inital push

app.py

@@ -0,0 +1,31 @@
+# streamlit for gpt2 model web app
+import streamlit as st
+import tensorflow as tf
+from transformers import TFGPT2LMHeadModel, GPT2Tokenizer
+
+tokenizer = GPT2Tokenizer.from_pretrained("ashiqabdulkhader/GPT2-Poet")
+model = TFGPT2LMHeadModel.from_pretrained("ashiqabdulkhader/GPT2-Poet")
+
+st.title("GPT2 Poet")
+st.write("This is a web app for GPT2 Poet model. You can generate poems using this web app.")
+
+prompt = st.text_input("Enter a prompt for the poem", "The quick brown fox")
+length = st.slider("Length of the poem", min_value=100,
+                   max_value=1000, value=100)
+temperature = st.slider("Temperature", min_value=0.0, max_value=1.0, value=1.0)
+top_k = st.slider("Top K", min_value=0, max_value=10, value=0)
+top_p = st.slider("Top P", min_value=0.0, max_value=1.0, value=0.9)
+
+input_ids = tokenizer.encode(prompt, return_tensors='tf')
+sample_outputs = model.generate(
+    input_ids,
+    do_sample=True,
+    max_length=length,
+    top_k=top_k,
+    top_p=top_p,
+    temperature=temperature,
+    num_return_sequences=3
+)
+
+st.write("Output:", tokenizer.decode(
+    sample_outputs[0], skip_special_tokens=True))

requirements.txt

@@ -0,0 +1,2 @@
+transformers
+tensorflow