Update app.py

app.py

@@ -1,51 +1,10 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-from datasets import load_dataset
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from transformers  import Trainer, TrainingArguments
 
-model_name = "HuggingFaceH4/zephyr-7b-beta"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-
-dataset = load_dataset("json", data_files="data.json", split = "train")
-
-# Tokenize the dataset
-def preprocess_function(examples):
-    inputs = [example['input'] for example in examples]
-    targets = [examples['output'] for example in examples]
-    model_inputs = tokenizer(inputs, padding=True, truncation=True)
-    labels = tokenizer(targets, padding=True, truncation=True).input_ids
-    model_inputs['labels'] = labels
-    return model_inputs
-
-tokenized_datasets = dataset.map(preprocess_function, batched = True)
-
-training_args = TrainingArguments(
-    output_dir = "./results",
-    evaluation_strategy = "epoch",
-    learning_rate = 2e-5,
-    per_device_train_batch_size = 3,
-    weight_decay = 0.01,
-)
-
-trainer = Trainer(
-    model = model,
-    args = training_args,
-    train_dataset = tokenized_datasets["train"],
-    eval_dataset = tokenized_datasets["validation"],
-)
-
-#  Start fine-tuning
-trainer.train()
-
-trainer.evaluate()
-
-model.save_pretrained("./fine_tuned_model")
-tokenizer.save_pretrained("./fine_tuned_model")
-
-
-client = InferenceClient("./fine_tuned_model")
+"""
+For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+"""
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 
 def respond(
@@ -80,6 +39,10 @@ def respond(
         response += token
         yield response
 
+
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
@@ -98,4 +61,4 @@ demo = gr.ChatInterface(
 
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()