Update app.py via AI Editor

app.py

@@ -10,37 +10,28 @@ import uuid
 import time
 from flask import Flask
 
-# Anthropic API stub (replace with real implementation)
 import requests
 
-# For allowed file types and basic file handling
 ALLOWED_EXTENSIONS = ('pdf', 'doc', 'docx', 'txt')
 
-# Logging configuration
 logging.basicConfig(
     format="%(asctime)s %(levelname)s:%(message)s",
     level=logging.INFO
 )
 logger = logging.getLogger(__name__)
 
-# In-memory storage for uploaded documents and results
 uploaded_documents = {}
 generated_content = {}
 
-# Anthropic API endpoint/config stub
 ANTHROPIC_API_URL = "https://api.anthropic.com/v1/messages"
-ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY", "YOUR_ANTHROPIC_API_KEY")  # Replace as appropriate
+ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY", "YOUR_ANTHROPIC_API_KEY")
 
-# Flask server for Dash
 server = Flask(__name__)
 
-# CUDA GPU configuration (for completeness, if used by downstream libraries)
 os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 
-# External stylesheets
 external_stylesheets = [dbc.themes.BOOTSTRAP]
 
-# Dash app initialization
 app = dash.Dash(
     __name__,
     server=server,
@@ -49,7 +40,6 @@ app = dash.Dash(
     title="Proposal Writing Assistant"
 )
 
-# Helper functions
 def allowed_file(filename):
     return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
 
@@ -64,7 +54,6 @@ def save_uploaded_file(file_content, filename):
 
 def anthropic_api_call(prompt, files=None, task_type=None, extra_instructions=""):
     logger.info(f"Calling Anthropic API for task: {task_type}")
-    # Stubbed implementation; replace with actual Anthropic API call
     headers = {
         "x-api-key": ANTHROPIC_API_KEY,
         "content-type": "application/json"
@@ -80,7 +69,6 @@ def anthropic_api_call(prompt, files=None, task_type=None, extra_instructions=""
     try:
         # response = requests.post(ANTHROPIC_API_URL, headers=headers, json=data, timeout=120)
         # result = response.json().get('content', ['[Anthropic response placeholder]'])[0]
-        # Simulated response:
         time.sleep(2)
         result = f"[Simulated response for {task_type}]"
         logger.info(f"Anthropic API success for task: {task_type}")
@@ -92,7 +80,6 @@ def anthropic_api_call(prompt, files=None, task_type=None, extra_instructions=""
 def parse_contents(contents, filename):
     content_type, content_string = contents.split(',')
     decoded = base64.b64decode(content_string)
-    # Only display first few characters for preview
     try:
         if filename.lower().endswith('.txt'):
             preview = decoded.decode('utf-8')[:2048]
@@ -103,7 +90,6 @@ def parse_contents(contents, filename):
         logger.error(f"Could not decode file {filename}: {e}")
         return f"Error decoding {filename}"
 
-# UI Elements
 def navbar():
     return dbc.Card(
         [
@@ -245,7 +231,6 @@ def main_layout():
 
 app.layout = main_layout
 
-# Callbacks
 @app.callback(
     Output("uploaded-doc-list", "children"),
     Output("preview-content", "children"),
@@ -285,7 +270,6 @@ def main_callback(
     new_chat_history = chat_history if chat_history else []
     doc_list_items = []
 
-    # Handle file upload
     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)
@@ -296,7 +280,6 @@ def main_callback(
             feedback = dbc.Alert(f"Uploaded {upload_filename}", color="success", dismissable=True)
             logger.info(f"File uploaded: {upload_filename}")
 
-    # Update doc list
     for doc_id, doc in uploaded_documents.items():
         doc_list_items.append(
             html.Li(
@@ -308,7 +291,6 @@ def main_callback(
             )
         )
 
-    # Handle document deletion
     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())
@@ -328,13 +310,11 @@ def main_callback(
             ]
             new_preview_content = "" if not uploaded_documents else no_update
 
-    # If no uploaded documents, block actions
     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
 
-    # Handle chat
     if triggered_id == "btn-send-chat" and chat_input and chat_input.strip():
         if not isinstance(new_chat_history, list):
             new_chat_history = []
@@ -343,9 +323,7 @@ def main_callback(
         ], 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}")
-        # Clear chat input (handled client-side)
 
-    # Identify last chat instructions to use
     last_chat = ""
     if isinstance(new_chat_history, list) and new_chat_history:
         for item in reversed(new_chat_history):
@@ -357,7 +335,6 @@ def main_callback(
     elif isinstance(chat_input, str):
         last_chat = chat_input
 
-    # Action buttons: always require at least one document
     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()))
@@ -365,7 +342,20 @@ def main_callback(
         file_content = doc['content']
         action_type = triggered_id.replace("action-", "").replace("-", " ").title()
 
-        # Anthropic API call in a thread
+        # 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():
@@ -373,27 +363,31 @@ def main_callback(
                 prompt = (
                     "Shred this document into requirements, organized by section. "
                     "Identify requirements by action words (shall, will, perform, etc). "
-                    "Output as spreadsheet: PWS Section, Requirement."
+                    "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."
+                    "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."
+                    "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."
+                    "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":
@@ -401,18 +395,21 @@ def main_callback(
                     "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."
+                    "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."
+                    "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)