Delete app.py

app.py

@@ -1,102 +0,0 @@
-### 1. Imports and class names setup ### 
-import gradio as gr
-import os
-import torch
-from transformers import BertTokenizer, BertModel, BertConfig
-from transformers import BertForSequenceClassification
-# from model import create_effnetb2_model
-from timeit import default_timer as timer
-# from typing import Tuple, Dict
-
-# Setup class names
-# class_names = ["pizza", "steak", "sushi"]
-
-### 2. Model and transforms preparation ###
-tokenizer = BertTokenizer.from_pretrained('bert-base-uncased',
-                                          do_lower_case=True)
-# Create BERT model
-model = BertForSequenceClassification.from_pretrained("bert-base-uncased",
-                                                      num_labels=2,
-                                                      output_attentions=False,
-                                                      output_hidden_states=False)
-model.load_state_dict(torch.load(f='finetuned_BERT_epoch_10.model', map_location=torch.device('cpu')))
-### 3. Predict function ###
-
-# Create predict function
-def predict(text) :
-    """Transforms and performs a prediction on Text.
-    """
-    # Start the timer
-    start_time = timer()
-    encoding = tokenizer.encode_plus(
-            text,
-            None,
-            add_special_tokens=True,
-            max_length=256,
-            pad_to_max_length=True,
-            return_token_type_ids=True,
-            return_tensors='pt'
-        )
-    
-    model.eval()
-
-    loss_val_total = 0
-    predictions = []
-    # batch = tuple(prediction)
-
-    inputs = {'input_ids':      encoding["input_ids"],
-            'attention_mask':   encoding["attention_mask"],
-                 }
-
-    with torch.no_grad():
-        outputs = model(**inputs)
-
-    print(outputs)
-    # loss = outputs[0]
-    logits = outputs[0]
-    # loss_val_total += loss.item()
-
-    logits = logits.detach().cpu().numpy()
-    # print(logits)
-    # label_ids = inputs['labels'].cpu().numpy()
-    predictions.append(logits)
-        # true_vals.append(label_ids)
-
-    # loss_val_avg = loss_val_total/len(dataloader_val)
-
-    predictions = np.concatenate(predictions, axis=0)
-    
-    preds_flat = np.argmax(predictions, axis=1).flatten()
-
-    if preds_flat==0:
-      prediction = "positive"
-    else:
-      prediction = "negative"
-    
-    # Calculate the prediction time
-    pred_time = round(timer() - start_time, 5)
-    
-    # Return the prediction dictionary and prediction time 
-    return prediction, pred_time
-
-### 4. Gradio app ###
-
-# Create title, description and article strings
-title = "Sentiment Analysis"
-description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
-
-# Create examples list from "examples/" directory
-# example_list = [["examples/" + example] for example in os.listdir("examples")]
-
-# Create the Gradio demo
-demo = gr.Interface(fn=predict, # mapping function from input to output
-                    inputs=["text", "checkbox"],
-                    outputs=["text", 
-                             gr.Number(label="Prediction time (s)")],
-                    # Create examples list from "examples/" directory
-                    # examples=example_list, 
-                    title=title,
-                    description=description)
-
-# Launch the demo!
-demo.launch()