app.py

import dash
from dash import dcc, html, Input, Output, State, callback_context, no_update
import dash_bootstrap_components as dbc
import logging
import threading
import os
import base64
import io
import uuid
import time
from flask import Flask

import requests

ALLOWED_EXTENSIONS = ('pdf', 'doc', 'docx', 'txt')

logging.basicConfig(
    format="%(asctime)s %(levelname)s:%(message)s",
    level=logging.INFO
)
logger = logging.getLogger(__name__)

uploaded_documents = {}
generated_content = {}

ANTHROPIC_API_URL = "https://api.anthropic.com/v1/messages"
ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY", "YOUR_ANTHROPIC_API_KEY")

server = Flask(__name__)

os.environ["CUDA_VISIBLE_DEVICES"] = "0"

external_stylesheets = [dbc.themes.BOOTSTRAP]

app = dash.Dash(
    __name__,
    server=server,
    external_stylesheets=external_stylesheets,
    suppress_callback_exceptions=True,
    title="Proposal Writing Assistant"
)

def allowed_file(filename):
    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS

def save_uploaded_file(file_content, filename):
    doc_id = str(uuid.uuid4())
    uploaded_documents[doc_id] = {
        "filename": filename,
        "content": file_content
    }
    logger.info(f"Uploaded document saved: {filename} with id {doc_id}")
    return doc_id

def anthropic_api_call(prompt, files=None, task_type=None, extra_instructions=""):
    logger.info(f"Calling Anthropic API for task: {task_type}")
    headers = {
        "x-api-key": ANTHROPIC_API_KEY,
        "content-type": "application/json"
    }
    data = {
        "model": "claude-3-opus-20240229",
        "messages": [
            {"role": "user", "content": prompt + "\n" + extra_instructions}
        ],
        "max_tokens": 4096,
        "temperature": 0.2
    }
    try:
        # response = requests.post(ANTHROPIC_API_URL, headers=headers, json=data, timeout=120)
        # result = response.json().get('content', ['[Anthropic response placeholder]'])[0]
        time.sleep(2)
        result = f"[Simulated response for {task_type}]"
        logger.info(f"Anthropic API success for task: {task_type}")
        return result
    except Exception as e:
        logger.error(f"Anthropic API error: {str(e)}")
        return f"Error: {str(e)}"

def parse_contents(contents, filename):
    content_type, content_string = contents.split(',')
    decoded = base64.b64decode(content_string)
    try:
        if filename.lower().endswith('.txt'):
            preview = decoded.decode('utf-8')[:2048]
        else:
            preview = f"Preview not available for {filename}"
        return preview
    except Exception as e:
        logger.error(f"Could not decode file {filename}: {e}")
        return f"Error decoding {filename}"

def navbar():
    return dbc.Card(
        [
            dbc.Nav(
                [
                    dbc.Button("Shred RFP/PWS/SOW/RFI", id="btn-shred", className="mb-2 btn-primary", style={"width": "100%"}),
                    dbc.Button("Generate Proposal Response", id="btn-generate", className="mb-2 btn-secondary", style={"width": "100%"}),
                    dbc.Button("Check Compliance", id="btn-compliance", className="mb-2 btn-tertiary", style={"width": "100%"}),
                    dbc.Button("Recover Document", id="btn-recover", className="mb-2 btn-primary", style={"width": "100%"}),
                    dbc.Button("Virtual Board", id="btn-virtual-board", className="mb-2 btn-secondary", style={"width": "100%"}),
                    dbc.Button("Estimate LOE", id="btn-loe", className="mb-2 btn-tertiary", style={"width": "100%"}),
                ],
                vertical=True,
                pills=False
            ),
            html.Hr(),
            html.Div(
                [
                    html.H6("Uploaded Documents"),
                    html.Ul(
                        id="uploaded-doc-list",
                        style={"listStyleType": "none", "paddingLeft": "0"}
                    ),
                ]
            ),
        ],
        body=True
    )

