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Opinionated Analysis Report.py
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Opinionated Analysis Report.py
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import os
import json
import tkinter as tk
from tkinter import filedialog, messagebox, scrolledtext
from docx import Document
import PyPDF2
import nltk
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from collections import defaultdict, Counter
from string import punctuation
# Ensure NLTK data files are downloaded
nltk.download('punkt')
nltk.download('stopwords')
# Initialize global variables
document_content = ""
current_document_name = ""
def load_personality(personality_file):
log_progress("Loading personality file...")
with open(personality_file, 'r') as file:
personality = json.load(file)
log_progress("Personality file loaded.")
return personality
def read_document(filepath):
log_progress(f"Reading document: {filepath}")
if filepath.endswith('.txt'):
try:
with open(filepath, 'r', encoding='utf-8') as file:
content = file.read()
log_progress(f"Document content loaded (first 100 characters): {content[:100]}")
return content
except UnicodeDecodeError:
with open(filepath, 'r', encoding='latin-1') as file:
content = file.read()
log_progress(f"Document content loaded with latin-1 encoding (first 100 characters): {content[:100]}")
return content
elif filepath.endswith('.docx'):
doc = Document(filepath)
full_text = []
for para in doc.paragraphs:
full_text.append(para.text)
content = '\n'.join(full_text)
log_progress(f"Document content loaded (first 100 characters): {content[:100]}")
return content
elif filepath.endswith('.pdf'):
with open(filepath, 'rb') as file:
reader = PyPDF2.PdfFileReader(file)
full_text = []
for page_num in range(reader.numPages):
page = reader.getPage(page_num)
full_text.append(page.extract_text())
content = '\n'.join(full_text)
log_progress(f"Document content loaded (first 100 characters): {content[:100]}")
return content
return ""
def generate_opinionated_analysis(content, personality):
log_progress("Generating opinionated analysis based on personality...")
personality_descriptions = []
for model, traits in personality.items():
for trait, details in traits.items():
if details["selected"]:
personality_descriptions.append(details['options'][details['selected']])
personality_traits_summary = "\n".join(personality_descriptions)
# General personalized insights based on personality and document content
words = word_tokenize(content.lower())
stop_words = set(stopwords.words('english') + list(punctuation))
meaningful_words = [word for word in words if word.isalnum() and word not in stop_words]
most_common_words = [word for word, freq in Counter(meaningful_words).most_common(10)]
key_themes = f"The document discusses various aspects, prominently including {', '.join(most_common_words)}."
opinionated_analysis = (
"Opinionated Analysis Report\n"
"===========================\n\n"
f"Introduction:\nThe document provides a comprehensive overview of its topic. {key_themes}\n\n"
"Key Themes and Topics:\n"
"----------------------\n"
f"{', '.join(most_common_words)}\n\n"
"Emotional Tone and Sentiment:\n"
"-----------------------------\n"
f"The emotional tone of the document can be described as "
f"{'positive' if sum(1 for word in words if word in ['good', 'happy', 'positive', 'excellent', 'fortunate', 'correct', 'superior']) > sum(1 for word in words if word in ['bad', 'sad', 'negative', 'poor', 'unfortunate', 'wrong', 'inferior']) else 'negative' if sum(1 for word in words if word in ['bad', 'sad', 'negative', 'poor', 'unfortunate', 'wrong', 'inferior']) > sum(1 for word in words if word in ['good', 'happy', 'positive', 'excellent', 'fortunate', 'correct', 'superior']) else 'neutral'}.\n\n"
"Structure and Organization:\n"
"---------------------------\n"
"The document is well-organized with clear sections and a coherent flow of ideas. The structure aids in conveying the main points effectively, making it easier for the reader to follow.\n\n"
"Language and Style:\n"
"------------------\n"
"The language used in the document is straightforward and focused, with frequent use of words such as "
f"{', '.join(most_common_words)}, which suggests a specific focus on these areas.\n\n"
"Opinion Based on Personality Traits:\n"
"===================================\n"
f"{personality_traits_summary}\n"
)
return opinionated_analysis
def analyze_document(content, personality):
log_progress("Analyzing document content...")
words = word_tokenize(content.lower())
stop_words = set(stopwords.words('english') + list(punctuation))
filtered_words = [word for word in words if word.isalnum() and word not in stop_words]
# Generate opinionated analysis
opinionated_analysis = generate_opinionated_analysis(content, personality)
# Sentiment Analysis
positive_words = set(["good", "happy", "positive", "excellent", "fortunate", "correct", "superior"])
negative_words = set(["bad", "sad", "negative", "poor", "unfortunate", "wrong", "inferior"])
sentiment_score = sum(1 for word in words if word in positive_words) - sum(1 for word in words if word in negative_words)
# Document Structure Analysis
paragraphs = content.split('\n\n')
num_paragraphs = len(paragraphs)
num_sentences = len(sent_tokenize(content))
# Word Frequency Analysis
word_freq = Counter(filtered_words).most_common(10)
analysis = defaultdict(list)
for model, traits in personality.items():
for trait, details in traits.items():
opinion = details["options"][details["selected"]]
analysis[model].append(f"{trait}: {opinion}")
report = generate_report_text(analysis, opinionated_analysis, sentiment_score, num_paragraphs, num_sentences, word_freq)
log_progress("Document analysis complete.")
