Create app.py

app.py

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+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)