app.py

import os
import gradio as gr
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.  Check environment variables
# ────────────────────────────────
llama_cloud_key = os.getenv("LLAMA_CLOUD_API_KEY")
groq_key        = os.getenv("GROQ_API_KEY")
mxbai_key       = os.getenv("MXBAI_API_KEY")

if not (llama_cloud_key and groq_key and mxbai_key):
    raise EnvironmentError(
        "LLAMA_CLOUD_API_KEY, GROQ_API_KEY and MXBAI_API_KEY must be set."
    )

# ────────────────────────────────
# 2.  Model / parser setup
# ────────────────────────────────
LLM_MODEL   = "llama-3.1-70b-versatile"
EMBED_MODEL = "mixedbread-ai/mxbai-embed-large-v1"

parser       = LlamaParse(api_key=llama_cloud_key, result_type="markdown")
file_extractor = {ext: parser for ext in (
    ".pdf", ".docx", ".doc", ".txt", ".csv", ".xlsx",
    ".pptx", ".html", ".jpg", ".jpeg", ".png", ".webp", ".svg",
)}
embed_model  = MixedbreadAIEmbedding(api_key=mxbai_key, model_name=EMBED_MODEL)
llm          = Groq(model=LLM_MODEL, api_key=groq_key)

# Global cache for the current document
vector_index = None


# ────────────────────────────────
# 3.  Helper functions
# ────────────────────────────────
def load_files(file_path: str) -> str:
    """Parse the uploaded document and build a vector index."""
    global vector_index
    if not file_path:
        return "⚠️  No file selected."

    if not any(file_path.endswith(ext) for ext in file_extractor):
        return ("⚠️  Unsupported file type. "
                f"Allowed: {', '.join(file_extractor.keys())}")

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

    vector_index = VectorStoreIndex.from_documents(docs, embed_model=embed_model)
    return f"✅ Parsed **{os.path.basename(file_path)}**. Ask away!"


def respond(message: str, history: list) -> str:
    """Chat handler. Streams partial tokens back to the UI."""
    if vector_index is None:
        return "➡️  Please upload a document first."

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

    partial = ""
    for chunk in streaming_resp.response_gen:
        partial += chunk
        yield partial     # <─ streaming to the frontend


def clear_state():
    """Reset everything."""
    global vector_index
    vector_index = None
    return [None, ""]


# ────────────────────────────────
# 4.  Gradio UI
# ────────────────────────────────
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(file_count="single",
                                 type="filepath",
                                 label="Upload document")
            with gr.Row():
                submit_btn = gr.Button("Submit", variant="primary")
                clear_btn  = gr.Button("Clear")
            status_box = gr.Markdown()

        with gr.Column(scale=3):
            chat = gr.ChatInterface(
                fn=respond,
                chatbot=gr.Chatbot(height=300),
                show_progress="full",   # keep the nice progress bar
                textbox=gr.Textbox(
                    placeholder="Ask a question about the uploaded document…",
                    container=False,
                ),
            )

    submit_btn.click(load_files, inputs=file_input, outputs=status_box)
    clear_btn.click(clear_state, outputs=[file_input, status_box])

# Disable OpenAPI generation (avoids the bool/‘const’ bug) …
demo.queue(api_open=False)

# ────────────────────────────────
# 5.  Launch
# ────────────────────────────────
if __name__ == "__main__":
    # …and make a public share link so the container doesn’t choke on localhost
    demo.launch(share=True, server_name="0.0.0.0", server_port=7860)