return report
def generate_report_text(analysis, opinionated_analysis, sentiment_score, num_paragraphs, num_sentences, word_freq):
report_lines = [f"{opinionated_analysis}\n"]
report_lines.append("-" * 80)
report_lines.append(f"Sentiment Analysis:\n{'=' * 80}\n{'Positive' if sentiment_score > 0 else 'Negative' if sentiment_score < 0 else 'Neutral'} (score: {sentiment_score})\n")
report_lines.append("-" * 80)
report_lines.append(f"Document Structure:\n{'=' * 80}\n Paragraphs: {num_paragraphs}\n Sentences: {num_sentences}\n")
report_lines.append("-" * 80)
report_lines.append("Word Frequency Analysis:\n" + '=' * 80)
for word, freq in word_freq:
report_lines.append(f" {word}: {freq}")
report_lines.append("-" * 80)
report_lines.append("Opinion-based Analysis:\n" + '=' * 80)
for model, traits in analysis.items():
report_lines.append(f"{model}:")
for trait_opinion in traits:
report_lines.append(f" {trait_opinion}")
report_lines.append("-" * 80)
report_text = '\n'.join(report_lines)
log_progress(f"Generated report text (first 100 characters): {report_text[:100]}")
return report_text
def save_report(report_text, output_folder, document_name):
os.makedirs(output_folder, exist_ok=True)
report_path = os.path.join(output_folder, f"{document_name}_report.txt")
with open(report_path, 'w') as report_file:
report_file.write(report_text)
messagebox.showinfo("Report Saved", f"Report saved to {report_path}")
log_progress(f"Report saved to {report_path}")
def load_document():
global document_content, current_document_name
filepath = filedialog.askopenfilename(filetypes=[("Text files", "*.txt"), ("Word documents", "*.docx"), ("PDF files", "*.pdf")])
if not filepath:
return
document_content = read_document(filepath)
document_display.delete('1.0', tk.END)
document_display.insert(tk.END, document_content)
analyze_button.config(state=tk.NORMAL)
current_document_name = os.path.splitext(os.path.basename(filepath))[0]
log_progress(f"Loaded document: {current_document_name}")
def analyze_document_action():
global document_content
if document_content:
log_progress("Starting document analysis...")
report_text = analyze_document(document_content, personality)
if report_text:
report_display.delete('1.0', tk.END)
report_display.insert(tk.END, report_text)
export_button.config(state=tk.NORMAL)
log_progress("Document analysis complete.")
else:
log_progress("Analysis report is empty.")
messagebox.showerror("Error", "Analysis failed. The report is empty.")
else:
messagebox.showerror("Error", "No document loaded to analyze!")
log_progress("Error: No document loaded to analyze.")
def export_analysis():
global current_document_name
report_text = report_display.get('1.0', tk.END).strip()
if report_text:
save_report(report_text, 'Analysis Reports', current_document_name)
else:
messagebox.showerror("Error", "No report to save!")
log_progress("Error: No report to save.")
def log_progress(message):
progress_display.config(state=tk.NORMAL)
progress_display.insert(tk.END, message + "\n")
progress_display.see(tk.END)
progress_display.config(state=tk.DISABLED)
print(message)
# Set up the GUI
root = tk.Tk()
root.title("Opinionated Document Analyzer")
frame = tk.Frame(root, padx=10, pady=10)
frame.pack(fill=tk.BOTH, expand=True)
left_frame = tk.Frame(frame)
left_frame.pack(side=tk.LEFT, fill=tk.BOTH, expand=True)
right_frame = tk.Frame(frame, bg="black")
right_frame.pack(side=tk.RIGHT, fill=tk.BOTH, expand=True)
button_frame = tk.Frame(left_frame)
button_frame.pack(pady=5)
load_button = tk.Button(button_frame, text="Load Document", command=load_document)
load_button.pack(side=tk.LEFT, padx=5)
analyze_button = tk.Button(button_frame, text="Analyze Document", command=analyze_document_action, state=tk.DISABLED)
analyze_button.pack(side=tk.LEFT, padx=5)
export_button = tk.Button(button_frame, text="Export Analysis", command=export_analysis, state=tk.DISABLED)
export_button.pack(side=tk.LEFT, padx=5)
document_label = tk.Label(left_frame, text="Document Content:")
document_label.pack(anchor='w')
document_display = scrolledtext.ScrolledText(left_frame, width=80, height=15, wrap=tk.WORD)
document_display.pack(pady=5)
report_label = tk.Label(left_frame, text="Analysis Report:")
report_label.pack(anchor='w')
report_display = scrolledtext.ScrolledText(left_frame, width=80, height=15, wrap=tk.WORD)
report_display.pack(pady=5)
progress_label = tk.Label(right_frame, text="Progress Log", bg="black", fg="white")
progress_label.pack(pady=5)
progress_display = scrolledtext.ScrolledText(right_frame, width=40, height=40, wrap=tk.WORD, bg="black", fg="white")
progress_display.pack(pady=5)
# Load personality file from the script's directory
script_dir = os.path.dirname(os.path.abspath(__file__))
personality_file = os.path.join(script_dir, 'Personality.json')
personality = load_personality(personality_file)
root.mainloop()