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app.py

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+# This is to demonstrate the core logic for the project
+
+# 1. Get the link to PDF
+# 2. Read the content of the PDF
+# 3. Iterate:
+#    3.1 Create a chunk (set of pages)
+#    3.2 Create summary by combining partial summary & chunk
+
+
+### 1. Import the libraries
+import streamlit as st
+import time
+import os
+from dotenv import load_dotenv
+
+from langchain.prompts import PromptTemplate
+
+# from langchain_community.llms import HuggingFaceHub
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_community.document_loaders import PyPDFLoader
+
+# This is to simplify local development
+# Without this you will need to copy/paste the API key with every change
+try:
+    # CHANGE the location of the file
+    load_dotenv('C:\\Users\\raj\\.jupyter\\.env')
+    # Add the API key to the session - use it for populating the interface
+    if os.getenv('HUGGINGFACEHUB_API_TOKEN'):
+        st.session_state['HUGGINGFACEHUB_API_TOKEN'] = os.getenv('HUGGINGFACEHUB_API_TOKEN')
+except:
+    print("Environment file not found !! Copy & paste your HuggingFace API key.")
+
+
+# Prompt to be used
+template = """
+    extend the abstractive summary below with the new content. Keep total size of the extended summary around 3000 words.
+
+    summary: 
+    {summary}
+
+    new content:
+    {content}
+
+    extended summary:
+    
+"""
+
+prompt_template = PromptTemplate(
+    input_variables = ['summary', 'content'],
+    template = template
+)
+
+# Model for summarization
+model_id = 'mistralai/Mistral-7B-Instruct-v0.2'
+CONTEXT_WINDOW_SIZE=32000
+MAX_TOKENS=2000
+
+
+if 'SUMMARY' not in st.session_state:
+    st.session_state['SUMMARY'] = ''
+
+# function to generate the summary
+def generate_summary():
+    
+    # Create an LLM
+    llm = HuggingFaceEndpoint(
+        repo_id=model_id, 
+        max_new_tokens=MAX_TOKENS
+    )
+
+    # Show spinner, while we are waiting for the response
+    with st.spinner('Invoking LLM ... '):
+        # 1. Load the PDF file
+        partial_summary = ''
+        loader = PyPDFLoader(pdf_link)
+        pages = loader.load()
+        page_count = len(pages)
+        print("Number of pages = ", page_count)
+
+        # 2. Iterate to generate the summary
+        
+        next_page_index = 0
+        while next_page_index < len(pages):
+            'Processing chunk, starting with page index : ',next_page_index
+
+            # Holds the chunk = a set of contenated pages
+            new_content = ''
+            
+            # Loop to create chunk 
+            for i, doc in enumerate(pages[next_page_index : ]):
+                last_i = i
+                if len(partial_summary) + len(new_content) + len(doc.page_content) + MAX_TOKENS < CONTEXT_WINDOW_SIZE :
+                    new_content = new_content + doc.page_content
+                else:
+                    break
+                    
+            # Initialize the new content and next page index
+            next_page_index = next_page_index + last_i + 1
+                
+            # Pass the current summary and new content to LLM for summarization
+            query = prompt_template.format(summary=partial_summary, content=new_content)
+            
+            
+
+            partial_summary = llm.invoke(query)
+        st.session_state['SUMMARY'] = partial_summary
+        
+
+# Title
+st.title('PDF Summarizer')
+
+if 'HUGGINGFACEHUB_API_TOKEN' in st.session_state:
+    cohere_api_key = st.sidebar.text_input('HuggingFace API key',value=st.session_state['HUGGINGFACEHUB_API_TOKEN'])
+else:
+    cohere_api_key = st.sidebar.text_input('HuggingFace API key',placeholder='copy & paste your API key')
+
+
+# draw the box for query
+pdf_link = st.text_input('Link to PDF document', placeholder='copy/paste link to the PDF', value='https://sgp.fas.org/crs/misc/R47644.pdf')
+
+# button
+st.button("Generate sumary", on_click=generate_summary)
+
+
+st.text_area('Response', value = st.session_state['SUMMARY'], height=800)
+ 

requirements.txt

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+python-dotenv
+langchain