Update app.py
Browse files
app.py
CHANGED
@@ -5,9 +5,11 @@ from newsfetch.news import newspaper
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from transformers import pipeline
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from transformers import T5Tokenizer, T5ForConditionalGeneration
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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from newspaper import Article
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from sklearn.preprocessing import LabelEncoder
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import joblib
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# Example usage:
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@@ -23,6 +25,11 @@ def main():
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try:
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news_article = newspaper(url)
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print("scraped: ",news_article)
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return news_article.article
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except Exception as e:
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return "Error: " + str(e)
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@@ -57,15 +64,35 @@ def main():
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return None,None
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else:
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st.write("This article is not classified as related to the supply chain.")
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-
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def classify_and_summarize(input_text, cls_model, tokenizer_cls, label_encoder, model_summ, tokenizer_summ, device):
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if input_text.startswith("http"):
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# If the input starts with "http", assume it's a URL and extract content
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article_content = scrape_news_content(input_text)
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else:
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# If the input is not a URL, assume it's the content
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article_content = input_text
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# Perform sentiment classification
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inputs_cls = tokenizer_cls(article_content, return_tensors="pt", max_length=512, truncation=True)
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inputs_cls = {key: value.to(device) for key, value in inputs_cls.items()}
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@@ -90,11 +117,13 @@ def main():
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print("No opportunity summary generated.")
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summary_opportunity = "No opportunity summary available" # Provide a default value or handle accordingly
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return classification, summary_risk, summary_opportunity
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print(url_input)
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cls_model =AutoModelForSequenceClassification.from_pretrained("riskclassification_finetuned_xlnet_model_ld")
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tokenizer_cls = AutoTokenizer.from_pretrained("xlnet-base-cased")
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label_encoder = LabelEncoder()
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@@ -118,7 +147,7 @@ def main():
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classification, summary_risk, summary_opportunity = classify_and_summarize(url_input, cls_model, tokenizer_cls, label_encoder, model_summ, tokenizer_summ, device)
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print("Classification:", classification)
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print("Risk Summary:", summary_risk)
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@@ -126,10 +155,45 @@ def main():
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# Display the entered URL
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st.write("Entered URL:", url_input)
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st.write("Classification:",classification)
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st.write("Risk Summary:",summary_risk)
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st.write("Opportunity Summary:",summary_opportunity)
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if __name__ == "__main__":
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main()
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from transformers import pipeline
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from transformers import T5Tokenizer, T5ForConditionalGeneration
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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from transformers import AutoTokenizer, AutoModelForQuestionAnswering
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from newspaper import Article
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from sklearn.preprocessing import LabelEncoder
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import joblib
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from datetime import datetime
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# Example usage:
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try:
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news_article = newspaper(url)
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print("scraped: ",news_article)
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print("Attributes of the newspaper object:", dir(news_article))
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# Print the methods of the newspaper object
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print("Methods of the newspaper object:", [method for method in dir(news_article) if callable(getattr(news_article, method))])
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# Try to print some specific attributes
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print("Authors:", news_article.authors)
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return news_article.article
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except Exception as e:
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return "Error: " + str(e)
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return None,None
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else:
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st.write("This article is not classified as related to the supply chain.")
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def classify_and_summarize(input_text, cls_model, tokenizer_cls, label_encoder, model_summ, tokenizer_summ, device):
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if input_text.startswith("http"):
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# If the input starts with "http", assume it's a URL and extract content
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article_content = scrape_news_content(input_text)
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st.write("Entered URL:", url_input)
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else:
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# If the input is not a URL, assume it's the content
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article_content = input_text
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# Get the number of lines in the text.
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truncated_content = " ".join(article_content.split()[:150])
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st.markdown(f"Truncated Content:\n{truncated_content}", unsafe_allow_html=True)
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# Add a button to toggle between truncated and full content
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if st.button("Read More"):
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# Display the full content when the button is clicked
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full_content = " ".join(article_content.split())
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st.markdown(f"Full Content:\n{full_content}", unsafe_allow_html=True)
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# Remove the truncated content when the full content is displayed
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st.markdown(
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"""
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<script>
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document.getElementById("truncated-content").style.display = "none";
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</script>
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""",
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unsafe_allow_html=True
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)
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# Perform sentiment classification
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inputs_cls = tokenizer_cls(article_content, return_tensors="pt", max_length=512, truncation=True)
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inputs_cls = {key: value.to(device) for key, value in inputs_cls.items()}
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print("No opportunity summary generated.")
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summary_opportunity = "No opportunity summary available" # Provide a default value or handle accordingly
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return classification, summary_risk, summary_opportunity,article_content
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print(url_input)
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cls_model =AutoModelForSequenceClassification.from_pretrained("riskclassification_finetuned_xlnet_model_ld")
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print(type(cls_model))
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tokenizer_cls = AutoTokenizer.from_pretrained("xlnet-base-cased")
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label_encoder = LabelEncoder()
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classification, summary_risk, summary_opportunity,article_content = classify_and_summarize(url_input, cls_model, tokenizer_cls, label_encoder, model_summ, tokenizer_summ, device)
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print("Classification:", classification)
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print("Risk Summary:", summary_risk)
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# Display the entered URL
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st.write("Classification:",classification)
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st.write("Risk Summary:",summary_risk)
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st.write("Opportunity Summary:",summary_opportunity)
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def process_question():
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# Use session_state to persist variables across sessions
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if 'qa_history' not in st.session_state:
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st.session_state.qa_history = []
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# Input box for user's question
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user_question_key = st.session_state.question_counter if 'question_counter' in st.session_state else 0
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user_question = st.text_input("Ask a question about the article content:", key=user_question_key)
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# Check if "Send" button is clicked
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send_button_key = f"send_button_{user_question_key}"
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if st.button("Send", key=send_button_key) and user_question:
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# Use a question-answering pipeline to generate a response
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model_name = "deepset/tinyroberta-squad2"
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nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
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QA_input = {'question': user_question, 'context': article_content}
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res = nlp(QA_input)
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# Display the user's question and the model's answer
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st.write(f"You asked: {user_question}")
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st.write("Model's Answer:", res["answer"])
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# Add the question and answer to the history
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st.session_state.qa_history.append((user_question, res["answer"]))
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# Clear the input box
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# Display the history
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st.write("Question-Answer History:")
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for q, a in st.session_state.qa_history:
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st.write(f"Q: {q}")
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st.write(f"A: {a}")
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# Run the function to process questions
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process_question()
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if __name__ == "__main__":
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main()
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