpy as np
-from transformers import pipeline
-
-# Load saved quotes and embeddings
-with open("model/quote_embeddings.pkl", "rb") as f:
-    quotes, embeddings = pickle.load(f)
-
-# Initialize embedder and FAISS index
-embedder = SentenceTransformer('all-MiniLM-L6-v2')
-
-embeddings = embeddings.astype('float32')
-index = faiss.IndexFlatIP(embeddings.shape[1])
-faiss.normalize_L2(embeddings)
-index.add(embeddings)
-
-# Initialize text generator pipeline
-generator = pipeline('text-generation', model='distilgpt2')
-
-# Define RAG search function
-def rag_search(query, top_k=3):
-    q_emb = embedder.encode([query]).astype('float32')
-    faiss.normalize_L2(q_emb)
-    scores, indices = index.search(q_emb, top_k)
-    context = "\n".join([f"{quotes[i]['quote']} — {quotes[i].get('author','Unknown')}" for i in indices[0]])
-    prompt = f"Answer using these quotes:\n{context}\nQuestion: {query}\nAnswer:"
-    outputs = generator(prompt, max_length=100, num_return_sequences=1)
-    answer = outputs[0]['generated_text'].split('Answer:')[-1].strip()
-    return answer
-
-# Streamlit UI starts here
-st.title("RAG Quote-Based Q&A")
-
-user_query = st.text_input("Ask a question related to quotes:")
-
-if user_query:
-    with st.spinner("Generating answer..."):
-        answer = rag_search(user_query)
-    st.markdown("### Answer:")
-    st.write(answer)
+import streamlit as st
+import pickle
+import faiss
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from transformers import pipeline
+
+# Load saved quotes and embeddings from root directory
+with open("quote_embeddings.pkl", "rb") as f:
+    quotes, embeddings = pickle.load(f)
+
+# Initialize embedder and FAISS index
+embedder = SentenceTransformer('all-MiniLM-L6-v2')
+
+embeddings = embeddings.astype('float32')
+index = faiss.IndexFlatIP(embeddings.shape[1])
+faiss.normalize_L2(embeddings)
+index.add(embeddings)
+
+# Initialize text generator pipeline
+generator = pipeline('text-generation', model='distilgpt2')
+
+# Define RAG search function
+def rag_search(query, top_k=3):
+    q_emb = embedder.encode([query]).astype('float32')
+    faiss.normalize_L2(q_emb)
+    scores, indices = index.search(q_emb, top_k)
+    context = "\n".join([f"{quotes[i]['quote']} — {quotes[i].get('author','Unknown')}" for i in indices[0]])
+    prompt = f"Answer using these quotes:\n{context}\nQuestion: {query}\nAnswer:"
+    outputs = generator(prompt, max_length=100, num_return_sequences=1)
+    answer = outputs[0]['generated_text'].split('Answer:')[-1].strip()
+    return answer
+
+# Streamlit UI starts here
+st.title("RAG Quote-Based Q&A")
+
+user_query = st.text_input("Ask a question related to quotes:")
+
+if user_query:
+    with st.spinner("Generating answer..."):
+        answer = rag_search(user_query)
+    st.markdown("### Answer:")
+    st.write(answer)