File size: 6,509 Bytes
afeacc2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 |
import streamlit as st
from PyPDF2 import PdfReader
import textract
from transformers import pipeline
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.llms import HuggingFaceHub
import random
# Function to create a multi-color line
def multicolor_line():
colors = ["#FF5733", "#33FF57", "#3357FF", "#FF33A1", "#FFC300"]
return f'<hr style="border: 1px solid {random.choice(colors)};">'
# Initialize the Hugging Face model for summarization
@st.cache_resource
def load_summarization_model():
return pipeline("summarization", model="facebook/bart-large-cnn")
# Initialize the Hugging Face model for critique generation (using T5)
@st.cache_resource
def load_critique_model():
return pipeline("text2text-generation", model="t5-base")
summarizer = load_summarization_model()
critique_generator = load_critique_model()
# Function to extract text from PDFs
def extract_text_from_pdf(pdf_file="/content/A_Validation_of_Six_Wearable_Devices_for_Estimatin.pdf"):
pdf_reader = PdfReader(pdf_file)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
return text
# Function to extract text from text files
def extract_text_from_file(txt_file):
with open(txt_file, "r") as file:
text = file.read()
return text
# Function to extract text from scanned PDFs or other formats
def extract_text_from_scanned_pdf(pdf_file):
text = textract.process(pdf_file).decode("utf-8")
return text
# Function to generate the summary using Hugging Face (BART model)
def summarize_text(text):
max_len = 1024 # Define the max input length for the summarizer
min_len = 50 # Define the minimum length for the summary
if not text.strip():
raise ValueError("Input text is empty, unable to summarize.")
if len(text.split()) > max_len:
text = " ".join(text.split()[:max_len])
if len(text.split()) < min_len:
raise ValueError("Input text is too short for summarization.")
summary = summarizer(text, max_length=200, min_length=50, do_sample=False)
return summary[0]['summary_text']
# Function to generate critique using the Hugging Face T5 model
def generate_critique(summary):
critique_input = f"Critique: {summary}"
critique = critique_generator(critique_input)
return critique[0]['generated_text']
# Function to refine the summary using critique feedback
def refine_summary(summary, critique):
refinement_input = f"Summary: {summary}\n\nCritique: {critique}\n\nRefine this into a cohesive and polished summary:"
refined_output = summarizer(refinement_input, max_length=300, min_length=100, do_sample=False)
return refined_output[0]['summary_text']
# LangChain Integration: Set up Hugging Face as the LLM for LangChain
hf_llm = HuggingFaceHub(repo_id="facebook/bart-large-cnn", model_kwargs={"temperature": 0.5} )
# Create a PromptTemplate for summarization
prompt_template = PromptTemplate(
input_variables=["text"],
template="Summarize the following text:\n{text}"
)
# Define the LangChain chain for summarization
def create_summarization_chain():
chain = LLMChain(llm=hf_llm, prompt=prompt_template)
return chain
# Update the Streamlit workflow
def main():
st.title("Multi-Agent Research Assistant for Refining Academic Content")
st.write("Upload a PDF or Text file to start the process.")
uploaded_file = st.file_uploader("Choose a PDF or Text file", type=["pdf", "txt"])
if uploaded_file is not None:
# Extract text from uploaded file
file_extension = uploaded_file.name.split('.')[-1].lower()
if file_extension == 'pdf':
st.write("Extracting text from PDF...")
text = extract_text_from_pdf(uploaded_file)
elif file_extension == 'txt':
st.write("Extracting text from Text file...")
text = extract_text_from_file(uploaded_file)
else:
st.error("Unsupported file type. Please upload a PDF or a Text file.")
return
if text.strip() == "":
st.error("No text could be extracted from the file.")
return
# Show extracted text if checkbox is checked
show_text = st.checkbox("Show extracted text")
if show_text:
# Increase the width of the text area slightly
st.text_area("Extracted Text", text, height=200, max_chars=2000, key="extracted_text", label_visibility="hidden")
# Show multi-color line after text extraction
st.markdown(multicolor_line(), unsafe_allow_html=True)
# Summarize text using Hugging Face model (BART)
st.write("Summarizing the content...")
try:
summary = summarize_text(text)
st.write("Summary:")
# Increase the width of the summary text area
st.text_area("Summary", summary, height=200, max_chars=2000, key="summary", label_visibility="hidden")
except Exception as e:
st.error(f"Error generating summary:\n\n{e}")
return
# Show multi-color line after summarization
st.markdown(multicolor_line(), unsafe_allow_html=True)
# Generate critique based on summary using Hugging Face model (T5)
st.write("Generating critique...")
try:
critique = generate_critique(summary)
st.write("Critique:")
# Increase the width of the critique text area
st.text_area("Critique", critique, height=200, max_chars=2000, key="critique", label_visibility="hidden")
except Exception as e:
st.error(f"Error generating critique:\n\n{e}")
return
# Show multi-color line after critique generation
st.markdown(multicolor_line(), unsafe_allow_html=True)
# Refine the summary using critique feedback
st.write("Refining the summary...")
try:
refined_summary = refine_summary(summary, critique)
st.write("Refined Summary:")
# Increase the width of the refined summary text area
st.text_area("Refined Summary", refined_summary, height=200, max_chars=2000, key="refined_summary", label_visibility="hidden")
except Exception as e:
st.error(f"Error refining summary:\n\n{e}")
return
# Show multi-color line after refinement
st.markdown(multicolor_line(), unsafe_allow_html=True)
if __name__ == "__main__":
main() |