app.py

import streamlit as st
from models import initialize_models
from models.pdf_handler import parse_pdf
from models.image_handler import analyze_image
from models.summarizer import summarize_text
from models.translator import translate_text
from models.problem_checker import flag_lab_problems
from PIL import Image

# Initialize Models
models = initialize_models()

# Streamlit App
st.title("Lab Test Analyzer Dashboard")

# File Upload
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()

    # Extract Text Based on File Type
    extracted_text = ""
    if file_type == "pdf":
        st.write("Processing PDF file...")
        extracted_text = parse_pdf(uploaded_file)
    elif file_type in ["png", "jpg", "jpeg"]:
        st.write("Processing Image file...")
        image = Image.open(uploaded_file)
        extracted_text = analyze_image(image, models["image_model"])
    elif file_type == "txt":
        st.write("Processing Text file...")
        extracted_text = uploaded_file.read().decode("utf-8")
    else:
        st.error("Unsupported file type.")

    # Display Extracted Text
    if extracted_text:
        st.subheader("Extracted Content")
        st.text_area("Extracted Text", extracted_text, height=200)

        # Summarization
        summary = summarize_text(extracted_text, models["summarize_model"])
        st.subheader("Summary of the Report")
        st.text_area("Summary", summary, height=150)

        # Sentiment Analysis
        st.subheader("Sentiment Analysis")
        sentiment, confidence = analyze_sentiment(extracted_text, models["sentiment_model"])
        st.write(f"**Sentiment**: {sentiment} (Confidence: {confidence:.2f})")

        # Problem Detection
        problems = flag_lab_problems(summary)
        st.subheader("Detected Problems")
        st.write(problems)

        # Translation
        st.subheader("Translations")
        translations = translate_content(summary, models["translation_model"])
        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.")