Update app.py

app.py

@@ -1,15 +1,14 @@
-# Step 1: Install necessary libraries (Handled by Hugging Face via 'requirements.txt')
 import streamlit as st
 import spacy
-from spacy.cli import download  # For downloading the spaCy model
+from spacy.cli import download
 import numpy as np
 from numpy.linalg import norm
 
-# Step 2: Download the spaCy model if not already installed
+# Download spaCy model if not already installed
 try:
     nlp = spacy.load("en_core_web_md")
 except OSError:
-    st.warning("Downloading spaCy model 'en_core_web_md'. This may take a few minutes...")
+    st.warning("Downloading the spaCy model. Please wait...")
     download("en_core_web_md")
     nlp = spacy.load("en_core_web_md")
 
@@ -57,47 +56,80 @@ faqs = {
     ]
 }
 
-# Step 4: Precompute vectors for FAQ questions
+# Precompute vectors for FAQ questions
 faq_docs = []
 for category, faq_list in faqs.items():
     for faq in faq_list:
         question = faq['question']
         answer = faq['answer']
-        faq_vector = nlp(question).vector  # Precompute vector
+        faq_vector = nlp(question).vector
         faq_docs.append((question, answer, faq_vector))
 
-# Step 5: Define the function to find the most relevant FAQs
-def find_most_relevant_faq_optimized(query, faq_docs):
-    """Find the top 3 most relevant FAQs based on semantic similarity."""
+def find_most_relevant_faq(query, faq_docs):
+    """Find the most relevant FAQs based on cosine similarity."""
     query_vector = nlp(query).vector
-
-    # Calculate cosine similarity between query and each FAQ
     similarities = [
         (question, answer, np.dot(query_vector, faq_vector) / (norm(query_vector) * norm(faq_vector)))
         for question, answer, faq_vector in faq_docs
     ]
-
-    # Sort by similarity score (highest first)
     similarities = sorted(similarities, key=lambda x: x[2], reverse=True)
+    return similarities[:3]
 
-    return similarities[:3]  # Return top 3 FAQs
+# Enhanced Streamlit UI
+st.set_page_config(
+    page_title="Smart FAQ Search - SARAS AI Institute",
+    page_icon="📚",
+    layout="wide"
+)
 
-# Step 6: Create the Streamlit UI
-st.title("Smart FAQ Search - SARAS AI Institute")
-st.markdown("### Find Answers to Your Questions Instantly")
+# Sidebar for Navigation
+with st.sidebar:
+    st.image("https://via.placeholder.com/150", caption="Saras AI Institute")
+    st.title("FAQ Search")
+    st.markdown("### Navigate:")
+    st.markdown("1. **Ask a Question**")
+    st.markdown("2. **Explore FAQs by Category**")
+    st.markdown("---")
+    st.write("📧 Contact us: support@sarasai.edu")
 
-# Text input for the user query
-query = st.text_input("Enter your question here:")
+# Main Header Section
+st.title("📖 Smart FAQ Search")
+st.markdown(
+    "<h4 style='color: #4CAF50;'>Find answers to your questions instantly!</h4>", 
+    unsafe_allow_html=True
+)
 
+# Input section with a placeholder
+query = st.text_input("🔍 Ask a question:", placeholder="E.g., What is the admission process?")
+
+# Display FAQs based on user query
 if query:
-    # Find the most relevant FAQs
-    top_faqs = find_most_relevant_faq_optimized(query, faq_docs)
+    st.markdown("---")
+    st.markdown("### 🔎 Top Relevant FAQs:")
+    top_faqs = find_most_relevant_faq(query, faq_docs)
 
-    # Display the results
-    st.markdown("### Top Relevant FAQs:")
     for i, (question, answer, score) in enumerate(top_faqs, 1):
-        st.write(f"**{i}. {question}**")
-        st.write(f"*Answer:* {answer}")
-        st.write(f"**Similarity Score:** {score:.2f}")
+        with st.expander(f"**{i}. {question}**"):
+            st.write(answer)
+            st.caption(f"Similarity Score: {score:.2f}")
 else:
-    st.write("Please enter a query to search for relevant FAQs.")
+    st.info("Enter a question above to find the most relevant FAQs.")
+
+# Add an Explore Section with FAQ Categories
+st.markdown("---")
+st.markdown("### 📂 Explore FAQs by Category")
+
+for category, faq_list in faqs.items():
+    with st.expander(f"**{category}**"):
+        for faq in faq_list:
+            st.write(f"**Q:** {faq['question']}")
+            st.write(f"**A:** {faq['answer']}")
+
+# Footer Section
+st.markdown("---")
+st.markdown(
+    "<div style='text-align: center;'>"
+    "💬 Need more help? Contact us at <a href='mailto:support@sarasai.edu'>support@sarasai.edu</a>."
+    "</div>",
+    unsafe_allow_html=True
+)