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import streamlit as st
from transformers import pipeline
import pdfplumber
from PIL import Image
import pytesseract
from langdetect import detect
# Initialize Models
@st.cache_resource
def initialize_models():
return {
"report_check_model": pipeline("text-classification", model="facebook/bart-large-mnli"),
"sentiment_model": pipeline("sentiment-analysis"),
"summarize_model": pipeline("summarization", model="facebook/bart-large-cnn"),
"translation_model": {
"en": pipeline("translation", model="Helsinki-NLP/opus-mt-mul-en"),
"hi": pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi"),
"ur": pipeline("translation", model="Helsinki-NLP/opus-mt-en-ur")
}
}
# Extract text from PDF
def extract_text_from_pdf(pdf_file):
text = ""
with pdfplumber.open(pdf_file) as pdf:
for page in pdf.pages:
text += page.extract_text()
return text.strip()
# Extract text from Image
def extract_text_from_image(image_file):
image = Image.open(image_file)
text = pytesseract.image_to_string(image)
return text.strip()
# Check if content is a lab report
def is_lab_report(text, model):
result = model(text, candidate_labels=["lab report", "not lab report"])
return result["labels"][0] == "lab report"
# Analyze sentiment
def analyze_sentiment(text, sentiment_model):
result = sentiment_model(text)[0]
sentiment = "Positive" if result["label"] == "POSITIVE" else "Negative"
return sentiment, result["score"]
# Summarize content
def summarize_content(text, summarize_model):
summary = summarize_model(text, max_length=130, min_length=30, do_sample=False)
return summary[0]['summary_text']
# Translate content
def translate_content(text, translation_models):
return {
"English": text,
"Hindi": translation_models["hi"](text)[0]["translation_text"],
"Urdu": translation_models["ur"](text)[0]["translation_text"]
}
# Streamlit App
def main():
st.title("Lab Test Analyzer")
models = initialize_models()
uploaded_file = st.file_uploader("Upload a Lab Report (PDF, Image, or Text)", type=["pdf", "png", "jpg", "jpeg", "txt"])
if uploaded_file:
file_type = uploaded_file.name.split(".")[-1].lower()
extracted_text = ""
if file_type == "pdf":
st.write("Processing PDF file...")
extracted_text = extract_text_from_pdf(uploaded_file)
elif file_type in ["png", "jpg", "jpeg"]:
st.write("Processing Image file...")
extracted_text = extract_text_from_image(uploaded_file)
elif file_type == "txt":
st.write("Processing Text file...")
extracted_text = uploaded_file.read().decode("utf-8")
else:
st.error("Unsupported file type.")
if extracted_text:
st.subheader("Extracted Content")
st.text_area("Extracted Text", extracted_text, height=200)
# Check if it's a lab report
if not is_lab_report(extracted_text, models["report_check_model"]):
st.error("The uploaded file does not appear to be a lab report.")
return
st.success("The uploaded file is a valid lab report.")
# Sentiment Analysis
sentiment, confidence = analyze_sentiment(extracted_text, models["sentiment_model"])
st.subheader("Sentiment Analysis")
st.write(f"**Sentiment**: {sentiment} (Confidence: {confidence:.2f})")
# Summarization
summary = summarize_content(extracted_text, models["summarize_model"])
st.subheader("Summary")
st.text_area("Summary", summary, height=150)
# Translation
translations = translate_content(summary, models["translation_model"])
st.subheader("Translations")
st.write("**English**: ", translations["English"])
st.write("**Hindi**: ", translations["Hindi"])
st.write("**Urdu**: ", translations["Urdu"])
else:
st.error("Could not extract text from the uploaded file.")
if __name__ == "__main__":
main()
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