Delete app.py

app.py

@@ -1,62 +0,0 @@
-import streamlit as st
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.document_loaders import PyPDFLoader
-from transformers import T5Tokenizer, T5ForConditionalGeneration
-from transformers import pipeline
-import torch
-import base64
-import time
-from PIL import Image
-
-st.image("https://huggingface.co/spaces/wiwaaw/summary/resolve/main/banner.png")
-
-#MODEL AND TOKENIZER
-model_checkpoint = "MBZUAI/LaMini-Flan-T5-783M"
-model_tokenizer = T5Tokenizer.from_pretrained(model_checkpoint)
-model = T5ForConditionalGeneration.from_pretrained(model_checkpoint)
-
-#FILE LOADER AND PREPROCESSING
-def preprocess_pdf(file):
-    loader = PyPDFLoader(file)
-    pages = loader.load_and_split()
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=170, chunk_overlap=70)
-    texts = text_splitter.split_documents(pages)
-    final_text = ""
-    for text in texts:
-        final_text = final_text + text.page_content
-    return final_text
-
-@st.cache_data
-#LLM PIPELINE
-def language_model_pipeline(filepath):
-    summarization_pipeline = pipeline(
-        'summarization',
-        model = model,
-        tokenizer = model_tokenizer,
-        max_length = 500,
-        min_length = 32
-    )
-    input_text = preprocess_pdf(filepath)
-    summary_result = summarization_pipeline(input_text)
-    summarized_text = summary_result[0]['summary_text']
-    return summarized_text
-
-title = st.title("PDF Summarization using LaMini")
-uploaded_file = st.file_uploader('Upload your PDF file', type=['pdf'])
-if uploaded_file is not None:
-    st.success("File Uploaded")
-    if st.button ("Summarize"):
-        time.sleep(10)
-        
-        filepath = uploaded_file.name
-        with open(filepath, "wb") as temp_file:
-            temp_file.write(uploaded_file.read())
-        
-        summarized_result = language_model_pipeline(filepath)
-        st.info("Summarization Complete")
-        st.success(summarized_result)
-
-
-
-
-