Create app.py

app.py

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+# ===============================
+# 📦 Install dependencies (Only for Colab)
+# ===============================
+# !pip install streamlit faiss-cpu PyPDF2 sentence-transformers
+
+# ===============================
+# 📄 Imports
+# ===============================
+import os
+import numpy as np
+import PyPDF2
+import streamlit as st
+from sentence_transformers import SentenceTransformer
+import faiss
+
+# ✅ Set Streamlit page configuration at the top
+st.set_page_config(
+    page_title="📅 Exam Schedule Chatbot",
+    page_icon="🤖",
+    layout="wide"
+)
+
+# ===============================
+# 🎨 Styling
+# ===============================
+st.markdown("""
+    <style>
+        .main-title {
+            font-size: 40px;
+            font-weight: 800;
+            color: #1f77b4;
+            text-align: center;
+        }
+        .sub-title {
+            font-size: 20px;
+            color: #555;
+            text-align: center;
+        }
+        .stTextInput > div > input {
+            font-size: 16px;
+            height: 3em;
+        }
+        .stFileUploader {
+            margin-bottom: 20px;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# ===============================
+# 🧠 Load PDF & Extract Text
+# ===============================
+def extract_text_from_pdf(pdf_file):
+    reader = PyPDF2.PdfReader(pdf_file)
+    text = ''
+    for page in reader.pages:
+        content = page.extract_text()
+        if content:
+            text += content
+    return text
+
+# ===============================
+# 🧩 Embed Text and Create FAISS Index
+# ===============================
+def embed_and_index(text, model):
+    chunks = text.split("\n")
+    docs = [chunk.strip() for chunk in chunks if chunk.strip()]
+    vectors = model.encode(docs, convert_to_tensor=False)
+    index = faiss.IndexFlatL2(len(vectors[0]))
+    index.add(np.array(vectors))
+    return docs, index
+
+# ===============================
+# 🤖 Query with Context
+# ===============================
+def query_with_context(question, docs, index, model):
+    question_vec = model.encode([question], convert_to_tensor=False)
+    D, I = index.search(np.array(question_vec), k=3)
+    context = "\n".join([docs[i] for i in I[0]])
+    return f"📌 **Relevant Information:**\n\n{context}"
+
+# ===============================
+# 💬 UI
+# ===============================
+st.markdown('<div class="main-title">📅 University Exam Schedule Chatbot</div>', unsafe_allow_html=True)
+st.markdown('<div class="sub-title">Ask questions like "When is the AI exam?" or "Date of Software Engineering paper?"</div>', unsafe_allow_html=True)
+
+uploaded_file = st.file_uploader("📤 Upload your Date Sheet PDF", type="pdf")
+query = st.text_input("❓ Ask something about your exam schedule:")
+
+if uploaded_file and query:
+    if 'docs' not in st.session_state:
+        text = extract_text_from_pdf(uploaded_file)
+        model = SentenceTransformer("all-MiniLM-L6-v2")
+        st.session_state.docs, st.session_state.index = embed_and_index(text, model)
+        st.session_state.embedding_model = model
+
+    answer = query_with_context(
+        query,
+        st.session_state.docs,
+        st.session_state.index,
+        st.session_state.embedding_model
+    )
+
+    st.markdown("### 💬 Answer:")
+    st.success(answer)