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import gradio as gr
import docx
import PyPDF2
from pptx import Presentation
from transformers import pipeline
from docx import Document
from io import BytesIO
import tempfile
# Initialize Hugging Face models for summarization, rephrasing, and sentiment analysis
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
rephraser = pipeline("text2text-generation", model="Vamsi/T5_Paraphrase_Paws", max_length=512, truncation=True)
sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
# Function to read content from different file types
def read_file(file, file_type):
content = ""
try:
if file_type == "docx":
doc = Document(file)
for para in doc.paragraphs:
content += para.text + "\n"
elif file_type == "txt":
content = file.read().decode("utf-8")
elif file_type == "pdf":
pdf_reader = PyPDF2.PdfReader(file)
for page in pdf_reader.pages:
content += page.extract_text() + "\n"
elif file_type == "pptx":
prs = Presentation(file)
for slide in prs.slides:
for shape in slide.shapes:
if hasattr(shape, "text"):
content += shape.text + "\n"
except Exception as e:
content = f"Error reading the file: {str(e)}"
return content
# Function to process the file and generate outputs
def process_file(file, file_type, language="en"):
content = read_file(file, file_type)
# Check if content is not empty
if not content.strip() or "Error" in content:
return "Error: The document is empty or unsupported format.", None, None, None, None, None
# Summarize the content
try:
summary = summarizer(content, max_length=150, min_length=50, do_sample=False)
summary_text = summary[0]['summary_text']
except Exception as e:
summary_text = f"Summary Error: {str(e)}"
# Rephrase the entire content in manageable chunks
rephrased_text = ""
try:
chunk_size = 500
content_chunks = [content[i:i + chunk_size] for i in range(0, len(content), chunk_size)]
for chunk in content_chunks:
rephrased = rephraser(chunk)
rephrased_text += rephrased[0]['generated_text'] + " "
except Exception as e:
rephrased_text = f"Rephrase Error: {str(e)}"
# Sentiment analysis
try:
sentiment = sentiment_analyzer(content[:512])
sentiment_text = sentiment[0]['label']
except Exception as e:
sentiment_text = f"Sentiment Analysis Error: {str(e)}"
# Extract keywords (for simplicity, extracting words here, but you can replace this with a better method)
keywords = ' '.join([word for word in content.split()[:10]])
# Saving processed file (for download link)
try:
with tempfile.NamedTemporaryFile(delete=False, suffix='.txt') as temp_file:
temp_file.write(content.encode('utf-8'))
processed_file_path = temp_file.name
except Exception as e:
processed_file_path = f"Error saving processed document: {str(e)}"
return content, rephrased_text.strip(), summary_text, sentiment_text, keywords, processed_file_path
# Define the functions for the different pages
def home_page():
with gr.Blocks() as home:
# Header
gr.Markdown("## Upload a Document to Process")
# Menu bar as buttons
with gr.Row():
home_btn = gr.Button("Home")
full_analysis_btn = gr.Button("Full Analysis", variant="primary")
# Display content on home page
gr.Markdown("Welcome to the Document Processor!")
gr.Markdown("Upload your document here and click to view details on the 'Full Analysis' page.")
# File upload and content output
file_input = gr.File(label="Upload Document")
content_output = gr.Textbox(label="Original Content")
rephrased_output = gr.Textbox(label="Rephrased Content")
def on_file_upload(file):
if not file:
return "No file uploaded.", None
content, rephrased, _, _, _, _ = process_file(file, file_type="docx")
return content, rephrased
# Process file on upload
file_input.change(on_file_upload, inputs=file_input, outputs=[content_output, rephrased_output])
return home
def detailed_page():
with gr.Blocks() as detailed:
# Header
gr.Markdown("## Detailed Analysis Page")
# Menu bar as buttons
with gr.Row():
home_btn = gr.Button("Home", variant="primary")
full_analysis_btn = gr.Button("Full Analysis")
# File upload and processing components
file_input = gr.File(label="Upload Document")
file_type = gr.Dropdown(["pdf", "docx", "txt", "pptx"], label="File Type")
keywords_output = gr.Textbox(label="Keywords")
sentiment_output = gr.Textbox(label="Sentiment Analysis")
download_link = gr.File(label="Download Processed Document")
def on_file_upload(file, file_type):
if not file:
return "No file uploaded.", None, None, None
_, _, _, sentiment, keywords, download_path = process_file(file, file_type)
return keywords, sentiment, download_path
# Process file on upload
file_input.change(on_file_upload, inputs=[file_input, file_type], outputs=[keywords_output, sentiment_output, download_link])
# Sample output or content for the detailed analysis page
gr.Markdown("Here you will see detailed analysis outputs after document upload.")
return detailed
# Main application interface with tabbed navigation
iface = gr.TabbedInterface([home_page(), detailed_page()], ["Home", "Full Analysis"])
iface.launch()
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