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import streamlit as st
from transformers import pipeline
import PyPDF2
import docx
import textwrap
# Streamlit Page Config
st.set_page_config(
page_title="TextSphere",
page_icon="🤖",
layout="wide",
initial_sidebar_state="expanded"
)
# Footer
st.markdown("""
<style>
.footer {
position: fixed;
bottom: 0;
right: 0;
padding: 10px;
font-size: 16px;
color: #333;
background-color: #f1f1f1;
}
</style>
<div class="footer">
Made with ❤️ by Baibhav Malviya
</div>
""", unsafe_allow_html=True)
# Load Model
@st.cache_resource
def load_models():
try:
summarization_model = pipeline("summarization", model="facebook/bart-large-cnn")
except Exception as e:
raise RuntimeError(f"Failed to load model: {str(e)}")
return summarization_model
summarization_model = load_models()
# Function to Extract Text from PDF
def extract_text_from_pdf(uploaded_pdf):
try:
pdf_reader = PyPDF2.PdfReader(uploaded_pdf)
pdf_text = ""
for page in pdf_reader.pages:
text = page.extract_text()
if text:
pdf_text += text + "\n"
if not pdf_text.strip():
st.error("No text found in the PDF.")
return None
return pdf_text
except Exception as e:
st.error(f"Error reading the PDF: {e}")
return None
# Function to Extract Text from TXT
def extract_text_from_txt(uploaded_txt):
try:
return uploaded_txt.read().decode("utf-8").strip()
except Exception as e:
st.error(f"Error reading the TXT file: {e}")
return None
# Function to Extract Text from DOCX
def extract_text_from_docx(uploaded_docx):
try:
doc = docx.Document(uploaded_docx)
return "\n".join([para.text for para in doc.paragraphs]).strip()
except Exception as e:
st.error(f"Error reading the DOCX file: {e}")
return None
# Function to Split Text into 1024-Token Chunks
def chunk_text(text, max_tokens=1024):
return textwrap.wrap(text, width=max_tokens)
# Sidebar for Task Selection (Default: Text Summarization)
st.sidebar.title("AI Solutions")
option = st.sidebar.selectbox(
"Choose a task",
["Text Summarization", "Question Answering", "Text Classification", "Language Translation"],
index=0 # Default to "Text Summarization"
)
# Text Summarization Task
if option == "Text Summarization":
st.title("📄 Text Summarization")
st.markdown("<h4 style='font-size: 20px;'>- because who needs to read the whole document? 🥵</h4>", unsafe_allow_html=True)
uploaded_file = st.file_uploader(
"Upload a document (PDF, TXT, DOCX) - *Note: Processes only 1024 tokens per chunk*",
type=["pdf", "txt", "docx"]
)
text_to_summarize = ""
if uploaded_file:
file_type = uploaded_file.name.split(".")[-1].lower()
if file_type == "pdf":
text_to_summarize = extract_text_from_pdf(uploaded_file)
elif file_type == "txt":
text_to_summarize = extract_text_from_txt(uploaded_file)
elif file_type == "docx":
text_to_summarize = extract_text_from_docx(uploaded_file)
else:
st.error("Unsupported file format.")
if st.button("Summarize"):
with st.spinner('Summarizing...'):
try:
if text_to_summarize:
chunks = chunk_text(text_to_summarize, max_tokens=1024)
summaries = []
for chunk in chunks:
input_length = len(chunk.split()) # Count words in the chunk
max_summary_length = max(50, input_length // 2) # Dynamically adjust max_length
summary = summarization_model(chunk, max_length=max_summary_length, min_length=50, do_sample=False)
summaries.append(summary[0]['summary_text'])
final_summary = " ".join(summaries) # Combine all chunk summaries
st.write("### Summary:")
st.write(final_summary)
else:
st.error("Please upload a document first.")
except Exception as e:
st.error(f"Error: {e}")
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