app.py

import torch, os
import gradio as gr
from pathlib import Path
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.core.memory import ChatMemoryBuffer
from llama_index.llms.huggingface import HuggingFaceLLM
from llama_index.embeddings.langchain import LangchainEmbedding
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index.core import PromptTemplate


pdf_files = [str(p) for p in Path(".").glob("*.pdf")]   
docs = SimpleDirectoryReader(input_files=pdf_files).load_data()

SYSTEM_PROMPT = """
You are a friendly café assistant for Café Eleven. Your job is to:
1. Greet the customer warmly
2. Help them place their order
3. Ask for pickup time
4. Suggest add-ons/extras from our menu
5. Confirm the complete order

Menu items are embedded in the document. Always:
- Be polite and professional
- Confirm order details clearly
- Suggest popular combinations
- Never make up items not in our menu
"""

wrapper_prompt = PromptTemplate(
    """<s>[INST] <<SYS>>
{system_prompt}
Current conversation:
{chat_history}
<</SYS>>

{query_str} [/INST]"""
)

llm = HuggingFaceLLM(
    tokenizer_name="meta-llama/Llama-2-7b-chat-hf",
    model_name="meta-llama/Llama-2-7b-chat-hf",
    context_window=3900,
    max_new_tokens=256,
    generate_kwargs={"temperature": 0.2, "do_sample": True},
    device_map="auto",
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
    system_prompt=SYSTEM_PROMPT,
    query_wrapper_prompt=wrapper_prompt,
)

embed_model = LangchainEmbedding(
    HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
)

Settings.llm = llm
Settings.embed_model = embed_model
Settings.chunk_size = 512

# ---------- 3. Build the chat engine ----------
memory = ChatMemoryBuffer.from_defaults(token_limit=2000)
index = VectorStoreIndex.from_documents(docs)
chat_engine = index.as_chat_engine(
    chat_mode="condense_plus_context",
    memory=memory,
    system_prompt=SYSTEM_PROMPT,
)

# ---------- 4. Gradio UI ----------
with gr.Blocks(title="Café Eleven Chat") as demo:
    gr.Markdown("## ☕ Café Eleven Ordering Assistant  \nType *quit* to end the chat.")
    chatbot = gr.Chatbot()
    user_txt = gr.Textbox(show_label=False, placeholder="Hi, I’d like a latte…")
    clear = gr.Button("Clear")

    def respond(message, chat_history):
        if message.lower().strip() in {"quit", "exit", "done"}:
            return "Thank you for your order! We'll see you soon.", chat_history
        response = chat_engine.chat(message).response
        chat_history.append((message, response))
        return "", chat_history

    user_txt.submit(respond, [user_txt, chatbot], [user_txt, chatbot])
    clear.click(lambda: None, None, chatbot, queue=False)

if __name__ == "__main__":
    demo.queue(concurrency_count=3).launch()