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| from __future__ import annotations | |
| import json | |
| import sys | |
| from pathlib import Path | |
| from typing import Dict | |
| import pandas as pd | |
| import streamlit as st | |
| from huggingface_hub import hf_hub_download | |
| import joblib | |
| # ------------------------------------------------------------------------- | |
| # Page Configuration | |
| # ------------------------------------------------------------------------- | |
| st.set_page_config( | |
| page_title="Twitter Sentiment Intelligence", | |
| page_icon="💼", | |
| layout="wide", | |
| ) | |
| st.title("Twitter Sentiment Intelligence") | |
| st.caption("Streamlit front-end for the Deloitte-ready Twitter Sentiment Intelligence dashboard.") | |
| try: | |
| # ------------------------------------------------------------------------- | |
| # Download model from Hugging Face Hub (no local artifacts needed) | |
| # ------------------------------------------------------------------------- | |
| MODEL_REPO = "vishnu-coder/twitter-sentiment-model" | |
| MODEL_FILENAME = "sentiment_pipeline.joblib" | |
| def load_model(): | |
| """Download and load the trained model from Hugging Face Hub.""" | |
| model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILENAME) | |
| pipeline = joblib.load(model_path) | |
| return pipeline | |
| pipeline = load_model() | |
| # ------------------------------------------------------------------------- | |
| # Helper function for predictions | |
| # ------------------------------------------------------------------------- | |
| def predict_sentiment(text: str) -> tuple[str, Dict[str, float]]: | |
| """Predict sentiment and confidence scores.""" | |
| probs = pipeline.predict_proba([text])[0] | |
| classes = pipeline.classes_ | |
| label = classes[probs.argmax()] | |
| probabilities = dict(zip(classes, probs)) | |
| return label, probabilities | |
| def format_probabilities(probabilities: Dict[str, float]) -> pd.DataFrame: | |
| """Convert probabilities to styled DataFrame.""" | |
| return ( | |
| pd.DataFrame.from_dict(probabilities, orient="index", columns=["confidence"]) | |
| .sort_values("confidence", ascending=False) | |
| .style.format({"confidence": "{:.2%}"}) | |
| ) | |
| # ------------------------------------------------------------------------- | |
| # Streamlit UI | |
| # ------------------------------------------------------------------------- | |
| def main(): | |
| st.sidebar.header("📊 Model Snapshot") | |
| st.sidebar.write("**Source:**", MODEL_REPO) | |
| st.sidebar.success("✅ Loaded model from Hugging Face Hub") | |
| st.subheader("🔮 Real-Time Sentiment Analysis") | |
| user_input = st.text_area("Enter a tweet or comment:", height=150) | |
| if st.button("Analyze", type="primary"): | |
| if not user_input.strip(): | |
| st.warning("⚠️ Please enter text to analyze.") | |
| else: | |
| label, probabilities = predict_sentiment(user_input) | |
| st.success(f"Predicted Sentiment: **{label.title()}**") | |
| st.dataframe(format_probabilities(probabilities), use_container_width=True) | |
| st.markdown("---") | |
| st.caption("© 2025 Deloitte-aligned Sentiment Analytics Accelerator") | |
| if __name__ == "__main__": | |
| main() | |
| except Exception as e: | |
| st.error(f"Startup failed: {e}") |