Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import openpyxl
+import faiss
+import spacy
+from sentence_transformers import SentenceTransformer
+
+# ✅ Load NLP Model
+nlp = spacy.load("en_core_web_sm")
+
+# ✅ Streamlit UI
+st.set_page_config(page_title="AI-Powered Timetable", layout="wide")
+st.markdown("<h1 style='text-align: center; color: #4CAF50;'>📅 AI-Powered Timetable</h1>", unsafe_allow_html=True)
+
+# ✅ File Upload Section
+st.sidebar.markdown("## 📂 Upload Your Timetable Files")
+uploaded_timetable = st.sidebar.file_uploader("Upload Timetable (Excel)", type=["xlsx"])
+
+# ✅ Load Timetable Data
+def load_timetable(file):
+    if not file:
+        return None
+    df = pd.read_excel(file)
+    return df
+
+timetable_df = load_timetable(uploaded_timetable)
+
+# ✅ Extract Timetable as Text Data
+if timetable_df is not None:
+    timetable_texts = timetable_df.astype(str).apply(lambda x: " | ".join(x), axis=1).tolist()
+else:
+    timetable_texts = []
+
+# ✅ Initialize Embeddings
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+if timetable_texts:
+    embeddings = embedder.encode(timetable_texts, convert_to_tensor=True).cpu().numpy()
+    dimension = embeddings.shape[1]  # FIXED: Ensure valid shape
+    index = faiss.IndexFlatL2(dimension)
+    index.add(embeddings)
+
+# ✅ Retrieve Relevant Texts
+def retrieve_timetable_data(query, top_k=2):
+    query_embedding = embedder.encode([query], convert_to_tensor=True).cpu().numpy()
+    distances, indices = index.search(query_embedding, top_k)
+
+    retrieved_texts = [timetable_texts[idx] for idx in indices[0] if idx < len(timetable_texts)]
+    return "\n".join(retrieved_texts)
+
+# ✅ NLP-Based Response System
+def generate_response(query):
+    doc = nlp(query.lower())
+
+    if "who" in query:
+        return "Searching for faculty information..."
+    elif "when" in query:
+        return "Retrieving schedule details..."
+    elif "where" in query:
+        return "Locating classrooms..."
+    
+    return "Let me check the timetable."
+
+# ✅ AI Query Section
+st.markdown("## 🤖 Ask AI About Your Timetable")
+user_query = st.text_input("Type your question here")
+
+if st.button("Ask AI"):
+    retrieved_info = retrieve_timetable_data(user_query)
+    response = generate_response(user_query)
+    st.write(f"📝 **Retrieved Timetable Data:**\n{retrieved_info}")
+    st.write(f"🤖 **AI Suggests:** {response}")
+
+# ✅ Display Uploaded Timetable
+if timetable_df is not None:
+    st.markdown("## 📜 Uploaded Timetable")
+    st.dataframe(timetable_df)