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