Update app.py

app.py

@@ -1,7 +1,70 @@
 import gradio as gr
 
 def greet(name):
-    return "Hello " + name + "!"
+    from datasets import load_dataset
+    
+    dataset = load_dataset("yelp_review_full")
+    dataset["train"][100]
+    
+    #creating the dataset
+    from transformers import AutoTokenizer
+    
+    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+    
+    
+    def tokenize_function(examples):
+        return tokenizer(examples["text"], padding="max_length", truncation=True)
+    
+    #mapping the values:
+    tokenized_datasets = dataset.map(tokenize_function, batched=True)
+    
+    #small Datasets:
+    small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
+    small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
+    
+    #Loading pretrained Model:
+    from transformers import AutoModelForSequenceClassification
+    
+    model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
+    
+    ###
+    
+    from transformers import TrainingArguments
+    
+    training_args = TrainingArguments(output_dir="test_trainer")
+    
+    #Evaluate
+    
+    def compute_metrics(eval_pred):
+        logits, labels = eval_pred
+        predictions = np.argmax(logits, axis=-1)
+        return metric.compute(predictions=predictions, references=labels)
+    
+    #Training Argumnents and importing Trainer:
+    from transformers import TrainingArguments, Trainer
+    
+    training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch")
+    
+    #Defining Hyperparameters for Trainer:
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=small_train_dataset,
+        eval_dataset=small_eval_dataset,
+        compute_metrics=compute_metrics,
+    )
+    
+    #Execute the training:
+    trainer.train()
+    
+    #Predictions:
+    predictions = trainer.predict(small_eval_dataset)
+    print(predictions.predictions.shape,predictions.label_ids.shape)
+    return predictions    
+    
+
+
+    
 
 demo = gr.Interface(fn=greet, inputs="text", outputs="text")