initial commit

app.py

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+import numpy as np
+import requests
+import streamlit as st
+import json
+def main():
+
+
+    
+    # Use feature-extraction API to get sentence embeddings.
+    api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
+    # Token to access Huggingface Inference API.
+    headers = {"Authorization": f"Bearer {st.secrets['HF_token']}"}
+
+
+    st.title("Semantic Search for Questions on Reddit.")
+
+    st.write("This application lets you perform sentiment analysis on book reviews.\
+        Simply input a review into the text below and the application will give two predictions for what the \
+             rating is on a scale of 0-5. The models will also produce the score they assigned their prediction. The score is\
+             between 0 and 1 and quantifies the confidence the model has in its prediction.\
+             \n\n \
+             Specifically, we consider two pre-trained models, [BERT-tiny](https://huggingface.co/dhmeltzer/bert-tiny-goodreads-wandb) and [DistilBERT](https://huggingface.co/dhmeltzer/distilbert-goodreads-wandb)\
+              which have been fine-tuned on a dataset of Goodreads book \
+             reviews, see [here](https://www.kaggle.com/competitions/goodreads-books-reviews-290312/data) for the original dataset. \
+             These models are deployed on AWS and are accessed using a REST API. To deploy the models we used a combination of AWS Sagemaker, Lambda, and API Gateway.\
+             \n\n \
+             To read more about this project and specifically how we cleaned the data and trained the models, see the following GitHub (repository)[https://github.com/david-meltzer/Goodreads-Sentiment-Analysis].")
+
+
+    AWS_key = st.secrets['AWS-key']
+
+    checkpoints = {}
+    checkpoints['DistilBERT'] = 'https://85a720iwy2.execute-api.us-east-1.amazonaws.com/add_apis/distilbert-goodreads'
+    checkpoints['BERT-tiny'] = 'https://055dugvmzl.execute-api.us-east-1.amazonaws.com/beta/'
+    
+    # User search with default question.
+    user_input = st.text_area("Search box", "I loved the Lord of the Rings trilogy. \
+                               It is a classic and beautifully written story and J.R.R. Tolkein really made Middle-Earth come to life. \
+                              My favorite part of the book though was when the hobbits met Tom Bombadil, it's too bad he was not in the movies.")
+
+
+    convert_dict = {}
+    for i in range(6):
+        convert_dict[f'LABEL_{i}'] = i
+
+    # Fetch results
+    if user_input:
+        # Get IDs for each search result. 
+        for model_name, URL in checkpoints.items():
+
+            headers={'x-api-key': AWS_key}
+
+            input_data = json.dumps({'inputs':user_input})
+            r = requests.post(URL,
+                  data=input_data,
+                  headers=headers).json()[0]
+ 
+            label, score = convert_dict[r['label']], r['score']
+
+            st.write(f"**Model Name**: {model_name}")
+            st.write(f"**Predicted Review**: {label}")
+            st.write(f"**Confidence**: {score}")
+            st.write("-"*20) 
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+folium
+streamlit==1.14.0
+altair<5

setup.py

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+from setuptools import setup
+
+common_kwargs = dict(
+    version="0.1.0",
+    license="MIT",
+    author="David Meltzer",
+    author_email="davidhmeltzer@gmail.com",
+    classifiers=[
+        "Intended Audience :: Developers",
+        "Intended Audience :: Science/Research",
+        "License :: OSI Approved :: MIT License",
+        "Natural Language :: English",
+        "Operating System :: OS Independent",
+        "Programming Language :: Python :: 3.9",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    ],
+    python_requires=">=3.9",
+    include_package_data=False,
+)
+
+setup(
+    name="sentiment_goodreads",
+    **common_kwargs
+)