def chat_window():
    return dbc.Card(
        [
            html.Div(
                [
                    html.Div(id="chat-history", style={"height": "160px", "overflowY": "auto", "padding": "0.5rem"}),
                    dbc.InputGroup(
                        [
                            dbc.Textarea(id="chat-input", placeholder="Send additional instructions...", style={"resize":"vertical", "wordWrap":"break-word", "width": "100%", "height": "60px"}),
                            dbc.Button("Send", id="btn-send-chat", className="btn-secondary", n_clicks=0),
                        ],
                        className="mt-2"
                    ),
                ]
            ),
        ],
        body=True,
        style={"marginBottom": "10px"}
    )

def top_action_buttons():
    return html.Div(
        [
            dbc.Button("Shred", id="action-shred", className="me-2 btn-primary", n_clicks=0, style={"minWidth": "120px"}),
            dbc.Button("Generate Response", id="action-generate", className="me-2 btn-secondary", n_clicks=0, style={"minWidth": "180px"}),
            dbc.Button("Check Compliance", id="action-compliance", className="me-2 btn-tertiary", n_clicks=0, style={"minWidth": "160px"}),
            dbc.Button("Recover", id="action-recover", className="me-2 btn-primary", n_clicks=0, style={"minWidth": "120px"}),
            dbc.Button("Virtual Board", id="action-virtual-board", className="me-2 btn-secondary", n_clicks=0, style={"minWidth": "160px"}),
            dbc.Button("Estimate LOE", id="action-loe", className="btn-tertiary", n_clicks=0, style={"minWidth": "140px"}),
        ],
        className="mb-3",
        style={"display": "flex", "flexWrap": "wrap"}
    )

def upload_area():
    return html.Div(
        [
            dcc.Upload(
                id="upload-document",
                children=html.Div(["Drag & drop or click to select a file."]),
                multiple=False,
                style={
                    "width": "100%",
                    "height": "70px",
                    "lineHeight": "70px",
                    "borderWidth": "1px",
                    "borderStyle": "dashed",
                    "borderRadius": "4px",
                    "textAlign": "center",
                    "marginBottom": "8px"
                }
            ),
            html.Div(id="upload-feedback")
        ]
    )

def preview_area():
    return dbc.Card(
        [
            html.H6("Document Preview / Output"),
            html.Pre(id="preview-content", style={"whiteSpace": "pre-wrap", "wordWrap": "break-word", "maxHeight": "340px", "overflowY": "auto"})
        ],
        body=True
    )

def main_layout():
    return dbc.Container(
        [
            dbc.Row(
                [
                    dbc.Col(
                        html.H2("Proposal Writing Assistant", style={"margin": "12px 0"}),
                        width=12
                    ),
                ],
                align="center",
                style={"marginBottom": "8px"}
            ),
            dbc.Row(
                [
                    dbc.Col(
                        navbar(),
                        width=3,
                        style={"minWidth": "220px", "maxWidth": "400px"}
                    ),
                    dbc.Col(
                        dbc.Card(
                            [
                                chat_window(),
                                top_action_buttons(),
                                upload_area(),
                                preview_area(),
                                dcc.Loading(
                                    id="loading",
                                    type="default",
                                    children=html.Div(id="loading-output"),
                                    style={"position": "absolute", "top": "6px", "left": "50%"}
                                ),
                            ],
                            body=True
                        ),
                        width=9
                    ),
                ],
                style={"minHeight": "90vh"}
            ),
        ],
        fluid=True
    )

app.layout = main_layout

@app.callback(
    Output("uploaded-doc-list", "children"),
    Output("preview-content", "children"),
    Output("upload-feedback", "children"),
    Output("loading-output", "children"),
    Output("chat-history", "children"),
    [
        Input("upload-document", "contents"),
        Input("action-shred", "n_clicks"),
        Input("action-generate", "n_clicks"),
        Input("action-compliance", "n_clicks"),
        Input("action-recover", "n_clicks"),
        Input("action-virtual-board", "n_clicks"),
        Input("action-loe", "n_clicks"),
        Input("btn-send-chat", "n_clicks"),
        Input({"type": "delete-doc-btn", "index": dash.ALL}, "n_clicks"),
    ],
    [
        State("upload-document", "filename"),
        State("chat-input", "value"),
        State("chat-history", "children"),
        State("preview-content", "children"),
        State("uploaded-doc-list", "children"),
    ],
    prevent_initial_call=True
)
def main_callback(
    upload_contents, shred, generate, compliance, recover, virtual_board, loe, send_chat, delete_doc_clicks,
    upload_filename, chat_input, chat_history, preview_content, uploaded_doc_list
):
    triggered_id = callback_context.triggered[0]["prop_id"].split(".")[0] if callback_context.triggered else None
    logger.info(f"Triggered callback: {triggered_id}")

