Upload app.py

app.py

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+pip install transformers
+pip install datasets
+pip install gradio
+sudo apt-get install git-lfs
+
+import torch
+import pandas as pd
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, AutoModelForSequenceClassification, TrainingArguments 
+import gradio as gr
+import torch.nn.functional as F
+
+from datasets import load_dataset
+dataset = load_dataset("bananabot/engMollywoodSummaries")
+dataset
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model_name = "EleutherAI/gpt-neo-125M"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
+
+max_length=123
+input_txt = "This malayalam movie is about"
+n_steps = 8
+
+input_ids = tokenizer(input_txt, return_tensors="pt")["input_ids"].to(device)
+output = model.generate(input_ids,  max_length=max_length, num_beams=5, do_sample=True, no_repeat_ngram_size=2, temperature=1.37, top_k=69, top_p=0.96)
+print(tokenizer.decode(output[0]))
+
+generator = gr.Interface.load("models/EleutherAI/gpt-neo-125M")
+translator = gr.Interface.load("models/Helsinki-NLP/opus-mt-en-ml")
+gr.Series(generator, translator).launch()  # this demo generates text, then translates it to Malayalam, and outputs the final result.