Update app.py via AI Editor

app.py

@@ -3,22 +3,25 @@ import io
 import os
 import pandas as pd
 from docx import Document
-from io import BytesIO
+from io import BytesIO, StringIO
 import dash
 import dash_bootstrap_components as dbc
 from dash import html, dcc, Input, Output, State, callback_context
-import google.generativeai as genai
 from docx.shared import Pt
 from docx.enum.style import WD_STYLE_TYPE
 from PyPDF2 import PdfReader
-from io import StringIO
+import openai
+import logging
+import threading
+
+# Logging configuration
+logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s %(message)s')
 
 # Initialize Dash app
 app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
 
-# Configure Gemini AI
-genai.configure(api_key=os.environ["GEMINI_API_KEY"])
-model = genai.GenerativeModel('gemini-2.5-pro-preview-03-25')
+# Configure OpenAI
+openai.api_key = os.environ.get("OPENAI_API_KEY", "")
 
 # Global variables
 uploaded_files = {}
@@ -44,6 +47,9 @@ app.layout = dbc.Container([
     dbc.Row([
         dbc.Col([
             html.H4("Proposal Documents", className="mt-3 mb-4"),
+            html.Div([
+                html.Div(className="blinking-dot", style={'margin':'0 auto','width':'16px','height':'16px'}),
+            ], style={'textAlign':'center', 'marginBottom':'10px'}),
             dcc.Upload(
                 id='upload-document',
                 children=html.Div([
@@ -75,6 +81,9 @@ app.layout = dbc.Container([
             ])
         ], width=3),
         dbc.Col([
+            html.Div([
+                html.Div(className="blinking-dot", style={'margin':'0 auto','width':'16px','height':'16px'}),
+            ], style={'textAlign':'center', 'marginBottom':'10px'}),
             html.Div(id='status-bar', className="alert alert-info", style={'marginBottom': '20px'}),
             dcc.Loading(
                 id="loading-indicator",
@@ -107,7 +116,7 @@ app.layout = dbc.Container([
                 id="chat-loading",
                 type="dot",
                 children=[
-                    dbc.Input(id="chat-input", type="text", placeholder="Chat with AI to update document...", className="mb-2"),
+                    dbc.Input(id="chat-input", type="text", placeholder="Chat with AI to update document...", className="mb-2", style={'whiteSpace':'pre-wrap'}),
                     dbc.Button("Send", id="btn-send-chat", color="primary", className="mb-3"),
                     html.Div(id="chat-output")
                 ]
@@ -128,11 +137,14 @@ def process_document(contents, filename):
             pdf = PdfReader(BytesIO(decoded))
             text = ""
             for page in pdf.pages:
-                text += page.extract_text()
+                page_text = page.extract_text()
+                if page_text:
+                    text += page_text
             return text
         else:
             return f"Unsupported file format: {filename}. Please upload a PDF or DOCX file."
     except Exception as e:
+        logging.error(f"Error processing document: {str(e)}")
         return f"Error processing document: {str(e)}"
 
 @app.callback(
@@ -155,7 +167,8 @@ def update_output(list_of_contents, list_of_names, existing_files):
             ]))
         if existing_files is None:
             existing_files = []
-        shredded_document = None  # Reset shredded document when new files are uploaded
+        shredded_document = None
+        logging.info("Documents uploaded and file list updated.")
         return existing_files + new_files, "Document uploaded. Please click 'Shred' to proceed."
     return existing_files, "Please upload a document and click 'Shred' to begin."
 
@@ -173,7 +186,8 @@ def remove_file(n_clicks, existing_files):
         raise dash.exceptions.PreventUpdate
     removed_file = ctx.triggered[0]['prop_id'].split(',')[0].split(':')[-1].strip('}')
     uploaded_files.pop(removed_file, None)
-    shredded_document = None  # Reset shredded document when a file is removed
+    shredded_document = None
+    logging.info(f"Removed file: {removed_file}")
     return [file for file in existing_files if file['props']['children'][1]['props']['children'] != removed_file], "Document removed. Please upload a document and click 'Shred' to begin."
 
 def generate_document(document_type, file_contents):
@@ -207,8 +221,23 @@ to be sure we address them.
 Now, generate the {document_type}:
 """
 
-    response = model.generate_content(prompt)
-    return response.text
+    logging.info(f"Generating document for type: {document_type}")
+    try:
+        response = openai.ChatCompletion.create(
+            model="gpt-4-1106-preview",
+            messages=[
+                {"role": "system", "content": "You are a helpful, expert government proposal writer."},
+                {"role": "user", "content": prompt}
+            ],
+            max_tokens=4096,
+            temperature=0.25,
+        )
+        generated_text = response['choices'][0]['message']['content']
+        logging.info("Document generated successfully.")
+        return generated_text
+    except Exception as e:
+        logging.error(f"Error generating document: {str(e)}")
+        raise
 
