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| import functools | |
| import streamlit as st | |
| from streamlit_option_menu import option_menu | |
| import streamlit.components.v1 as html | |
| import pandas as pd | |
| import numpy as np | |
| from pathlib import Path | |
| import altair as alt | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| from transformers import RobertaConfig | |
| from transformers import RobertaForSequenceClassification, RobertaTokenizer, RobertaConfig | |
| import torch | |
| # from torch import cuda | |
| import gradio as gr | |
| import os | |
| import re | |
| import torch, gc | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| # device | |
| tokenizer = AutoTokenizer.from_pretrained("devloverumar/chatgpt-content-detector") | |
| model = AutoModelForSequenceClassification.from_pretrained("devloverumar/chatgpt-content-detector", num_labels=2) | |
| # from PIL import Image | |
| # gc.collect() | |
| # torch.cuda.empty_cache() | |
| def text_to_sentences(text): | |
| clean_text = text.replace('\n', ' ') | |
| return re.split(r'(?<=[^A-Z].[.?]) +(?=[A-Z])', clean_text) | |
| # function to concatenate sentences into chunks of size 900 or less | |
| def chunks_of_900(text, chunk_size = 900): | |
| sentences = text_to_sentences(text) | |
| chunks = [] | |
| current_chunk = "" | |
| for sentence in sentences: | |
| if len(current_chunk + sentence) <= chunk_size: | |
| if len(current_chunk)!=0: | |
| current_chunk += " "+sentence | |
| else: | |
| current_chunk += sentence | |
| else: | |
| chunks.append(current_chunk) | |
| current_chunk = sentence | |
| chunks.append(current_chunk) | |
| return chunks | |
| def predict(query): | |
| tokens = tokenizer.encode(query) | |
| all_tokens = len(tokens) | |
| tokens = tokens[:tokenizer.model_max_length - 2] | |
| used_tokens = len(tokens) | |
| tokens = torch.tensor([tokenizer.bos_token_id] + tokens + [tokenizer.eos_token_id]).unsqueeze(0) | |
| mask = torch.ones_like(tokens) | |
| with torch.no_grad(): | |
| logits = model(tokens.to(device), attention_mask=mask.to(device))[0] | |
| probs = logits.softmax(dim=-1) | |
| real, fake = probs.detach().cpu().flatten().numpy().tolist() # Hello-SimpleAI/chatgpt-detector-roberta | |
| # fake, real = probs.detach().cpu().flatten().numpy().tolist() # PirateXX/AI-Content-Detector-V2 | |
| return real | |
| def findRealProb(text): | |
| chunksOfText = (chunks_of_900(text)) | |
| results = [] | |
| for chunk in chunksOfText: | |
| output = predict(chunk) | |
| results.append([output, len(chunk)]) | |
| ans = 0 | |
| cnt = 0 | |
| for prob, length in results: | |
| cnt += length | |
| ans = ans + prob*length | |
| realProb = ans/cnt | |
| return {"Real": realProb, "Fake": 1-realProb}, results | |
| TXT_TO_INSPECT=None | |
| def inspect_content(text): | |
| TXT_TO_INSPECT=text | |
| st.markdown(""" <style> .appview-container .main .block-container { | |
| max-width: 100%; | |
| padding-top: 1rem; | |
| padding-right: {1}rem; | |
| padding-left: {1}rem; | |
| padding-bottom: {1}rem; | |
| }</style> """, unsafe_allow_html=True) | |
| #Add a logo (optional) in the sidebar | |
| # logo = Image.open(r'C:\Users\13525\Desktop\Insights_Bees_logo.png') | |
| with st.sidebar: | |
| choose = option_menu("Forensic Examiner", ["Inspect Content","Generate Content","About", "Contact"], | |
| icons=['camera fill', 'kanban', 'book','person lines fill'], | |
| menu_icon="app-indicator", default_index=0, | |
| styles={ | |
| "container": {"padding": "0 5 5 5 !important", "background-color": "#fafafa"}, | |
| "icon": {"color": "orange", "font-size": "25px"}, | |
| "nav-link": {"font-size": "16px", "text-align": "left", "margin":"0px", "--hover-color": "#eee"}, | |
| "nav-link-selected": {"background-color": "#02ab21"}, | |
| } | |
| ) | |
| if choose == "Inspect Content": | |
| #Add the cover image for the cover page. Used a little trick to center the image | |
| st.markdown(""" <style> .font { | |
| font-size:25px ; font-family: 'Cooper Black'; color: #FF9633;} | |
| </style> """, unsafe_allow_html=True) | |
| col1, col2 = st.columns( [0.8, 0.2]) | |
| with col1: # To display the header text using css style | |
| st.markdown('<p class="font">Inspect Content</p>', unsafe_allow_html=True) | |
| with col2: # To display brand logo | |
| st.image('./media/inspection-1.jpg', width=100 ) | |
| txt = st.text_area('Add Text here',height=300, max_chars=2000, value= ''' | |
| Cristiano Ronaldo is a Portuguese professional soccer player who currently plays | |
| as a forward for Manchester United and the Portugal national team. He is widely | |