    feedback = no_update
    loading_message = ""
    new_preview_content = no_update
    new_chat_history = chat_history if chat_history else []
    doc_list_items = []

    if triggered_id == "upload-document" and upload_contents and upload_filename:
        if not allowed_file(upload_filename):
            feedback = dbc.Alert("Unsupported file type. Please upload PDF, Word, or TXT.", color="danger", dismissable=True)
        else:
            doc_id = save_uploaded_file(upload_contents, upload_filename)
            preview = parse_contents(upload_contents, upload_filename)
            new_preview_content = f"{upload_filename}:\n\n{preview}"
            feedback = dbc.Alert(f"Uploaded {upload_filename}", color="success", dismissable=True)
            logger.info(f"File uploaded: {upload_filename}")

    for doc_id, doc in uploaded_documents.items():
        doc_list_items.append(
            html.Li(
                [
                    html.Span(doc['filename'], style={"marginRight": "8px"}),
                    dbc.Button("Delete", id={"type": "delete-doc-btn", "index": doc_id}, color="danger", size="sm", n_clicks=0)
                ],
                style={"display": "flex", "justifyContent": "space-between", "alignItems": "center", "marginBottom": "5px"}
            )
        )

    if isinstance(delete_doc_clicks, list) and any(delete_doc_clicks):
        idx = delete_doc_clicks.index(max(delete_doc_clicks))
        doc_ids = list(uploaded_documents.keys())
        if idx < len(doc_ids):
            deleted_doc = uploaded_documents.pop(doc_ids[idx])
            feedback = dbc.Alert(f"Deleted {deleted_doc['filename']}", color="info", dismissable=True)
            logger.info(f"Document deleted: {deleted_doc['filename']}")
            doc_list_items = [
                html.Li(
                    [
                        html.Span(doc['filename'], style={"marginRight": "8px"}),
                        dbc.Button("Delete", id={"type": "delete-doc-btn", "index": doc_id}, color="danger", size="sm", n_clicks=0)
                    ],
                    style={"display": "flex", "justifyContent": "space-between", "alignItems": "center", "marginBottom": "5px"}
                )
                for doc_id, doc in uploaded_documents.items()
            ]
            new_preview_content = "" if not uploaded_documents else no_update

    if len(uploaded_documents) == 0 and triggered_id not in ["upload-document", "btn-send-chat"]:
        feedback = dbc.Alert("Please upload a document before performing actions.", color="warning", dismissable=True)
        logger.warning("Attempted action without documents.")
        return doc_list_items, new_preview_content, feedback, loading_message, new_chat_history

    if triggered_id == "btn-send-chat" and chat_input and chat_input.strip():
        if not isinstance(new_chat_history, list):
            new_chat_history = []
        new_chat_history.append(html.Div([
            html.Strong("You: "), html.Span(chat_input)
        ], style={"marginBottom": "0.25rem"}))
        feedback = dbc.Alert("Chat message sent. Instructions will be used in next action.", color="info", dismissable=True)
        logger.info(f"Chat message sent: {chat_input}")

    last_chat = ""
    if isinstance(new_chat_history, list) and new_chat_history:
        for item in reversed(new_chat_history):
            if isinstance(item, html.Div):
                children = item.children
                if len(children) > 1 and isinstance(children[1], html.Span):
                    last_chat = children[1].children
                    break
    elif isinstance(chat_input, str):
        last_chat = chat_input

    if triggered_id in ["action-shred", "action-generate", "action-compliance", "action-recover", "action-virtual-board", "action-loe"]:
        loading_message = dbc.Alert("Processing request, please wait...", color="primary", dismissable=False, style={"textAlign": "center"})
        doc_id, doc = next(iter(uploaded_documents.items()))
        file_name = doc['filename']
        file_content = doc['content']
        action_type = triggered_id.replace("action-", "").replace("-", " ").title()

