app.py

import gradio as gr
from bedrock_client import bedrock_llm
from langchain.schema import SystemMessage, HumanMessage, AIMessage
import os


AUTHS = [(os.environ.get('USER'), os.environ.get('PW'))]
SYSTEM_PROMPT = os.environ.get('SYSTEM_PROMPT', '')


def chat(message, history):
    # 3a) Build Bedrock input (with system prompt + raw dict‐history)

    # 1) start with the system prompt
    history_langchain_format: list = [SystemMessage(content=SYSTEM_PROMPT)]

    # 2) replay the user/assistant turns
    for msg in history:
        if msg["role"] == "user":
            history_langchain_format.append(HumanMessage(content=msg["content"]))
        elif msg["role"] == "assistant":
            history_langchain_format.append(AIMessage(content=msg["content"]))

    # 3) append the new user message
    history_langchain_format.append(HumanMessage(content=message))

    stream =bedrock_llm.stream(history_langchain_format)

    full = next(stream)
    # 2) Try to get the first chunk safely
    try:
        first_chunk = next(stream)
    except StopIteration:
        # No content at all! Yield an error or empty string
        yield "⚠️ Sorry, no response from the model."
        return


    for chunk in stream:
         full +=chunk
         yield full.content


with gr.Blocks(css_paths=["static/deval.css"],theme = gr.themes.Default(primary_hue="blue", secondary_hue="yellow"),) as demo:
    # ── Logo + Header + Logout ────────────────────────────────


    with gr.Row():
        with gr.Column(scale=1):
                gr.Image(
                    value="static/logo.png",
                    height=50,
                    show_label=False,
                    interactive=False,
                    show_download_button=False,
                    show_fullscreen_button=False,
                    elem_id="logo-primary",    # matches the CSS above
                )
        with gr.Column(scale=10):
            gr.Markdown(
                "# DEvalBot\n\n"
                "**Hinweis:** Bitte gebe keine vertraulichen Informationen ein. "
                "Dazu zählen u.a. sensible personenbezogene Daten, institutsinterne "
                "Informationen oder Dokumente, unveröffentlichte Berichtsinhalte, "
                "vertrauliche Informationen oder Dokumente externer Organisationen "
                "sowie sensible erhobene Daten (wie etwa Interviewtranskripte).", elem_id="header-text"
            )

    # inject auto-reload script
    gr.HTML(
        """
        <script>
          // Reload the page after 1 minutes (300 000 ms)
          setTimeout(() => {
            window.location.reload();
          }, 1000);
        </script>
        """
    )
    gr.ChatInterface(
        chat,
        type="messages",
        multimodal=os.environ.get('MULTIMODAL', False),
        editable=True,
        concurrency_limit=20,
        save_history=True,
    )
    


demo.queue().launch(auth=AUTHS,share=True, ssr_mode=False)