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from fastapi import FastAPI, Request, Response
import gradio
from transformers import pipeline
from sklearn.metrics import f1_score, precision_score, recall_score
import prometheus_client as prom
app = FastAPI()
username = "yrajm1997"
repo_name = "finetuned-sentiment-model"
repo_path = username+ '/' + repo_name
sentiment_model = pipeline(model= repo_path)
import pandas as pd
test_data = pd.read_csv("test_reviews.csv")
f1_metric = prom.Gauge('sentiment_f1_score', 'F1 score for random 100 test samples')
precision_metric = prom.Gauge('sentiment_precision_score', 'Precision score for random 100 test samples')
recall_metric = prom.Gauge('sentiment_recall_score', 'Recall score for random 100 test samples')
# Function for response generation
def predict_sentiment(text):
result = sentiment_model(text)
if result[0]['label'].endswith('0'):
return 'Negative'
else:
return 'Positive'
# Function for updating metrics
def update_metrics():
test = test_data.sample(100)
test_text = test['Text'].values
test_pred = sentiment_model(list(test_text))
pred_labels = [int(pred['label'].split("_")[1]) for pred in test_pred]
f1 = f1_score(test['labels'], pred_labels).round(3)
precision = precision_score(test['labels'], pred_labels).round(3)
recall = recall_score(test['labels'], pred_labels).round(3)
f1_metric.set(f1)
precision_metric.set(precision)
recall_metric.set(recall)
@app.get("/metrics")
async def get_metrics():
update_metrics()
return Response(media_type="text/plain", content= prom.generate_latest())
# Input from user
in_prompt = gradio.components.Textbox(lines=10, placeholder=None, label='Enter review text')
# Output response
out_response = gradio.components.Textbox(type="text", label='Sentiment')
# Gradio interface to generate UI link
title = "Sentiment Classification"
description = "Analyse sentiment of the given review"
iface = gradio.Interface(fn = predict_sentiment,
inputs = [in_prompt],
outputs = [out_response],
title = title,
description = description)
app = gradio.mount_gradio_app(app, iface, path="/")
#iface.launch(server_name = "0.0.0.0", server_port = 8001) # Ref. for parameters: https://www.gradio.app/docs/interface
if __name__ == "__main__":
# Use this for debugging purposes only
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8001) |