app.py

import streamlit as st
import random
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
import torch
import io
from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
from pdfminer.converter import TextConverter
from pdfminer.layout import LAParams
from pdfminer.pdfpage import PDFPage
from docx import Document

# --- Streamlit Page Configuration ---
st.set_page_config(page_title="AI & Plagiarism Detection", page_icon="🔍", layout="wide")

# --- DeepSeek Theme ---
DEEPSEEK_THEME = {
    "backgroundColor": "#282c34",
    "textColor": "#abb2bf",
    "inputAreaColor": "#3E4451",
    "accentColor": "#61afef",
    "sidebarColor": "#21252b",
    "font": "sans-serif",
}

# --- Function to Apply Theme ---
def apply_theme(theme):
    st.markdown(f"""
        <style>
            body {{
                color: {theme["textColor"]};
                background-color: {theme["backgroundColor"]};
                font-family: {theme["font"]};
            }}
            .welcome-text {{
                color: {theme["textColor"]};
                font-size: 36px;
                font-weight: bold;
                text-align: center;
                margin-bottom: 20px;
            }}
            .output-box {{
                background-color: {theme["inputAreaColor"]};
                color: {theme["textColor"]};
                padding: 10px;
                border-radius: 5px;
                margin-top: 20px;
            }}
            .stTextArea textarea {{
                background-color: {theme["inputAreaColor"]};
                color: {theme["textColor"]};
                border: 1px solid {theme["accentColor"]};
                border-radius: 5px;
            }}
            .stFileUploader > div > div:nth-child(1) > div > button {{
                background-color: {theme["accentColor"]};
                color: {theme["backgroundColor"]};
                border-radius: 5px;
            }}
            .stMetricLabel {{
                color: {theme["textColor"]} !important;
            }}
            .stMetricValue {{
                color: {theme["textColor"]} !important;
            }}
            .streamlit-expanderHeader {{
                color: {theme["textColor"]};
            }}
            .streamlit-expanderContent {{
                color: {theme["textColor"]};
            }}
            [data-testid="stSidebar"] {{
                background-color: {theme["sidebarColor"]};
                color: {theme["textColor"]};
            }}
        </style>
    """, unsafe_allow_html=True)

# --- Helper Functions ---
def extract_text_from_pdf(pdf_file):
    resource_manager = PDFResourceManager()
    output_string = io.StringIO()
    laparams = LAParams()
    device = TextConverter(resource_manager, output_string, laparams=laparams)
    interpreter = PDFPageInterpreter(resource_manager, device)

    for page in PDFPage.get_pages(pdf_file, caching=True, check_extractable=True):
        interpreter.process_page(page)
    text = output_string.getvalue()
    device.close()
    output_string.close()
    return text

def extract_text_from_docx(docx_file):
    doc = Document(docx_file)
    full_text = []
    for paragraph in doc.paragraphs:
        full_text.append(paragraph.text)
    return '\n'.join(full_text)

def split_text_into_chunks(text, tokenizer, max_length=512):
    chunks = []
    tokens = tokenizer.tokenize(text)
    for i in range(0, len(tokens), max_length):
        chunk_tokens = tokens[i:i + max_length]
        chunk_text = tokenizer.convert_tokens_to_string(chunk_tokens)
        chunks.append(chunk_text)
    return chunks

@st.cache_resource
def load_ai_detection_model(model_name="Hello-SimpleAI/chatgpt-detector-roberta"):
    try:
        ai_detection = pipeline("text-classification", model=model_name, truncation=True, max_length=512)
        return ai_detection
    except Exception as e:
        st.error(f"Error loading AI detection model: {e}")
        return None

@st.cache_resource
def load_plagiarism_model(model_name="jpwahle/longformer-base-plagiarism-detection"):
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        model = AutoModelForSequenceClassification.from_pretrained(model_name)
        return tokenizer, model
    except Exception as e:
        st.error(f"Error loading plagiarism detection model: {e}")
        return None

def detect_ai_content(text_chunks, ai_detection_model, ai_threshold=0.4):
    try:
        ai_percentages = []
        for chunk in text_chunks:
            result = ai_detection_model(chunk)
            ai_label = result[0]['label']
            ai_score = result[0]['score']

            if ai_label == 'AI' and ai_score > ai_threshold:
                ai_percentages.append(ai_score)
            elif ai_label == 'Human' and ai_score < (1 - ai_threshold):
                ai_percentages.append(0)
            else:
                ai_percentages.append(0)

        return ai_percentages
    except Exception as e:
        st.error(f"Error during AI content detection: {e}")
        return None

def plagiarism_check(text_chunks, tokenizer, model):
    try:
        plagiarized_count = 0
        for chunk in text_chunks:
            inputs = tokenizer(chunk, return_tensors="pt", truncation=True, padding=True, max_length=512)
            with torch.no_grad():
                outputs = model(**inputs)
                predicted_class = torch.argmax(outputs.logits, dim=-1).item()
                if predicted_class == 1:
                    plagiarized_count += 1

        plagiarism_percentage = (plagiarized_count / len(text_chunks)) * 100
        return plagiarism_percentage
    except Exception as e:
        st.error(f"Error during plagiarism detection: {e}")
        return None

