Create app.py

app.py

@@ -0,0 +1,49 @@
+import os
+import zipfile
+import streamlit as st
+from langchain_community.document_loaders import DirectoryLoader, TextLoader
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+# Step 1: Extract ZIP
+def extract_zip(zip_path, extract_to):
+    if os.path.exists(zip_path) and not os.path.exists(extract_to):
+        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+            zip_ref.extractall(extract_to)
+        st.success("✅ Knowledge Base extracted successfully!")
+
+# Step 2: Load and embed knowledge base
+@st.cache_resource
+def load_knowledge_base(folder_path):
+    loader = DirectoryLoader(folder_path, glob="*.md", loader_cls=TextLoader)
+    docs = loader.load()
+    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    split_docs = splitter.split_documents(docs)
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-MiniLM-L6-v2")
+    db = FAISS.from_documents(split_docs, embeddings)
+    return db
+
+# Streamlit UI
+st.title("📘 Fitlytic Chatbot")
+
+# Step 3: Extract ZIP if needed
+zip_path = "Knowledge_Base.zip"
+extract_to = "Knowledge_Base"
+extract_zip(zip_path, extract_to)
+
+# Step 4: Load knowledge base
+if os.path.exists(extract_to):
+    db = load_knowledge_base(extract_to)
+else:
+    st.error("❌ ZIP file not found or extraction failed.")
+    st.stop()
+
+# Step 5: User interaction
+query = st.text_input("Ask me anything about Fitlytic:")
+if query:
+    results = db.similarity_search(query, k=1)
+    if results:
+        st.success(results[0].page_content)
+    else:
+        st.error("😕 Sorry, I couldn't find an answer. Try rephrasing it.")