| import streamlit as st | |
| import requests | |
| BOW_API_ENDPOINT = "http://cps.bow.hifeyinc.com/predict" | |
| SEMANTIC_API_ENDPOINT = "http://cps.hifeyinc.com/predict" | |
| st.title("CPS UseCase Text Classification App") | |
| st.markdown( | |
| """ | |
| The app was trained to predict the type of headline a post is. | |
| Predictions are made by two models: a bag-of-words model and a semantic model. | |
| Examples of inputs you can provide are: | |
| - Authors: David | |
| - Headline: Find a nice summer vacation destination. | |
| """ | |
| ) | |
| author = st.text_input("Enter Author") | |
| headline = st.text_area("Enter Headline") | |
| if st.button("Predict"): | |
| if author and headline: | |
| bow_payload = { | |
| "data": [{"headline": headline, "authors": author}] | |
| } | |
| semantic_payload = { | |
| "data": [{"headline": headline, "authors": author}] | |
| } | |
| bow_response = requests.post(BOW_API_ENDPOINT, json=bow_payload) | |
| semantic_response = requests.post(SEMANTIC_API_ENDPOINT, json=semantic_payload) | |
| if bow_response.status_code == 200 and semantic_response.status_code == 200: | |
| bow_result = bow_response.json() | |
| semantic_result = semantic_response.json() | |
| bow_predictions = bow_result.get("predictions", []) | |
| semantic_predictions = semantic_result.get("predictions", []) | |
| if bow_predictions and semantic_predictions: | |
| st.success("Predictions:") | |
| prediction_data = { | |
| "Model": ["Bag of Words", "Semantic"], | |
| "Prediction": [bow_predictions[0], semantic_predictions[0]] | |
| } | |
| st.table(prediction_data) | |
| else: | |
| st.warning("No predictions available.") | |
| else: | |
| st.error("Error occurred while making predictions.") | |
| else: | |
| st.warning("Please enter both author and headline.") | |