Create app.py

app.py

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+import faiss
+import pickle
+import os
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from langchain.llms import HuggingFacePipeline
+from langchain.vectorstores import FAISS as LangChainFAISS
+from langchain.docstore import InMemoryDocstore
+from langchain.schema import Document
+from langchain.chains import RetrievalQA
+import gradio as gr
+
+# Paths (relative to app root)
+vector_path = "vector_store_faiss_chroma/faiss_index.index"
+metadata_path = "vector_store_faiss_chroma/metadata.pkl"
+model_path = "HuggingFaceModels/falcon-1b-instruct"
+
+# Load the FAISS index
+faiss_index = faiss.read_index(f"{vector_path}")
+
+# Load metadata (text chunks)
+    with open(f"{metadata_path}", "rb") as f:
+    metadata = pickle.load(f)
+
+# Rebuild LangChain Documents
+docs = [Document(page_content=doc["page_content"]) for doc in metadata]
+
+# Link documents to FAISS vectors
+docstore = InMemoryDocstore({str(i): docs[i] for i in range(len(docs))})
+id_map = {i: str(i) for i in range(len(docs))}
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path)
+
+# Create a generation pipeline
+text_generator_pipeline = pipeline(
+    model=model,
+    tokenizer=tokenizer,
+    task="text-generation",
+    return_full_text=False,
+    max_new_tokens=512,
+    temperature=0.2
+)
+# Wrap it as a LangChain LLM
+llm = HuggingFacePipeline(pipeline=text_generator_pipeline)
+
+# Create vectorstore and retriever
+vectorstore_faiss = LangChainFAISS(
+    index=faiss_index,
+    docstore=docstore,
+    index_to_docstore_id=id_map,
+    embedding_function=None # Not needed for retrieval only
+)
+
+# Create a retriever that returns top-k most relevant chunks
+retriever = vectorstore_faiss.as_retriever(search_kwargs={"k": 3})
+
+# Create the RAG pipeline (Retriever + LLM)
+qa_chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=retriever,
+    return_source_documents=True
+)
+
+# 🔁 Chatbot function: takes a user question, returns generated answer
+def ask_rag(query):
+    result = qa_chain({"query": query})
+    answer = result["result"]
+
+    # Optional: include sources (limited to 2)
+    sources = result.get("source_documents", [])
+    source_texts = "\n\n".join([f"🔹 Source {i+1}:\n{doc.page_content[:300]}..." for i, doc in enumerate(sources[:2])])
+
+    return f"📘 Answer:\n{answer}\n\n📚 Sources:\n{source_texts}"
+
+# 🎛️ Gradio UI components
+gr.Interface(
+    fn=ask_rag,
+    inputs=gr.Textbox(lines=2, placeholder="Ask me about UCT admissions, housing, fees..."),
+    outputs="text",
+    title="🎓 University of Cape Town Course Advisor Chatbot",
+    description="Ask academic questions. Powered by FAISS + Falcon-E-1B + LangChain.",
+    allow_flagging="never"
+).launch()