init

README.md

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+---
+title: Name Entity Recognition (Group 30)
+emoji: 📹
+colorFrom: blue
+colorTo: red
+sdk: streamlit
+app_file: app.py
+pinned: false
+---
+
+# image2textapp
+Group 30 Demo - Name Entity Recognition 

app.py

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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
+
+##########################
+headers = {"Authorization": "Bearer "}
+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=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)

requirements.txt

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+streamlit
+pandas