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Browse files- app.py +101 -0
- requirements.txt +4 -0
app.py
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### 1. Imports and class names setup ###
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import gradio as gr
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import os
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import torch
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from transformers import BertTokenizer, BertModel, BertConfig
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# from model import create_effnetb2_model
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from timeit import default_timer as timer
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# from typing import Tuple, Dict
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# Setup class names
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# class_names = ["pizza", "steak", "sushi"]
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### 2. Model and transforms preparation ###
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased',
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do_lower_case=True)
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# Create BERT model
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model = BertForSequenceClassification.from_pretrained("bert-base-uncased",
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num_labels=len(label_dict),
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output_attentions=False,
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output_hidden_states=False)
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model.load_state_dict(torch.load('/content/finetuned_BERT_epoch_10.model', map_location=torch.device('cpu')))
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### 3. Predict function ###
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# Create predict function
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def predict(text) :
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"""Transforms and performs a prediction on Text.
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"""
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# Start the timer
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start_time = timer()
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encoding = tokenizer.encode_plus(
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text,
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None,
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add_special_tokens=True,
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max_length=256,
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pad_to_max_length=True,
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return_token_type_ids=True,
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return_tensors='pt'
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)
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model.eval()
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loss_val_total = 0
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predictions = []
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# batch = tuple(prediction)
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inputs = {'input_ids': encoding["input_ids"],
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'attention_mask': encoding["attention_mask"],
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}
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with torch.no_grad():
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outputs = model(**inputs)
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print(outputs)
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# loss = outputs[0]
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logits = outputs[0]
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# loss_val_total += loss.item()
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logits = logits.detach().cpu().numpy()
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# print(logits)
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# label_ids = inputs['labels'].cpu().numpy()
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predictions.append(logits)
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# true_vals.append(label_ids)
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# loss_val_avg = loss_val_total/len(dataloader_val)
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predictions = np.concatenate(predictions, axis=0)
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preds_flat = np.argmax(predictions, axis=1).flatten()
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if preds_flat==0:
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prediction = "positive"
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else:
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prediction = "negative"
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# Calculate the prediction time
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pred_time = round(timer() - start_time, 5)
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# Return the prediction dictionary and prediction time
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return prediction, pred_time
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### 4. Gradio app ###
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# Create title, description and article strings
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title = "Sentiment Analysis"
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description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
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# Create examples list from "examples/" directory
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# example_list = [["examples/" + example] for example in os.listdir("examples")]
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# Create the Gradio demo
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demo = gr.Interface(fn=predict, # mapping function from input to output
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inputs=["text", "checkbox"],
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outputs=["text",
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gr.Number(label="Prediction time (s)")],
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# Create examples list from "examples/" directory
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# examples=example_list,
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title=title,
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description=description)
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# Launch the demo!
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demo.launch()
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requirements.txt
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torch==1.12.0
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torchvision==0.13.0
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transformers==4.35.2
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gradio==3.1.4
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