 @app.callback(
     Output('document-preview', 'children'),
@@ -239,7 +268,7 @@ def generate_document_preview(*args):
             shredded_document = generate_document(document_type, file_contents)
             return dcc.Markdown(shredded_document), f"{document_type} generated", "Document shredded. You can now proceed with other operations.", {'display': 'none'}
         except Exception as e:
-            print(f"Error generating document: {str(e)}")
+            logging.error(f"Error generating document: {str(e)}")
             return html.Div(f"Error generating document: {str(e)}"), "Error", "An error occurred while shredding the document.", {'display': 'none'}
 
     if shredded_document is None:
@@ -262,9 +291,10 @@ def generate_document_preview(*args):
         if document_type == "Pink Review":
             pink_review_document = current_document
 
+        logging.info(f"{document_type} document generated successfully.")
         return dcc.Markdown(current_document), f"{document_type} generated", f"{document_type} document generated successfully.", {'display': 'none'}
     except Exception as e:
-        print(f"Error generating document: {str(e)}")
+        logging.error(f"Error generating document: {str(e)}")
         return html.Div(f"Error generating document: {str(e)}"), "Error", "An error occurred while generating the document.", {'display': 'none'}
 
 @app.callback(
@@ -274,6 +304,7 @@ def generate_document_preview(*args):
 )
 def update_pink_review_filename(contents, filename):
     if contents is not None:
+        logging.info(f"Pink Review file uploaded: {filename}")
         return filename
     return ""
 
@@ -298,11 +329,24 @@ Instructions:
 3. Incorporate the requested changes seamlessly.
 Now, provide the updated {document_type}:
 """
-    
-    response = model.generate_content(prompt)
-    current_document = response.text
-    
-    return f"Document updated based on: {chat_input}", dcc.Markdown(current_document)
+
+    logging.info(f"Updating document via chat for {document_type} instruction: {chat_input}")
+    try:
+        response = openai.ChatCompletion.create(
+            model="gpt-4-1106-preview",
+            messages=[
+                {"role": "system", "content": "You are a helpful, expert government proposal writer."},
+                {"role": "user", "content": prompt}
+            ],
+            max_tokens=4096,
+            temperature=0.2,
+        )
+        current_document = response['choices'][0]['message']['content']
+        logging.info("Document updated via chat successfully.")
+        return f"Document updated based on: {chat_input}", dcc.Markdown(current_document)
+    except Exception as e:
+        logging.error(f"Error updating document via chat: {str(e)}")
+        return f"Error updating document: {str(e)}", html.Div(f"Error updating document: {str(e)}")
 
 @app.callback(
     Output("download-document", "data"),
@@ -315,27 +359,29 @@ def download_document(n_clicks):
         raise dash.exceptions.PreventUpdate
     
     if document_type == "LOE":
-        # Create a pandas DataFrame for LOE
-        df = pd.read_csv(StringIO(current_document))
-        
-        # Save the DataFrame to an Excel file
-        output = BytesIO()
-        with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
-            df.to_excel(writer, sheet_name='LOE', index=False)
-        
-        return dcc.send_bytes(output.getvalue(), f"{document_type}.xlsx")
+        try:
+            df = pd.read_csv(StringIO(current_document))
+            output = BytesIO()
+            with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
+                df.to_excel(writer, sheet_name='LOE', index=False)
+            logging.info("LOE document downloaded as Excel.")
+            return dcc.send_bytes(output.getvalue(), f"{document_type}.xlsx")
+        except Exception as e:
+            logging.error(f"Error downloading LOE document: {str(e)}")
+            return dcc.send_string(f"Error downloading LOE: {str(e)}", f"{document_type}_error.txt")
     else:
-        # Create an in-memory Word document
-        doc = Document()
-        doc.add_paragraph(current_document)
-        
-        # Save the document to a BytesIO object
-        output = BytesIO()
-        doc.save(output)
-        
-        return dcc.send_bytes(output.getvalue(), f"{document_type}.docx")
+        try:
+            doc = Document()
+            doc.add_paragraph(current_document)
+            output = BytesIO()
+            doc.save(output)
+            logging.info("Document downloaded as Word.")
+            return dcc.send_bytes(output.getvalue(), f"{document_type}.docx")
+        except Exception as e:
+            logging.error(f"Error downloading document: {str(e)}")
+            return dcc.send_string(f"Error downloading document: {str(e)}", f"{document_type}_error.txt")
 
 if __name__ == '__main__':
     print("Starting the Dash application...")
-    app.run(debug=False, host='0.0.0.0', port=7860)
+    app.run(debug=True, host='0.0.0.0', port=7860, threaded=True)
     print("Dash application has finished running.")