app.py

"""
Doc-Q&A app (Gradio 5.x + Llama-Index 0.12.x, June 2025)

Key upgrades
------------
▪ Gradio 5.34—new event system (`upload`, `clear` etc.)  
▪ Llama-Index 0.12.42—`VectorStoreIndex.from_documents` signature unchanged  
▪ MixedbreadAIEmbedding 0.3.0  ➜ supports `batch_size`, `timeout`  
▪ Tenacity for exponential-back-off when MXBAI returns 5xx / rate limits
"""

from __future__ import annotations

import os
from pathlib import Path
from typing import List

import gradio as gr
from tenacity import retry, wait_exponential, stop_after_attempt
from mixedbread_ai.core.api_error import ApiError

from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.embeddings.mixedbreadai import MixedbreadAIEmbedding
from llama_index.llms.groq import Groq
from llama_parse import LlamaParse

# ──────────────────────────────────────────────────────────────────
# 1.  Environment variables (fail-fast if missing)
# ──────────────────────────────────────────────────────────────────
LLAMA_CLOUD_API_KEY = os.getenv("LLAMA_CLOUD_API_KEY")
GROQ_API_KEY        = os.getenv("GROQ_API_KEY")
MXBAI_API_KEY       = os.getenv("MXBAI_API_KEY")

if not all([LLAMA_CLOUD_API_KEY, GROQ_API_KEY, MXBAI_API_KEY]):
    raise EnvironmentError(
        "LLAMA_CLOUD_API_KEY, GROQ_API_KEY and MXBAI_API_KEY must be set in the env."
    )

# ──────────────────────────────────────────────────────────────────
# 2.  Models & parsers  (latest defaults - June 2025)
# ──────────────────────────────────────────────────────────────────
LLM_MODEL   = "llama-3.1-70b-versatile"                 # Groq’s best for Q&A
EMBED_MODEL = "mixedbread-ai/mxbai-embed-large-v1"      # 1024-dim

parser = LlamaParse(api_key=LLAMA_CLOUD_API_KEY, result_type="markdown")

SUPPORTED_EXTS = (
    ".pdf", ".docx", ".doc", ".txt", ".csv", ".xlsx",
    ".pptx", ".html", ".jpg", ".jpeg", ".png", ".webp", ".svg",
)
file_extractor = {ext: parser for ext in SUPPORTED_EXTS}

embed_model = MixedbreadAIEmbedding(
    api_key     = MXBAI_API_KEY,
    model_name  = EMBED_MODEL,
    batch_size  = 8,          # keep requests < 100 KB
    timeout     = 60,         # generous server-side processing window
)

llm = Groq(model=LLM_MODEL, api_key=GROQ_API_KEY)

# A simple global cache (could be swapped for Redis, etc.)
vector_index: VectorStoreIndex | None = None


# ──────────────────────────────────────────────────────────────────
# 3.  Helper wrappers
# ──────────────────────────────────────────────────────────────────
@retry(
    wait=wait_exponential(multiplier=2, min=4, max=32),
    stop=stop_after_attempt(4),
    retry_error_callback=lambda retry_state: None,   # bubble up as None
    reraise=False,
)
def _safe_build_index(docs) -> VectorStoreIndex | None:
    """Retry MXBAI 503 / 429 transparently."""
    try:
        return VectorStoreIndex.from_documents(docs, embed_model=embed_model)
    except ApiError as e:
        # Tenacity will catch and retry unless non-5xx / non-429
        if e.status_code not in (429, 500, 502, 503, 504):
            raise
        raise   # trigger retry


def load_files(file: Path | None) -> str:
    """Parse uploaded file and build vector index (with retries)."""
    global vector_index
    if file is None:
        return "⚠️  No file selected."

    if file.suffix.lower() not in SUPPORTED_EXTS:
        allow = ", ".join(SUPPORTED_EXTS)
        return f"⚠️  Unsupported file type. Allowed: {allow}"

    docs = SimpleDirectoryReader(
        input_files=[str(file)],
        file_extractor=file_extractor,
    ).load_data()

    idx = _safe_build_index(docs)
    if idx is None:
        return "🚧 Embedding service busy. Please retry in ~1 minute."

    vector_index = idx
    return f"✅ Parsed **{file.name}** — you can start chatting!"


def respond(message: str, history: List[List[str]]):
    """Stream answer chunks to the Chatbot."""
    if vector_index is None:
        yield "➡️  Please upload a document first."
        return

    query_engine = vector_index.as_query_engine(streaming=True, llm=llm)
    response = query_engine.query(message)

    partial = ""
    for token in response.response_gen:
        partial += token
        yield partial


def clear():
    """Reset everything (file widget + status + index)."""
    global vector_index
    vector_index = None
    return None, "", None   # file_input, status_md, chatbot history


# ──────────────────────────────────────────────────────────────────
# 4.  Gradio UI  (5.x syntax)
# ──────────────────────────────────────────────────────────────────
with gr.Blocks(
    theme=gr.themes.Default(
        primary_hue="green",
        secondary_hue="blue",
        font=[gr.themes.GoogleFont("Poppins")],
    ),
    css="footer {visibility:hidden}",
) as demo:

    gr.Markdown("<h1 style='text-align:center'>DataCamp Doc Q&A 🤖📑</h1>")

    with gr.Row():
        with gr.Column(scale=1):
            file_input = gr.File(
                label="Upload document",
                file_count="single",
                type="filepath",
                show_label=True,
            )
            status_md  = gr.Markdown()
            with gr.Row():
                clear_btn = gr.Button("Reset 🔄", variant="secondary")

        with gr.Column(scale=3):
            chatbot = gr.Chatbot(height=340)
            txt_box = gr.Textbox(
                placeholder="Ask something about the uploaded document…",
                container=False,
                scale=7,
            )
            send_btn = gr.Button("Send", variant="primary")

    # events (v5 style)
    file_input.upload(
        fn=load_files,
        inputs=file_input,
        outputs=status_md,
    )
    send_btn.click(
        fn=respond,
        inputs=[txt_box, chatbot],
        outputs=chatbot,
    )
    clear_btn.click(
        fn=clear,
        outputs=[file_input, status_md, chatbot],
    )

# optional: disable public OpenAPI schema (old crash guard)
demo.queue(api_open=False)

# ──────────────────────────────────────────────────────────────────
# 5.  Launch
# ──────────────────────────────────────────────────────────────────
if __name__ == "__main__":
    demo.launch(share=True, server_name="0.0.0.0", server_port=7860)