Create app.py

app.py

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+# load important libraries
+from datasets import load_dataset
+from transformers import AutoModelForSeq2SeqLM
+from transformers import AutoTokenizer
+from transformers import GenerationConfig
+import streamlit as st
+
+# load the dialog summarization dataset
+huggingface_dataset_name = "knkarthick/dialogsum"
+dataset = load_dataset(huggingface_dataset_name)
+
+# load the google FLAN-T5 base model
+model_name='google/flan-t5-base'
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# load the specific tokenizer for above model
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+
+# initialize variables
+example_indices_full = [40]
+example_indices_full_few_shot = [40, 80, 120, 200, 220]
+dash_line = '-'.join('' for x in range(100))
+
+# zero_shot inference
+def zero_shot(my_example):
+    prompt = f"""
+Dialogue:
+
+{my_example}
+
+What was going on?
+"""
+    generation_config = GenerationConfig(max_new_tokens=80, do_sample=True, temperature=1.0)
+
+    inputs = tokenizer(prompt, return_tensors='pt')
+    output = tokenizer.decode(
+        model.generate(
+            inputs["input_ids"],
+            generation_config=generation_config,
+        )[0],
+        skip_special_tokens=True
+    )
+
+    return output
+
+# this prompt template will be used
+def my_prompt(example_indices, my_example):
+    prompt = ''
+    for index in example_indices:
+        dialogue = dataset['test'][index]['dialogue']
+        summary = dataset['test'][index]['summary']
+        prompt += f"""
+Dialogue:
+
+{dialogue}
+
+What was going on?
+{summary}
+
+
+"""
+
+    prompt += f"""
+Dialogue:
+
+{my_example}
+
+What was going on?
+"""
+
+    return prompt
+
+
+# this is for one_shot
+def one_shot(example_indices_full,my_example):
+  generation_config = GenerationConfig(max_new_tokens=80, do_sample=True, temperature=1.0)
+
+  inputs = tokenizer(my_prompt(example_indices_full,my_example), return_tensors='pt')
+  output = tokenizer.decode(
+      model.generate(
+          inputs["input_ids"],
+          generation_config=generation_config,
+      )[0],
+      skip_special_tokens=True
+  )
+  return output
+
+# few_shot
+def few_shot(example_indices_full_few_shot,my_example):
+  generation_config = GenerationConfig(max_new_tokens=80, do_sample=True, temperature=1.0)
+  inputs = tokenizer(my_prompt(example_indices_full_few_shot,my_example), return_tensors='pt')
+  output = tokenizer.decode(
+      model.generate(
+          inputs["input_ids"],
+          generation_config=generation_config,
+      )[0],
+      skip_special_tokens=True
+  )
+  return output
+
+st.title("Google FLAN-T5(Base) Prompt Engineered Model: Zero-shot, Single-shot, and Few-shot")
+
+my_example = st.text_area("Enter dialogues to summarize", value="#Maaz#: Jalal how are you?\n#Jalal#: I am good thank you.\n#Maaz#: Are you going to school tomorrow.\n#Jalal#: No bro i am not going to school tomorrow.\n#Maaz#: why?\n#Jalal#: I am working on a project, are you want to work with me on my project?\n#Maaz#: sorry, i have to go to school.")
+
+if st.button("Run"):  
+    zero_shot_output = zero_shot(my_example)
+    one_shot_output = one_shot(example_indices_full, my_example)
+    few_shot_output = few_shot(example_indices_full_few_shot, my_example)
+
+    st.header("**Comparison of Outputs**")
+
+    # Create three columns
+    col1, col2, col3 = st.columns(3)
+
+    # Display outputs in respective columns
+    with col1:
+        st.subheader("Zero-shot Output")
+        st.write(zero_shot_output)
+
+    with col2:
+        st.subheader("One-shot Output")
+        st.write(one_shot_output)
+
+    with col3:
+        st.subheader("Few-shot Output")
+        st.write(few_shot_output)