app.py

import os
import zipfile
import torch
import faiss
import numpy as np
import gradio as gr

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from sentence_transformers import SentenceTransformer
#from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
#from langchain.embeddings import HuggingFaceEmbeddings
#from langchain.vectorstores import FAISS as LangChainFAISS
#from langchain.docstore import InMemoryDocstore
from langchain.schema import Document
#from langchain.llms import HuggingFacePipeline
from huggingface_hub import login
from huggingface_hub import upload_file

from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS as LangChainFAISS
from langchain_community.docstore.in_memory import InMemoryDocstore
from langchain_community.llms import HuggingFacePipeline


# Extract the Knowledge Base ZIP
if os.path.exists("md_knowledge_base.zip"):
    with zipfile.ZipFile("md_knowledge_base.zip", "r") as zip_ref:
        zip_ref.extractall("md_knowledge_base")
    print("✅ Knowledge base extracted.")

# Load Markdown Files
KB_PATH = "md_knowledge_base"
files = [os.path.join(dp, f) for dp, _, fn in os.walk(KB_PATH) for f in fn if f.endswith(".md")]
docs = [doc for f in files for doc in TextLoader(f, encoding="utf-8").load()]
print(f"✅ Loaded {len(docs)} documents.")

# Chunking
def get_dynamic_chunk_size(text):
    if len(text) < 1000:
        return 300
    elif len(text) < 5000:
        return 500
    else:
        return 1000

chunks = []
for doc in docs:
    chunk_size = get_dynamic_chunk_size(doc.page_content)
    chunk_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=100)
    chunks.extend(chunk_splitter.split_documents([doc]))
texts = [chunk.page_content for chunk in chunks]

# Vectorstore (FAISS)
embed_model_id = "sentence-transformers/all-MiniLM-L6-v2"
embedder = SentenceTransformer(embed_model_id)
embeddings = embedder.encode(texts, show_progress_bar=False)

dim = embeddings.shape[1]
index = faiss.IndexFlatL2(dim)
index.add(np.array(embeddings, dtype="float32"))

docs = [Document(page_content=t) for t in texts]
docstore = InMemoryDocstore({str(i): docs[i] for i in range(len(docs))})
id_map = {i: str(i) for i in range(len(docs))}
embed_fn = HuggingFaceEmbeddings(model_name=embed_model_id)

vectorstore = LangChainFAISS(
    index=index,
    docstore=docstore,
    index_to_docstore_id=id_map,
    embedding_function=embed_fn
)

print("✅ FAISS vectorstore ready.")

# Load Falcon-e-1B-Instruct
model_id = "tiiuae/falcon-e-1b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16
).to("cuda" if torch.cuda.is_available() else "cpu")

text_gen_pipeline = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device=0 if torch.cuda.is_available() else -1,
    return_full_text=False,
    do_sample=False,
    max_new_tokens=200,
    pad_token_id=tokenizer.eos_token_id
)

llm = HuggingFacePipeline(pipeline=text_gen_pipeline)

def truncate_context(context, max_length=1024):
    tokens = tokenizer.encode(context)
    if len(tokens) > max_length:
        tokens = tokens[:max_length]
    return tokenizer.decode(tokens, skip_special_tokens=True)

def format_prompt(context, question):
    return (
        "You are the Hull University Assistant—a friendly, knowledgeable chatbot dedicated to "
        "helping students with questions about courses, admissions, tuition fees, and student life. "
        "Use ONLY the information provided in the context below to answer the question. "
        "If the answer cannot be found in the context, reply: \"I’m sorry, but I don’t have that "
        "information available right now.\"\n\n"
        f"Context:\n{truncate_context(context)}\n\n"
        f"Student Question: {question}\n"
        "Assistant Answer:"
    )

def answer_fn(question):
    docs = vectorstore.similarity_search(question, k=5)
    if not docs:
        return "I'm sorry, I couldn't find any relevant information for your query."
    context = "\n\n".join(d.page_content for d in docs)
    prompt = format_prompt(context, question)
    try:
        response = llm.invoke(prompt).strip()
        return response
    except Exception as e:
        return f"An error occurred: {e}"

# Gradio Interface
def chat_fn(user_message, history):
    bot_response = answer_fn(user_message)
    history = history + [{"role": "user", "content": user_message}, {"role": "assistant", "content": bot_response}]
    return history, history


#def chat_fn(user_message, history):
 #   bot_response = answer_fn(user_message)
  #  history = history + [(user_message, bot_response)]
   # return history, history


with gr.Blocks() as demo:
    gr.Markdown("## 📘 University of Hull Assistant")
    #chatbot = gr.Chatbot()
    chatbot = gr.Chatbot(label="University of Hull Assistant", type="messages")
    state = gr.State([])

    user_input = gr.Textbox(placeholder="Ask a question about University of Hull...", show_label=False)
    user_input.submit(fn=chat_fn, inputs=[user_input, state], outputs=[chatbot, state])

# demo.launch()
if __name__ == "__main__":
    demo.launch(show_api=False)