Create app.py

app.py

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+from transformers import FSMTForConditionalGeneration, FSMTTokenizer
+from transformers import AutoModelForSequenceClassification
+from lxml_html_clean import Cleaner
+
+
+from transformers import AutoTokenizer
+from langdetect import detect
+from newspaper import Article
+from PIL import Image
+import streamlit as st
+
+import requests
+import torch
+
+st.markdown("## Prediction of Misinformation by given URL")
+background = Image.open('logo.jpg')
+st.image(background)
+
+st.markdown(f"### Article URL")
+text = st.text_area("Insert some url here", 
+        value="https://www.livelaw.in/news-updates/supreme-court-collegium-recommends-appointment-advocate-praveen-kumar-giri-judge-allahabad-high-court-279470")
+
+# @st.cache(allow_output_mutation=True)
+# def get_models_and_tokenizers():
+#     model_name = 'distilbert-base-uncased-finetuned-sst-2-english'
+#     model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
+#     model.eval()
+#     tokenizer = AutoTokenizer.from_pretrained(model_name)
+#     model.load_state_dict(torch.load('./my_saved_model/checkpoint-6320/rng_state.pth', map_location='cpu'))
+
+#     model_name_translator = "facebook/wmt19-ru-en"
+#     tokenizer_translator = FSMTTokenizer.from_pretrained(model_name_translator)
+#     model_translator = FSMTForConditionalGeneration.from_pretrained(model_name_translator)
+#     model_translator.eval()
+#     return model, tokenizer, model_translator, tokenizer_translator
+@st.cache_data()
+def get_models_and_tokenizers():
+    model_name = 'distilbert-base-uncased-finetuned-sst-2-english'
+    checkpoint_dir = './my_saved_model/checkpoint-6320/'  # Path to your checkpoint folder
+    
+    # Load the classification model and tokenizer
+    model = AutoModelForSequenceClassification.from_pretrained(checkpoint_dir, num_labels=2)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    
+    # Load the translator model and tokenizer
+    model_name_translator = "facebook/wmt19-ru-en"
+    tokenizer_translator = FSMTTokenizer.from_pretrained(model_name_translator)
+    model_translator = FSMTForConditionalGeneration.from_pretrained(model_name_translator)
+    
+    model.eval()
+    model_translator.eval()
+    return model, tokenizer, model_translator, tokenizer_translator
+
+model, tokenizer, model_translator, tokenizer_translator = get_models_and_tokenizers()
+
+article = Article(text)
+article.download()
+article.parse()
+concated_text = article.title + '. ' + article.text
+lang = detect(concated_text)
+
+st.markdown(f"### Language detection")
+
+if lang == 'ru':
+    st.markdown(f"The language of this article is {lang.upper()} so we translated it!")
+    with st.spinner('Waiting for translation'):
+        input_ids = tokenizer_translator.encode(concated_text, 
+            return_tensors="pt", max_length=512, truncation=True)
+        outputs = model_translator.generate(input_ids)
+        decoded = tokenizer_translator.decode(outputs[0], skip_special_tokens=True)
+        st.markdown("### Translated Text")
+        st.markdown(f"{decoded[:777]}")
+        concated_text = decoded
+else:
+    st.markdown(f"The language of this article for sure:  {lang.upper()}!")
+
+    st.markdown("### Extracted Text")
+    st.markdown(f"{concated_text[:777]}")
+
+tokens_info = tokenizer(concated_text, truncation=True, return_tensors="pt")
+with torch.no_grad():
+    raw_predictions = model(**tokens_info)
+softmaxed = int(torch.nn.functional.softmax(raw_predictions.logits[0], dim=0)[1] * 100)
+st.markdown("### Truthteller Predicts..")
+st.progress(softmaxed)
+st.markdown(f"This is fake by *{softmaxed}%*!")
+if (softmaxed > 70):
+    st.error('We would not trust this text! This is misleading..')
+elif (softmaxed > 40):
+    st.warning('We are not sure about this text!')
+else:
+    st.success('We would trust this text!')