Create app.py

app.py

@@ -0,0 +1,42 @@
+import streamlit as st
+from datasets import load_dataset
+from transformers import AutoModelForSeq2SeqLM
+from transformers import AutoTokenizer
+from transformers import GenerationConfig
+
+huggingface_dataset_name = "knkarthick/dialogsum"
+dataset = load_dataset(huggingface_dataset_name)
+
+model_name = 'google/flan-t5-base'
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+
+# select an example
+example_index = [40]
+
+# get the dialogue
+dialogue = dataset['test'][example_index]['dialogue']
+
+# get the human summary
+summary = dataset['test'][example_index]['summary']
+
+# Configurations
+# generation_config = GenerationConfig(max_new_tokens=50, do_sample=True, temperature=0.7)
+generation_config = GenerationConfig(max_new_tokens=50)
+
+# Encode input:
+inputs_encoded = tokenizer(dialogue, return_tensors='pt')
+
+# Model Output:
+model_output = model.generate(inputs_encoded["input_ids"], generation_config=generation_config)[0]
+
+# Decode the output
+zero_output = tokenizer.decode(model_output, skip_special_tokens=True)
+
+
+print("Input: ", dialogue)
+print(dash_line)
+print( "Human summary: ", summary)
+print(dash_line)
+print("Model Output: ", zero_output)