        # DECODE the document content for use in the prompt
        try:
            content_type, content_string = file_content.split(',')
            decoded = base64.b64decode(content_string)
            if file_name.lower().endswith('.txt'):
                document_text = decoded.decode('utf-8', errors='replace')
            else:
                document_text = f"[Start of document {file_name} as base64]\n{decoded[:350].hex()}...[truncated]\n[End of document]"
        except Exception as e:
            logger.error(f"Could not decode document {file_name} for Anthropic: {e}")
            document_text = f"[Could not decode {file_name}]"

        logger.info(f"Sending document content of length {len(document_text)} to Anthropic for {action_type}")

        result_holder = {}

        def threaded_api_call():
            if triggered_id == "action-shred":
                prompt = (
                    "Shred this document into requirements, organized by section. "
                    "Identify requirements by action words (shall, will, perform, etc). "
                    "Output as spreadsheet: PWS Section, Requirement.\n\n"
                    f"Document Content:\n{document_text}\n"
                )
                task_type = "Shred"
            elif triggered_id == "action-generate":
                prompt = (
                    "Generate a detailed proposal response, organized by section/subsection. "
                    "Focus on approach, steps, workflow, people, processes, technology. "
                    "Include research validation and citations. Address Red Review findings.\n\n"
                    f"Document Content:\n{document_text}\n"
                )
                task_type = "Generate Proposal Response"
            elif triggered_id == "action-compliance":
                prompt = (
                    "Check compliance of the proposal response against the shredded requirements. "
                    "Produce a spreadsheet: PWS number, requirement, finding, recommendation.\n\n"
                    f"Proposal Response Document Content:\n{document_text}\n"
                )
                task_type = "Check Compliance"
            elif triggered_id == "action-recover":
                prompt = (
                    "Using the compliance spreadsheet, improve the document sections. "
                    "Address recommendations without materially changing content. "
                    "Organize improvements by PWS section headers/subheaders.\n\n"
                    f"Document Content:\n{document_text}\n"
                )
                task_type = "Recover Document"
            elif triggered_id == "action-virtual-board":
                prompt = (
                    "Evaluate the proposal based on requirements and evaluation criteria. "
                    "Generate a section-by-section evaluation spreadsheet using ratings: "
                    "unsatisfactory, satisfactory, good, very good, excellent. Include explanations. "
                    "Base evaluation on sections L and M.\n\n"
                    f"Document Content:\n{document_text}\n"
                )
                task_type = "Virtual Board"
            elif triggered_id == "action-loe":
                prompt = (
                    "Estimate Level of Effort for the proposal. Output spreadsheet: "
                    "PWS task area, brief description, labor categories, estimated hours per category.\n\n"
                    f"Document Content:\n{document_text}\n"
                )
                task_type = "Estimate LOE"
            else:
                prompt = ""
                task_type = "Unknown"
            logger.info(f"Prompt to Anthropic for {action_type}: {prompt[:400]}...[truncated]")
            result_holder["result"] = anthropic_api_call(prompt, files=[file_content], task_type=task_type, extra_instructions=last_chat or "")

        thread = threading.Thread(target=threaded_api_call)
        thread.start()
        thread.join()

        result = result_holder.get("result", "[No result]")
        generated_content[triggered_id] = result
        new_preview_content = f"{action_type} Output for {file_name}:\n\n{result}"
        feedback = dbc.Alert(f"{action_type} completed.", color="success", dismissable=True)
        logger.info(f"{action_type} completed for {file_name}")

    return doc_list_items, new_preview_content, feedback, loading_message, new_chat_history

if __name__ == '__main__':
    print("Starting the Dash application...")
    app.run(debug=True, host='0.0.0.0', port=7860, threaded=True)
    print("Dash application has finished running.")