import gradio as gr | |
from PIL import Image | |
# import pickle | |
import json | |
import numpy as np | |
# from fastapi import FastAPI,Response | |
# from sklearn.metrics import accuracy_score, f1_score | |
# import prometheus_client as prom | |
import pandas as pd | |
# import uvicorn | |
import os | |
from transformers import VisionEncoderDecoderModel,pipeline, ViTImageProcessor, AutoTokenizer | |
import torch | |
#model | |
# loaded_model = pickle.load(open(save_file_name, 'rb')) | |
# app=FastAPI() | |
# test_data=pd.read_csv("test.csv") | |
# f1_metric = prom.Gauge('death_f1_score', 'F1 score for test samples') | |
# Function for updating metrics | |
# def update_metrics(): | |
# test = test_data.sample(20) | |
# X = test.iloc[:, :-1].values | |
# y = test['DEATH_EVENT'].values | |
# # test_text = test['Text'].values | |
# test_pred = loaded_model.predict(X) | |
# #pred_labels = [int(pred['label'].split("_")[1]) for pred in test_pred] | |
# f1 = f1_score( y , test_pred).round(3) | |
# #f1 = f1_score(test['labels'], pred_labels).round(3) | |
# f1_metric.set(f1) | |
with open("model/config.json") as f: | |
n=json.load(f) | |
encoder_name_or_path=n["encoder"]["_name_or_path"] | |
decoder_name_or_path=n["decoder"]["_name_or_path"] | |
print(encoder_name_or_path,decoder_name_or_path,) | |
model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(encoder_name_or_path,decoder_name_or_path) | |
tokenizer = AutoTokenizer.from_pretrained(decoder_name_or_path) | |
tokenizer.pad_token = tokenizer.unk_token | |
feature_extractor = ViTImageProcessor.from_pretrained(encoder_name_or_path) | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
# cap_model.to(device) | |
# def generate_caption(model, image, tokenizer=None): | |
# generated_ids = model.generate(pixel_values=inputs.pixel_values) | |
# print("generated_ids",generated_ids) | |
# if tokenizer is not None: | |
# print("tokenizer not null--",tokenizer) | |
# generated_caption = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
# else: | |
# print("tokenizer null--",tokenizer) | |
# generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
# return generated_caption | |
def predict_event(image): | |
generated_caption = tokenizer.decode(model.generate(feature_extractor(image, return_tensors="pt").pixel_values.to(device))[0]) | |
return '\033[96m' +generated_caption+ '\033[0m' | |
# @app.get("/metrics") | |
# async def get_metrics(): | |
# update_metrics() | |
# return Response(media_type="text/plain", content= prom.generate_latest()) | |
title = "capstone" | |
description = "final capstone" | |
# inputs=gr.inputs.Image(type="pil") | |
iface = gr.Interface(predict_event, | |
inputs=["image"], | |
# gr.Image(type="pil"), | |
outputs=["text"] ) | |
iface.launch() | |
# app = gr.mount_gradio_app(app, iface, path="/") | |
# iface.launch(server_name = "0.0.0.0", server_port = 8001,share=True) | |
# if __name__ == "__main__": | |
# Use this for debugging purposes only | |
# uvicorn.run(app, host="0.0.0.0", port=8001) |