import streamlit as st import requests import csv import datetime import time import pandas as pd import matplotlib.pyplot as plt import os ########################## SECRET = os.environ["api_secret"] headers = {"Authorization": "Bearer " + SECRET} API_URL = "https://api-inference.huggingface.co/models/cccmatthew/surrey-gp30" ########################## def load_response_times(): try: df = pd.read_csv('model_interactions.csv', usecols=["Timestamp", "Response Time"]) df['Timestamp'] = pd.to_datetime(df['Timestamp']) return df except Exception as e: st.error(f"Failed to read response times: {e}") return pd.DataFrame() def plot_response_times(df): if not df.empty: plt.figure(figsize=(10, 5)) plt.plot(df['Timestamp'], df['Response Time'], marker='o', linestyle='-') plt.title('Response Times Over Time') plt.xlabel('Timestamp') plt.ylabel('Response Time (seconds)') plt.grid(True) st.pyplot(plt) else: st.write("No response time data to display.") #Function to setup the logs ina csv file def setup_csv_logger(): with open('model_interactions.csv', 'a', newline='') as file: writer = csv.writer(file) #The headers will be written if not present if file.tell() == 0: writer.writerow(["Timestamp", "User Input", "Model Prediction", "Response Time"]) def log_to_csv(sentence, results, response_time): with open('model_interactions.csv', 'a', newline='') as file: writer = csv.writer(file) writer.writerow([datetime.datetime.now(), sentence, results, response_time]) setup_csv_logger() st.title('Group 30 - DistilBERT') st.write('This application uses DistilBERT to classify Abbreviations (AC) and Long Forms (LF)') example_sentences = [ "RAFs are plotted for a selection of neurons in the dorsal zone (DZ) of auditory cortex in Figure 1.", "Light dissolved inorganic carbon (DIC) resulting from the oxidation of hydrocarbons.", "Images were acquired using a GE 3.0T MRI scanner with an upgrade for echo-planar imaging (EPI)." ] sentence = st.selectbox('Choose an example sentence or type your own below:', example_sentences + ['Custom Input...']) if sentence == 'Custom Input...': sentence = st.text_input('Input your sentence here', '') def merge_entities(sentence, entities): entities = sorted(entities, key=lambda x: x['start']) annotated_sentence = "" last_end = 0 for entity in entities: annotated_sentence += sentence[last_end:entity['start']] annotated_sentence += f"{sentence[entity['start']:entity['end']]} [{entity['entity_group']}]" last_end = entity['end'] annotated_sentence += sentence[last_end:] return annotated_sentence def send_request_with_retry(url, headers, json_data, retries=3, backoff_factor=1): """Send request with retries on timeouts and HTTP 503 errors.""" for attempt in range(retries): start_time = time.time() try: response = requests.post(url, headers=headers, json=json_data) response.raise_for_status() response_time = time.time() - start_time return response, response_time except requests.exceptions.HTTPError as e: if response.status_code == 503: st.info('Server is unavailable, retrying...') else: raise except (requests.exceptions.ConnectionError, requests.exceptions.Timeout) as e: st.info(f"Network issue ({str(e)}), retrying...") time.sleep(backoff_factor * (2 ** attempt)) st.error("Failed to process request after several attempts.") return None, None if st.button('Classify'): if sentence: API_URL = API_URL headers = headers response, response_time = send_request_with_retry(API_URL, headers, {"inputs": sentence}) if response is not None: results = response.json() st.write('Results:') annotated_sentence = merge_entities(sentence, results) st.markdown(annotated_sentence, unsafe_allow_html=True) log_to_csv(sentence, results, response_time) df = load_response_times() plot_response_times(df) else: st.error("Unable to classify the sentence due to server issues.") else: st.error('Please enter a sentence.') #Separate button to just plot the response time if st.button('Show Response Times'): df = load_response_times() plot_response_times(df)