first commit

app.py

@@ -0,0 +1,78 @@
+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
+
+access_token = st.secrets["HF_TOKEN"]
+
+# Model and tokenizer 
+#model_checkpoint = "LaMini-Flan-T5-248M"
+model_checkpoint = "MBZUAI/LaMini-Flan-T5-783M"
+model_tokenizer = T5Tokenizer.from_pretrained(model_checkpoint)
+model = T5ForConditionalGeneration.from_pretrained(model_checkpoint, device_map='auto', torch_dtype=torch.float32)
+
+#REPO_ID = "MBZUAI/LaMini-Flan-T5-783M"
+#model = pipeline(task='summarization', model=REPO_ID, token=access_token)
+
+# 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
+
+# Language Model pipeline
+def language_model_pipeline(filepath):
+    summarization_pipeline = pipeline(
+        'summarization',
+        model=model,
+        tokenizer=model_tokenizer,
+        max_length=500, 
+        min_length=70)
+    input_text = preprocess_pdf(filepath)
+    summary_result = summarization_pipeline(input_text)
+    summarized_text = summary_result[0]['summary_text']
+    return summarized_text
+
+@st.cache_data
+# Function to display the PDF content
+def display_pdf(file):
+    with open(file, "rb") as f:
+        base64_pdf = base64.b64encode(f.read()).decode('utf-8')
+
+    pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
+    st.markdown(pdf_display, unsafe_allow_html=True)
+
+# Streamlit code 
+st.set_page_config(layout="wide")
+
+def main():
+    st.title("Document Summarization App using Language Model")
+
+    uploaded_file = st.file_uploader("Upload your PDF file", type=['pdf'])
+
+    if uploaded_file is not None:
+        if st.button("Summarize"):
+            col1, col2 = st.columns(2)
+            filepath = "pdf/" + uploaded_file.name
+            with open(filepath, "wb") as temp_file:
+                temp_file.write(uploaded_file.read())
+            with col1:
+                st.info("Uploaded File")
+                pdf_view = display_pdf(filepath)
+
+            with col2:
+                summarized_result = language_model_pipeline(filepath)
+                st.info("Summarization Complete")
+                st.success(summarized_result)
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,14 @@
+langchain
+sentence_transformers
+torch
+sentencepiece
+transformers
+accelerate
+chromadb
+pypdf
+tiktoken
+streamlit
+fastapi
+uvicorn
+python-multipart
+aiofiles