Delete analyze.py

analyze.py

@@ -1,94 +0,0 @@
-import streamlit as st
-import pandas as pd
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import emoji
-
-model_path = "ANLPRL/TBModel"
-tokenizer_path = "ANLPRL/TBTokenizer"
-
-# Load the tokenizer and model
-model = AutoModelForSequenceClassification.from_pretrained(model_path)
-tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
-
-def predict(text):
-    encoded_data = tokenizer.encode_plus(text, padding=True, truncation=True, return_tensors='pt')
-    input_ids = encoded_data['input_ids']
-    attention_mask = encoded_data['attention_mask']
-    with torch.no_grad():
-        outputs = model(input_ids, attention_mask)
-    logits = outputs.logits
-    probabilities = torch.softmax(logits, dim=1)
-    _, predicted = torch.max(probabilities, dim=1)
-    
-    # Create dictionary to map numerical labels to categories
-    label_dict = {0: 'Positive', 1: 'Negative', 2: 'Neutral'}
-    predicted_label = label_dict[predicted.item()]
-    
-    return predicted_label
-
-# Define examples as a list
-examples = [
-    "ChatGPT Plus uses cutting-edge AI technology to learn from customer conversations.",
-    "ChatGPT can produce harmful and biased answers.",
-    "Gpt dont have feelings or a personal identity, but it strive to provide informative responses.",
-]
-
-# Create the Streamlit app
-emoji_dict = {
-    "positive": "\U0001F60A",
-    "negative": "\U0001F61E",
-    "neutral": "\U0001F610"
-}
-
-st.title("CHAT-GPT SENTIMENT ANALYSIS")
-
-# Create the form to handle user inputs
-with st.form("sentiment_analysis_form"):
-    # Add the dropdown list for examples
-    selected_option = st.selectbox("Select an example to analyze", [""] + examples, index=0)
-
-    # Add the text input for user input
-    user_input = st.text_input("Enter your own text to analyze", "")
-
-    # Define color codes for different sentiment classes
-    positive_color = "#00C851"
-    negative_color = "#ff4444"
-    neutral_color = "#FFBB33"
-
-    # Add the submit button to analyze the sentiment
-    analyze_button = st.form_submit_button("Analyze")
-
-# Handle the form submission
-if analyze_button:
-    if user_input.strip() != "":
-        prediction = predict(user_input.strip())
-        if prediction == 'Positive':
-            st.write(f"<span style='color:{positive_color}; font-weight:bold;'>{emoji_dict['positive']} Positive</span>", unsafe_allow_html=True)
-        elif prediction == 'Negative':
-            st.write(f"<span style='color:{negative_color}; font-weight:bold;'>{emoji_dict['negative']} Negative</span>", unsafe_allow_html=True)
-        else:
-            st.write(f"<span style='color:{neutral_color}; font-weight:bold;'>{emoji_dict['neutral']} Neutral</span>", unsafe_allow_html=True)
-    elif selected_option != "":
-        prediction = predict(selected_option)
-        if prediction == 'Positive':
-            st.write(f"<span style='color:{positive_color}; font-weight:bold;'>{emoji_dict['positive']} Positive</span>", unsafe_allow_html=True)
-        elif prediction == 'Negative':
-            st.write(f"<span style='color:{negative_color}; font-weight:bold;'>{emoji_dict['negative']} Negative</span>", unsafe_allow_html=True)
-        else:
-            st.write(f"<span style='color:{neutral_color}; font-weight:bold;'>{emoji_dict['neutral']} Neutral</span>", unsafe_allow_html=True)
-    else:
-        st.write("Please enter a text or select an example to predict")
-
-
-st.markdown("""---""")
-st.caption("""
-    Developed by Applied NLP Research Lab
-School of Digital Sciences,
-Kerala University of Digital Sciences, Innovation and Technology,
-Technopark phase 4, Thiruvananthapuram, India | 
-Email: [email protected]   
-    <span style='text-align:center; display:block;'>
-        https://sites.google.com/duk.ac.in/anlprl
-    </span>
-""", unsafe_allow_html=True)