Create app.py

app.py

@@ -0,0 +1,165 @@
+"""TEXT SUMMARIZATION Web APP"""
+
+# Importing Packages
+import base64
+import streamlit as st
+import torch
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers import pipeline
+
+# Load the tokenizer and model
+checkpoint = 'Lamini-1'
+tokenizer = T5Tokenizer.from_pretrained(checkpoint)
+base_model = T5ForConditionalGeneration.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.float32)
+
+
+# File Loader & Processing
+def file_processing(file):
+    loader = PyPDFLoader(file)
+    pages = loader.load_and_split()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=50)
+    texts = text_splitter.split_documents(pages)
+    final_texts = ""
+    for text in texts:
+        print(text)
+        final_texts = final_texts + text.page_content
+    return final_texts
+
+
+# Language Model Pipeline -> Summarization
+def llm_pipeline(filepath, summary_length):
+    pipe_summ = pipeline(
+        "summarization",
+        model=base_model,  # T5ForConditionalGeneration.from_pretrained(checkpoint),
+        tokenizer=tokenizer,  # T5Tokenizer.from_pretrained(checkpoint),
+        max_length=summary_length,
+        min_length=50,
+    )
+    input = file_processing(filepath)
+    result = pipe_summ(input)
+    result = result[0]["summary_text"]
+    return result
+
+
+# Streamlit Code
+st.set_page_config(layout="wide")
+
+
+# Display Background
+def add_bg_from_local(image_file):
+    with open(image_file, "rb") as image_file:
+        encoded_string = base64.b64encode(image_file.read())
+    st.markdown(
+        f"""
+    <style>
+    .stApp {{
+        background-image: url(data:image/{"png"};base64,{encoded_string.decode()});
+        background-size: cover;
+        opacity:0.9;
+    }}
+    </style>
+    """,
+        unsafe_allow_html=True,
+    )
+
+
+add_bg_from_local("Images/background.jpg")
+
+# Font Style
+with open("font.css") as f:
+    st.markdown("<style>{}</style>".format(f.read()), unsafe_allow_html=True)
+
+# Sidebar
+st.sidebar.image("Images/sidebar_pic.png")
+st.sidebar.title("ABOUT THE APP")
+st.sidebar.write(
+    "SummaScribe: Your PDF wingman! 🚀 Unleash the power of Streamlit and LangChain to transform boring text PDFs into "
+    "snappy summaries. Lightning-fast processing,ninja-level NLP algorithms, and a touch of magic—making info "
+    "extraction a breeze!"
+)
+selected_summary_length = st.sidebar.slider("SELECT SUMMARY STRENGTH", min_value=50, max_value=1000,
+                                            value=500)
+
+
+# Display pdf of a given file
+@st.cache_data
+def display(file):
+    # Opening file from filepath
+    with open(file, "rb") as f:
+        base64_pdf = base64.b64encode(f.read()).decode("utf-8")
+    # Embedding pdf in html
+    display_pdf = (
+        f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="500" '
+        f'type="application/pdf"></iframe>'
+    )
+    # Displaying File
+    st.markdown(display_pdf, unsafe_allow_html=True)
+
+
+# Main content
+st.markdown(
+    """
+    <style>
+    .summascribe-title {
+        font-size: 57px;
+        text-align: center;
+        transition: transform 0.2s ease-in-out;
+    }
+    .summascribe-title span {
+        transition: color 0.2s ease-in-out;
+    }
+    .summascribe-title:hover span {
+        color: #f5fefd; /* Hover color */
+    }
+    .summascribe-title:hover {
+        transform: scale(1.15);
+    }
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+text = "SummaScribe"  # Text to be styled
+colored_text = ''.join(
+    ['<span style="color: hsl(220, 60%, {}%);">{}</span>'.format(70 - (i * 10 / len(text)), char) for i, char in
+     enumerate(text)])
+colored_text_with_malt = colored_text + ' <span style="color: hsl(220, 60%, 70%);">&#x2727;</span>'
+st.markdown(f'<h1 class="summascribe-title">{colored_text_with_malt}</h1>', unsafe_allow_html=True)
+
+st.markdown(
+    '<h2 style="font-size:30px;color: #F5FEFD; text-align: center;">Text Document Summarization using LLMs</h2>',
+    unsafe_allow_html=True,
+)
+
+
+# Your Streamlit app content here...
+def main():
+    # st.title("SUMMASCRIBE")
+    # st.subheader("Text Document Summarization using Large Language Models")
+    uploaded_file = st.file_uploader("Upload PDF file", type=["pdf"])
+    with st.expander("NOTE"):
+        st.write(
+            "Summascribe currently accepts PDF documents that contain only text and no images. This limitation is due "
+            "to our app's current focus on leveraging advanced natural language processing (NLP) algorithms to "
+            "extract key information from textual content."
+        )
+    if uploaded_file is not None:
+        if st.button("Summarize"):
+            col1, col2 = st.columns((1, 1))
+            filepath = "data/" + uploaded_file.name
+            with open(filepath, "wb") as temp_file:
+                temp_file.write(uploaded_file.read())
+            with col1:
+                st.info("Uploaded File")
+                display(filepath)
+            with col2:
+                st.spinner(text="In progress...")
+                st.info("Summary")
+                summary = llm_pipeline(filepath, selected_summary_length)
+                st.success(summary, icon="✅")
+
+
+if __name__ == "__main__":
+    main()