# --- Main Function ---
def main():
    # --- Apply DeepSeek Theme ---
    apply_theme(DEEPSEEK_THEME)

    # --- Sidebar ---
    with st.sidebar:
        st.markdown("<h1 style='color:#61afef;'>AI & Plagiarism</h1>", unsafe_allow_html=True)
        st.markdown("Navigation")
        menu_options = ["New Chat"]  # Removed "My Profile" and "Get App"
        selected_option = st.radio("Choose an option", menu_options)
        st.markdown("---")

        st.markdown("Today")
        recent_chats = ["Chat 1", "Chat 2", "Chat 3"]
        for chat in recent_chats:
            st.markdown(f"- {chat}")

    # --- Main Content ---
    col1, col2 = st.columns([1, 3])  # Adjust the ratio as needed

    with col2:
        st.markdown("<h1 class='welcome-text'>Hi, I'm AI & Plagiarism Assistant.</h1>", unsafe_allow_html=True)
        st.markdown("How can I help you today?")

        # --- Input Area: Text Area and File Upload ---
        input_text = st.text_area("Message", "", height=200)
        uploaded_files = st.file_uploader("Attach documents (PDF or DOCX)", type=["pdf", "docx"], accept_multiple_files=True)

        # --- Load models ---
        ai_detection_model, tokenizer, plagiarism_model = load_models()

        # --- Process Input ---
        if input_text or uploaded_files:
            raw_text = ""

            # --- Process Uploaded Files ---
            if uploaded_files:
                with st.expander("Uploaded Files", expanded=False):
                    for uploaded_file in uploaded_files:
                        file_size = len(uploaded_file.getvalue())
                        if file_size > 1000000000:
                            st.error(f"{uploaded_file.name}: File size exceeds the 1GB limit.")
                            continue

                        try:
                            if uploaded_file.type == "application/pdf":
                                extracted_text = extract_text_from_pdf(uploaded_file)
                                raw_text += extracted_text + "\n"
                                st.write(f"Extracted text from {uploaded_file.name}")
                            elif uploaded_file.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
                                extracted_text = extract_text_from_docx(uploaded_file)
                                raw_text += extracted_text + "\n"
                                st.write(f"Extracted text from {uploaded_file.name}")
                            else:
                                st.error(f"{uploaded_file.name}: Unsupported file type")
                                continue

                        except Exception as e:
                            st.error(f"Error processing {uploaded_file.name}: {e}")
                            continue

            # --- Append Manual Text ---
            raw_text += input_text.strip()

            # --- Split text into manageable chunks ---
            text_chunks = split_text_into_chunks(raw_text.strip(), tokenizer)

            # --- Process and Display Results ---
            process_and_display(text_chunks, "Combined Input", ai_detection_model, tokenizer, plagiarism_model)

# --- Helper function to process text and display results ---
def process_and_display(text_chunks, source_name, ai_detection_model, tokenizer, plagiarism_model):
    # AI Detection
    ai_percentage_avg = None
    human_percentage = None
    if ai_detection_model:
        ai_percentages = detect_ai_content(text_chunks, ai_detection_model)
        if ai_percentages:
            ai_percentage_avg = sum(ai_percentages) / len(ai_percentages) * 100
            human_percentage = 100 - ai_percentage_avg

    # Plagiarism Check
    plagiarism_percentage = None
    if tokenizer and plagiarism_model:
        plagiarism_percentage = plagiarism_check(text_chunks, tokenizer, plagiarism_model)

    # --- Tiled Output ---
    with st.container():
        st.markdown(f"<div class='output-box'><h3>{source_name}</h3></div>", unsafe_allow_html=True)

        col1, col2 = st.columns(2)

        with col1:
            st.markdown("<div class='output-box'><h4>AI Detection:</h4></div>", unsafe_allow_html=True)
            if ai_percentage_avg is not None:
                st.metric(label="AI Content", value=f"{ai_percentage_avg:.2f}%", delta="AI Generated")
                st.metric(label="Human Written", value=f"{human_percentage:.2f}%", delta="Humanized Text")
            else:
                st.write("AI Detection not available")

        with col2:
            st.markdown("<div class='output-box'><h4>Plagiarism Detection:</h4></div>", unsafe_allow_html=True)
            if plagiarism_percentage is not None:
                st.metric(label="Plagiarism", value=f"{plagiarism_percentage:.2f}%", delta="Plagiarized" if plagiarism_percentage > 0 else "Original")
            else:
                st.write("Plagiarism Detection not available")

# --- Load models globally ---
@st.cache_resource
def load_models():
    ai_detection_model = load_ai_detection_model()
    tokenizer, plagiarism_model = load_plagiarism_model()
    return ai_detection_model, tokenizer, plagiarism_model

# --- Call Main ---
if __name__ == "__main__":
    ai_detection_model, tokenizer, plagiarism_model = load_models()
    main()