Add application file

main.py

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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!')
+
+

requirements.txt

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+transformers
+flask
+torch
+gradio
+datasets
+evaluate
+scikit-learn
+scipy
+matplotlib
+accelerate
+nvidia-ml-py3