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| # streamlit_app.py | |
| import streamlit as st | |
| from transformers import pipeline | |
| # Caching the text classification models | |
| def load_pipeline(model_name): | |
| return pipeline("text-classification", model=model_name) | |
| # Initialize session state for conversation history, bot response, and selected model | |
| if 'conversation_history' not in st.session_state: | |
| st.session_state.conversation_history = "" | |
| if 'bot_response' not in st.session_state: | |
| st.session_state.bot_response = "" | |
| if 'selected_model' not in st.session_state: | |
| st.session_state.selected_model = "distilbert/distilbert-base-uncased-finetuned-sst-2-english" | |
| def classify_text(user_message): | |
| # Update the conversation history | |
| st.session_state.conversation_history += f"User: {user_message}\n" | |
| pipe = load_pipeline(st.session_state.selected_model) | |
| result = pipe(user_message)[0] # pipe returns a list of results | |
| st.session_state.conversation_history += f"Bot: {result['label']} (Score: {result['score']:.2f})\n" | |
| st.session_state.bot_response = result | |
| return result | |
| # Sidebar options | |
| st.sidebar.title("App Settings") | |
| # Model selection | |
| model_options = { | |
| "DistilBERT Sentiment Analysis": "distilbert/distilbert-base-uncased-finetuned-sst-2-english", | |
| "BERT Multilingual Sentiment Analysis": "nlptown/bert-base-multilingual-uncased-sentiment" | |
| } | |
| selected_model = st.sidebar.selectbox("Select model:", list(model_options.keys())) | |
| st.session_state.selected_model = model_options[selected_model] | |
| show_history = st.sidebar.checkbox("Show conversation history", value=True) | |
| character_limit = st.sidebar.slider("Set character limit for input:", min_value=50, max_value=500, value=200) | |
| # Session reset button | |
| if st.sidebar.button("Reset Conversation"): | |
| st.session_state.conversation_history = "" | |
| st.session_state.bot_response = "" | |
| st.sidebar.success("Conversation history cleared.") | |
| # Streamlit app layout | |
| st.title("🧠 Text Classification Bot") | |
| st.subheader("Classify your text with a sentiment analysis model!") | |
| # Input field with character limit | |
| user_message = st.text_input(f"Enter your message (max {character_limit} characters):", max_chars=character_limit) | |
| # Send button to generate classification | |
| if st.button("Classify"): | |
| if user_message: | |
| # Get classification from the selected model | |
| classification_result = classify_text(user_message) | |
| # Display bot's response in a dedicated area | |
| st.markdown("### Classification Result") | |
| st.success(f"**Label:** {classification_result['label']}\n**Score:** {classification_result['score']:.2f}") | |
| if show_history: | |
| # Display conversation history in a text area for better scrolling | |
| st.write("### Conversation History") | |
| st.text_area("Conversation", value=st.session_state.conversation_history, height=250, max_chars=None) | |
| else: | |
| # Show a warning if no message is provided | |
| st.warning("Please enter a message before classifying.") | |
| # About section | |
| st.markdown("---") | |
| st.markdown("### About this App") | |
| st.info("This app uses pre-trained models for sentiment analysis. You can select a model and enter text to see its classification and sentiment score.") | |
| st.sidebar.markdown("---") | |
| st.sidebar.write("Created by [Your Name](https://github.com/yourprofile)") | |