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app.py ADDED
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+ # -*- coding: utf-8 -*-
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+ """
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+ Created on Fri Apr 19 16:58:55 2024
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+
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+ @author: phonoDS
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+ """
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+
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+ import streamlit as st
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
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+ import tensorflow as tf
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+ tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
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+
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+ # Load pre-trained model and tokenizer
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+ saved_directory = "C:/Users/phonoDS/FineTuned_Roberta_Sentiment_Analysis"
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+ tokenizer = AutoTokenizer.from_pretrained(saved_directory)
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+ model = AutoModelForSequenceClassification.from_pretrained(saved_directory)
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+ nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
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+
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+ # Define function to analyze sentiment
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+ def analyze_sentiment(text):
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+ result = nlp(text)
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+ return result[0]['label'], result[0]['score']
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+
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+ # Streamlit UI
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+ st.set_page_config(page_title="Sentiment Analysis", layout="wide")
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+ col1, col2 = st.columns([6, 1]) # Divide the screen into two columns
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+
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+ with col2: # Right-aligned column for the logo
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+ st.image("C:/Users/phonoDS/Downloads/oryx_logo.png", width=200, use_column_width=False) # Provide the path to your company logo
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+
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+ with col1: # Main content area
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+ st.title("Sentiment Analysis Prototype Tool by orYx Models")
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+ user_input = st.text_area("Enter text to analyze:", height=200)
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+
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+ submit_button = st.button("Analyze:")
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+
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+ if submit_button and user_input:
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+ sentiment, score = analyze_sentiment(user_input)
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+ st.markdown("Sentiment Analysis Result:")
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+ st.write(f"Sentiment: {sentiment}")
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+ st.write(f"Confidence: {score*100:.2f}%")
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+
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+ # Text titles below the text box
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+ st.markdown("This sentiment analysis model serves as a testing prototype, specifically developed for LDS to assess the variability and precision of OrYx Models' sentiment analysis tool.")
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+ st.markdown("All feedback gathered from LDS, including both structured and unstructured data, will be incorporated into the model to enhance its domain specificity and maximize accuracy.")
config.json ADDED
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+ {
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+ "_name_or_path": "cardiffnlp/twitter-roberta-base-sentiment-latest",
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+ "architectures": [
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+ "RobertaForSequenceClassification"
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+ ],
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "Negative",
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+ "1": "Neutral",
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+ "2": "Positive"
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+ "problem_type": "single_label_classification",
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+ }
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tokenizer.json ADDED
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vocab.json ADDED
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