Upload app.py

app.py

@@ -1,10 +1,7 @@
 import os
-import re
 import zipfile
 import gradio as gr
-from langchain_community.vectorstores import FAISS
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.docstore.document import Document
+
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.vectorstores import FAISS
@@ -13,41 +10,16 @@ from langchain.chains import ConversationalRetrievalChain
 from langchain.memory import ConversationBufferMemory
 from langchain.prompts import PromptTemplate
 
-# --- Step 1: Clean .txt files ---
-DATA_DIR = "knowledge_base"
-docs = []
-
-for fname in os.listdir(DATA_DIR):
-    file_path = os.path.join(DATA_DIR, fname)
-    if os.path.isfile(file_path) and fname.endswith(".txt"):
-        with open(file_path, "r", encoding="utf-8") as f:
-            text = f.read()
-            cleaned_text = text.replace('\xa0', ' ')
-            cleaned_text = re.sub(r'\n+', '\n', cleaned_text)
-            cleaned_text = re.sub(r' +', ' ', cleaned_text).strip()
-            docs.append({"page": fname, "text": cleaned_text})
-
-# --- Step 2: Split text into chunks ---
-splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
-texts = []
-metadatas = []
-
-for doc in docs:
-    chunks = splitter.split_text(doc["text"])
-    for i, chunk in enumerate(chunks):
-        texts.append(chunk)
-        metadatas.append({"source": doc["page"], "chunk": i})
-
-# --- Step 3: Create Document objects ---
-documents = [Document(page_content=texts[i], metadata=metadatas[i]) for i in range(len(texts))]
-
-# --- Step 4: Load embedding model ---
-embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-
-# --- Step 5: Build FAISS index ---
-vectordb = FAISS.from_documents(documents, embedding_model)
-
-# --- Step 6: Load the LLM ---
+# --- Step 1: Unzip FAISS index ---
+if not os.path.exists("faiss_index") and os.path.exists("faiss_index.zip"):
+    with zipfile.ZipFile("faiss_index.zip", "r") as zip_ref:
+        zip_ref.extractall(".")
+
+# --- Step 2: Load embedding and vectorstore ---
+embedding_model = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
+vectordb = FAISS.load_local("faiss_index", embedding_model,allow_dangerous_deserialization=True)
+
+# --- Step 3: Load the LLM ---
 model_id = "tiiuae/falcon3-1b-instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id)
@@ -63,10 +35,10 @@ pipe = pipeline(
 )
 llm = HuggingFacePipeline(pipeline=pipe)
 
-# --- Step 7: Setup memory and QA chain ---
+# --- Step 4: Setup memory and QA chain ---
 memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
 
-custom_prompt = PromptTemplate.from_template("""
+prompt = PromptTemplate.from_template("""
 You are a helpful assistant at the University of Hertfordshire. Use the context below to answer the question clearly and factually.
 If the answer is not in the context, say you don't know.
 
@@ -76,28 +48,26 @@ Context:
 Question:
 {question}
 
-Answer:
+Helpful Answer:
 """)
 
 qa_chain = ConversationalRetrievalChain.from_llm(
     llm=llm,
     retriever=vectordb.as_retriever(search_kwargs={"k": 3}),
     memory=memory,
-    chain_type="stuff",
-    combine_docs_chain_kwargs={"prompt": custom_prompt}
+    chain_type="map_reduce",
+    combine_docs_chain_kwargs={"prompt": prompt}
 )
 
-# --- Step 8: Define chatbot logic ---
+UH_LOGO = "images/UH.png"
+# --- Step 5: Define chatbot logic ---
 def chat(message, history):
     result = qa_chain.invoke({"question": message})
     response = result.get("answer", "")
     response = response.split("Answer:")[-1].replace("<|assistant|>", "").strip()
     return response
 
-# UH logo
-UH_LOGO = "images/UH.png"
-
-# --- Step 9: UI ---
+# --- Step 6: UI ---
 sample_questions = [
     "How do I register as a new student?",
     "Where can I find accommodation?",