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| from AICodeInit import * | |
| from peft import AutoPeftModelForCausalLM | |
| from transformers import AutoTokenizer, pipeline | |
| import gradio as gr | |
| from huggingface_hub import login | |
| import os | |
| # Huggingface Authentication | |
| api_key = os.getenv('cs549_naacp_access_key') | |
| if api_key is None: | |
| raise ValueError("API_KEY is not set in the environment variables.") | |
| login(api_key) | |
| # Load the model | |
| # model = AutoPeftModelForCausalLM.from_pretrained("Moritz-Pfeifer/financial-times-classification-llama-2-7b-v1.3") | |
| # tokenizer = AutoTokenizer.from_pretrained("Moritz-Pfeifer/financial-times-classification-llama-2-7b-v1.3") | |
| # def predict_text(test, model, tokenizer): | |
| # prompt = f""" | |
| # You are given a news article regarding the greater Boston area. | |
| # Analyze the sentiment of the article enclosed in square brackets, | |
| # determine if it is positive, negative or other, and return the answer as the corresponding sentiment label | |
| # "positive" or "negative". If the sentiment is neither positive or negative, return "other". | |
| # [{test}] =""" | |
| # pipe = pipeline(task="text-generation", | |
| # model=model, | |
| # tokenizer=tokenizer, | |
| # max_new_tokens = 1, | |
| # temperature = 0.1, | |
| # ) | |
| # result = pipe(prompt) | |
| # answer = result[0]['generated_text'].split("=")[-1] | |
| # # print(answer) | |
| # if "positive" in answer.lower(): | |
| # return "positive" | |
| # elif "negative" in answer.lower(): | |
| # return "negative" | |
| # else: | |
| # return "other" | |
| # def predict(input_text): | |
| # return predict_text(input_text, model, tokenizer) | |
| # interface = gr.Interface(fn=predict, inputs="text", outputs="text", title="Text Classifier", description="Insert your text and get the classification result.") | |
| # interface.launch() | |
| #initialize llama model model, tokenizer, prompt, pipe = create_pipeline() | |
| nlp = load_model() | |
| prompt, pipe = create_pipeline() | |
| #demo function calls predict text from other file | |
| def entity_sentiment(text): | |
| entities = set(extract_entities(text,nlp)) | |
| entities_context = extract_entities_with_context(text, nlp) | |
| #sentiment = get_sentiment(text) | |
| entity_sentiment_scoreonly=analyze_entity_sentiments_score(entities_context) | |
| #sentiment_category = categorize_sentiment(sentiment) | |
| #average_sentiment = sum(entity_sentiments)/len(entity_sentiments) | |
| average_score = calculate_avg_score(entity_sentiment_scoreonly) | |
| average_sentiment = categorize_sentiment(average_score) | |
| llama_sentiment = predict_text(text,pipe,prompt) | |
| return entities,entities_context,entity_sentiment_scoreonly,average_score,average_sentiment,llama_sentiment | |
| demo = gr.Interface( | |
| fn=entity_sentiment, | |
| inputs=["text"], | |
| outputs=[ | |
| gr.Textbox(label="Entities"), | |
| gr.Textbox(label="Entity Contexts"), | |
| gr.Textbox(label="Entity Sentiment Scores"), | |
| gr.Textbox(label="Average Score"), | |
| gr.Textbox(label="Average Sentiment"), | |
| gr.Textbox(label="Llama Sentiment") | |
| ]) | |
| # output_label=["Entities","Entity Contexts", "Entity Sentiment Scores","Average Score of Entites","Llama sentiment on Lede"] | |
| demo.launch(share=True) | |
| if __name__ == "__main__": | |
| interface.launch(share=True) |