app.py

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("model")

# cap_model = VisionEncoderDecoderModel.from_pretrained("model")

# tokenizer = AutoTokenizer.from_pretrained("model")









# 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):
    

    img =  Image.open(image).convert("RGB")

    generated_caption = tokenizer.decode(model.generate(feature_extractor(img, return_tensors="pt").pixel_values.to("cuda"))[0])

    # caption_vitgpt = generate_caption(model, image)
    #caption_vitgpt = generate_caption(feature_extractor, cap_model, image, tokenizer)

    return '\033[96m' +generated_caption[:85]+ '\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"



iface = gr.Interface(fn=predict_event, 
                         # inputs=gr.inputs.Image(type="pil"),
                         gr.Image(type="pil"),"image",
                         outputs = gr.outputs.Textbox(label="Caption generated by ViT+GPT-2")
                        
    


# app = gr.mount_gradio_app(app, iface, path="/")

iface.launch(server_name = "0.0.0.0", server_port = 8001)

# if __name__ == "__main__":
    # Use this for debugging purposes only
 
    # uvicorn.run(app, host="0.0.0.0", port=8001)