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| 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"<mark style='background-color: #ffcccb;'><b>{sentence[entity['start']:entity['end']]}</b> [{entity['entity_group']}]</mark>" | |
| last_end = entity['end'] | |
| annotated_sentence += sentence[last_end:] | |
| return annotated_sentence | |
| def send_request_with_retry(url, headers, json_data, retries=5, backoff_factor=10): | |
| """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 Time'): | |
| df = load_response_times() | |
| plot_response_times(df) | |