Updated model

app.py

@@ -1,4 +1,36 @@
 import streamlit as st
+from transformers import GPT2Tokenizer, GPT2Model, FlaxGPT2LMHeadModel, GPT2LMHeadModel, pipeline, set_seed
+import torch
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+#===========================================#
+#        Loads Model and Pipeline        #
+#===========================================#
+
+from transformers import GPT2Tokenizer, GPT2Model, FlaxGPT2LMHeadModel, GPT2LMHeadModel, pipeline, set_seed
+ 
+tokenizer = AutoTokenizer.from_pretrained("flax-community/swe-gpt-wiki")
+model = AutoModelWithLMHead.from_pretrained("flax-community/swe-gpt-wiki")
+
+generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
+set_seed(42)
+
+
+#===========================================#
+#              Streamlit Code               #
+#===========================================#
+desc = "En svensk GPT-modell tränad på wikipedia"
+
+st.title('Vi använder wikipidea för att generera text')
+st.write(desc)
+
+num_sentences = st.number_input('Number of Sentences', min_value=1, max_value=20, value=5)
+user_input = st.text_input('Seed Text (can leave blank)')
+
+
+if st.button('Generate Text'):
+    generated_text = generator(user_input, max_length=num_sentences, num_return_sequences=1)
+    st.write(generated_text[0]["generated_text"])
+
+
+
+    

evaluate.py

@@ -0,0 +1,8 @@
+from transformers import GPT2Tokenizer, GPT2Model, FlaxGPT2LMHeadModel, GPT2LMHeadModel, pipeline, set_seed
+ 
+tokenizer = GPT2Tokenizer.from_pretrained("flax-community/swe-gpt-wiki")
+model = GPT2LMHeadModel.from_pretrained("flax-community/swe-gpt-wiki")
+generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
+set_seed(42)
+result = generator("Det var en", max_length=50, num_return_sequences=5)
+print(result)

requirements.txt

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