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app.py

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+### 1. Imports and class names setup ### 
+import gradio as gr
+import os
+import torch
+from transformers import BertTokenizer, BertModel, BertConfig
+# 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=len(label_dict),
+                                                      output_attentions=False,
+                                                      output_hidden_states=False)
+model.load_state_dict(torch.load('/content/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()

requirements.txt

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+torch==1.12.0
+torchvision==0.13.0
+transformers==4.35.2
+gradio==3.1.4