| considered one of the greatest soccer players of all time, having won numerous | |
| awards and accolades throughout his career. Ronaldo began his professional career | |
| with Sporting CP in Portugal before moving to Manchester United in 2003. | |
| He spent six seasons with the club, winning three Premier League titles | |
| and one UEFA Champions League title. In 2009, he transferred to Real Madrid | |
| for a then-world record transfer fee of $131 million. He spent nine seasons with | |
| the club, winning four UEFA Champions League titles, two La Liga titles, | |
| and two Copa del Rey titles. In 2018, he transferred to Juventus, where he spent | |
| three seasons before returning to Manchester United in 2021. He has also had | |
| a successful international career with the Portugal national team, having won | |
| the UEFA European Championship in 2016 and the UEFA Nations League in 2019. | |
| ''', on_change=inspect_content) | |
| if TXT_TO_INSPECT is not None: | |
| with st.spinner('Loading the model..'): | |
| model.to(device) | |
| st.success(f'Model Loaded!', icon="✅") | |
| # st.success(f'Reported EER for the selected model {reported_eer}%') | |
| with st.spinner("Getting prediction..."): | |
| # print(audio.shape) | |
| predictions=findRealProb(txt) | |
| print('prediction_value',predictions) | |
| if predictions[0]['Fake'] > 0.5: | |
| # st.error(f"The Sample is spoof: \n Confidence {(prediction_value) }%", icon="🚨") | |
| st.error(f"This text is AI generated", icon="🚨") | |
| else: | |
| st.success(f"This text is real", icon="✅") | |
| # if choose == "Generate Content": | |
| # st.markdown(""" <style> .font { | |
| # font-size:25px ; font-family: 'Cooper Black'; color: #FF9633;} | |
| # </style> """, unsafe_allow_html=True) | |
| # st.markdown('<p class="font">Comparison of Models</p>', unsafe_allow_html=True) | |
| # data_frame = get_data() | |
| # tab1, tab2 = st.tabs(["EER", "min-TDCF"]) | |
| # with tab1: | |
| # data_frame["EER ASVS 2019"] = data_frame["EER ASVS 2019"].astype('float64') | |
| # data_frame["EER ASVS 2021"] = data_frame["EER ASVS 2021"].astype('float64') | |
| # data_frame["Cross-dataset 19-21"] = data_frame["Cross-dataset 19-21"].astype('float64') | |
| # data = data_frame[["Model Name","EER ASVS 2019","EER ASVS 2021","Cross-dataset 19-21"]].reset_index(drop=True).melt('Model Name') | |
| # chart=alt.Chart(data).mark_line().encode( | |
| # x='Model Name', | |
| # y='value', | |
| # color='variable' | |
| # ) | |
| # st.altair_chart(chart, theme=None, use_container_width=True) | |
| # with tab2: | |
| # data_frame["min-TDCF ASVS 2019"] = data_frame["EER ASVS 2019"].astype('float64') | |
| # data_frame["min-TDCF ASVS 2021"] = data_frame["EER ASVS 2021"].astype('float64') | |
| # data_frame["min-TDCF Cross-dataset"] = data_frame["Cross-dataset 19-21"].astype('float64') | |
| # data = data_frame[["Model Name","min-TDCF ASVS 2019","min-TDCF ASVS 2021","min-TDCF Cross-dataset"]].reset_index(drop=True).melt('Model Name') | |
| # chart=alt.Chart(data).mark_line().encode( | |
| # x='Model Name', | |
| # y='value', | |
| # color='variable' | |
| # ) | |
| # st.altair_chart(chart, theme=None, use_container_width=True) | |
| # # Data table | |
| # st.markdown(""" <style> .appview-container .main .block-container { | |
| # max-width: 100%; | |
| # padding-top: {1}rem; | |
| # padding-right: {1}rem; | |
| # padding-left: {1}rem; | |
| # padding-bottom: {1}rem; | |
| # }</style> """, unsafe_allow_html=True) | |
| # st.dataframe(data_frame, use_container_width=True) | |
| if choose == "About": | |
| st.markdown(""" <style> .font { | |
| font-size:35px ; font-family: 'Cooper Black'; color: #FF9633;} | |
| </style> """, unsafe_allow_html=True) | |
| st.markdown('<p class="font">About</p>', unsafe_allow_html=True) | |
| if choose == "Contact": | |
| st.markdown(""" <style> .font { | |
| font-size:35px ; font-family: 'Cooper Black'; color: #FF9633;} | |
| </style> """, unsafe_allow_html=True) | |
| st.markdown('<p class="font">Contact Us</p>', unsafe_allow_html=True) | |
| with st.form(key='columns_in_form2',clear_on_submit=True): #set clear_on_submit=True so that the form will be reset/cleared once it's submitted | |
| #st.write('Please help us improve!') | |
| Name=st.text_input(label='Please Enter Your Name') #Collect user feedback | |
| Email=st.text_input(label='Please Enter Your Email') #Collect user feedback | |
| Message=st.text_input(label='Please Enter Your Message') #Collect user feedback | |
| submitted = st.form_submit_button('Submit') | |
| if submitted: | |
| st.write('Thanks for your contacting us. We will respond to your questions or inquiries as soon as possible!') | |