Create new file

app.py

@@ -0,0 +1,67 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
+import torch
+
+first = """It is a wonderful day to"""
+
+
+name_of_model = st.text_input("Name of the model you want to run", "gpt2")
+
+@st.cache(allow_output_mutation=True)
+def get_model(name_of_model):
+    tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    model = AutoModelForCausalLM.from_pretrained(name_of_model)
+    return model, tokenizer
+    
+model, tokenizer = get_model()
+temp = st.sidebar.slider("Temperature", 0.7, 1.5)
+number_of_outputs = st.sidebar.slider("Number of Outputs", 5, 50)
+lengths = st.sidebar.slider("Length", 3, 500)
+bad_words = st.text_input("Words You Do Not Want Generated", " core lemon height time ")
+logs_outputs = st.sidebar.slider("Logit Outputs", 50, 300)
+
+def run_generate(text, bad_words):
+  yo = []
+  input_ids = tokenizer.encode(text, return_tensors='pt')
+  res = len(tokenizer.encode(text))
+  bad_words = bad_words.split()
+  bad_word_ids = []
+  for bad_word in bad_words: 
+    bad_word = " " + bad_word
+    ids = tokenizer(bad_word).input_ids
+    bad_word_ids.append(ids)
+  sample_outputs = model.generate(
+    input_ids,
+    do_sample=True, 
+    max_length= res + lengths, 
+    min_length = res + lengths, 
+    top_k=50,
+    temperature=temp,
+    num_return_sequences=number_of_outputs,
+    bad_words_ids=bad_word_ids
+  )
+  for i in range(number_of_outputs):
+    e = tokenizer.decode(sample_outputs[i])
+    e = e.replace(text, "")
+    yo.append(e)
+  return yo
+with st.form(key='my_form'):
+    text = st.text_area(label='Enter sentence', value=first)
+    submit_button = st.form_submit_button(label='Submit')
+    submit_button2 = st.form_submit_button(label='Submit Log Probs')
+    if submit_button:
+      translated_text = run_generate(text, bad_words)
+      st.write(translated_text if translated_text else "No translation found")
+    if submit_button2:
+      with torch.no_grad():
+        text2 = str(text)
+        print(text2)
+        text3 = tokenizer.encode(text2)
+        myinput, past_key_values = torch.tensor([text3]), None
+        myinput = myinput
+        logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
+        logits = logits[0,-1]
+        probabilities = torch.nn.functional.softmax(logits)
+        best_logits, best_indices = logits.topk(logs_outputs)
+        best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]      
+        st.write(best_words)