app.py

import streamlit as st
import PyPDF2

def extract_text_from_pdf(file):
    pdf_reader = PyPDF2.PdfReader(file)
    text = ""
    for page_num in range(len(pdf_reader.pages)):
        page = pdf_reader.pages[page_num]
        text += page.extract_text()
    return text

st.title("ML Trainer")

# File uploader
uploaded_file = st.file_uploader("Choose a PDF or Text file", type=["pdf", "txt"])

if uploaded_file is not None:
    if uploaded_file.type == "application/pdf":
        text = extract_text_from_pdf(uploaded_file)
    else:  # Assuming it's a plain text file
        text = uploaded_file.read().decode('utf-8')

    # Display text fields
    col1, col2 = st.columns(2)
    with col1:
        st.header("Uploaded Text")
        st.text_area(text, height=300)
    with col2:
        st.header("User Notes")
        user_notes = st.text_area("", height=200)

        # Rating buttons
        st.write("Rating:")
        if st.button("Positive"):
            rating("positive", user_notes)
        if st.button("Satisfactory"):
            rating("satisfactory", user_notes)
        if st.button("Negative"):
            rating("negative", user_notes)

def rating(rating_label, rationale):
    # Placeholder for now - you'd add your ML model training logic here
    st.write("Rating:", rating_label)
    st.write("Rationale:", rationale)