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import streamlit as st
from transformers import pipeline, AutoTokenizer
from PyPDF2 import PdfReader
import docx
import plotly.graph_objects as go
# Page configuration
st.set_page_config(layout="wide")
st.title("📄 AI Content Analyzer")
st.markdown("Upload PDF/Word files to detect AI-generated content")
# Constants
MAX_WORDS = 1000 # Maximum words to analyze
WARNING_THRESHOLD = 1200 # Warning threshold for large files
# Load AI detection model
@st.cache_resource
def load_model():
model_name = "roberta-base-openai-detector"
tokenizer = AutoTokenizer.from_pretrained(model_name)
return pipeline("text-classification", model=model_name, tokenizer=tokenizer)
detector = load_model()
def count_words(text):
return len(text.split())
def create_gauge(score):
fig = go.Figure(go.Indicator(
mode = "gauge+number",
value = score,
domain = {'x': [0, 1], 'y': [0, 1]},
title = {'text': "AI Content Probability", 'font': {'size': 20}},
gauge = {
'axis': {'range': [None, 100], 'tickwidth': 1},
'bar': {'color': "darkblue"},
'steps': [
{'range': [0, 50], 'color': 'green'},
{'range': [50, 75], 'color': 'yellow'},
{'range': [75, 100], 'color': 'red'}]
}))
st.plotly_chart(fig, use_container_width=True)
# File uploader
uploaded_file = st.file_uploader("Upload file (PDF or Word)", type=["pdf", "docx"])
if uploaded_file:
# Extract text
text = ""
if uploaded_file.name.endswith(".pdf"):
reader = PdfReader(uploaded_file)
text = " ".join([page.extract_text() or "" for page in reader.pages])
else:
doc = docx.Document(uploaded_file)
text = " ".join([para.text for para in doc.paragraphs])
word_count = count_words(text)
# Word limit warning
if word_count > WARNING_THRESHOLD:
st.warning(f"⚠️ File contains {word_count} words (Analyzing first {MAX_WORDS} words only)")
if st.button("Analyze Content"):
if word_count < 50:
st.error("❌ Insufficient text for analysis (minimum 50 words required)")
else:
# Process first 1000 words
processed_text = " ".join(text.split()[:MAX_WORDS])
# Perform analysis
result = detector(processed_text)
ai_prob = result[0]['score']*100 if result[0]['label']=='FAKE' else 100-result[0]['score']*100
# Display results
st.subheader("Analysis Results")
create_gauge(ai_prob)
col1, col2 = st.columns(2)
with col1:
st.metric("Words Analyzed", f"{min(word_count, MAX_WORDS)}/{word_count}")
with col2:
st.metric("AI Probability", f"{ai_prob:.1f}%")
with st.expander("View Text Sample"):
st.text(processed_text[:1000] + ("..." if word_count>1000 else "")) |