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import streamlit as st
from transformers import pipeline
import pdfplumber
from langchain.llms import HuggingFacePipeline
from langchain.prompts import PromptTemplate
# Function to extract text from a PDF
def extract_text_from_pdf(pdf_file):
with pdfplumber.open(pdf_file) as pdf:
text = ''
for page in pdf.pages:
text += page.extract_text()
return text
# Define the prompt template
template = """
You are a medical summarization expert. Focus on the following key aspects when summarizing:
1. Patient History
2. Diagnosis
3. Treatment Recommendations
4. Follow-up Plans
Here’s the medical report to summarize:
{text}
"""
prompt = PromptTemplate(
input_variables=["text"],
template=template
)
# Streamlit application layout
st.title("Medical Report Summarizer")
# Option to upload PDF or enter text
option = st.selectbox("Choose Input Method", ["Upload PDF", "Enter Text"])
if option == "Upload PDF":
uploaded_file = st.file_uploader("Upload your PDF file", type=["pdf"])
if uploaded_file is not None:
# Extract text from the uploaded PDF
extracted_text = extract_text_from_pdf(uploaded_file)
# Dynamic calculation for max_length based on extracted text length
length = max(2, int(len(extracted_text) // 2))
# Load the summarization pipeline with updated max_length
summarizer = pipeline(
"summarization",
model="fine_tuned_model", # Ensure the path to your fine-tuned model is correct
temperature=0.3,
min_length=100,
max_length=int(length),
# top_k=80, # Uncomment if you want to use top_k
# top_p=0.95 # Uncomment if you want to use top_p
)
llm = HuggingFacePipeline(pipeline=summarizer)
# Create the formatted prompt
formatted_prompt = prompt.format(text=extracted_text)
# Generate the summary
summary = llm(formatted_prompt)
st.subheader("Summary:")
st.write(summary)
elif option == "Enter Text":
input_text = st.text_area("Enter the text to summarize", height=300)
if st.button("Summarize"):
if input_text:
# Dynamic calculation for max_length based on entered text length
length = max(2, int(len(input_text) // 2))
# Load the summarization pipeline with updated max_length
summarizer = pipeline(
"summarization",
model="fine_tuned_model",
temperature=0.3,
min_length=100,
max_length=int(length),
top_k=80,
top_p=0.95
)
llm = HuggingFacePipeline(pipeline=summarizer)
# Create the formatted prompt
formatted_prompt = prompt.format(text=input_text)
# Generate the summary
summary = llm(formatted_prompt)
st.subheader("Summary:")
st.write(summary)
else:
st.warning("Please enter some text to summarize.") |