Delete app.py

app.py

@@ -1,55 +0,0 @@
-import streamlit as st
-import torch
-from tqdm import tqdm
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForSeq2SeqLM
-from transformers import AutoTokenizer
-
-config = PeftConfig.from_pretrained("NursNurs/T5ForReverseDictionary")
-model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large")
-model = PeftModel.from_pretrained(model, "NursNurs/T5ForReverseDictionary")
-
-tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large")
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-def return_top_k(sentence, k=10):
-
-  inputs = [f"Descripton : {sentence}. Word : "]
-
-  inputs = tokenizer(
-      inputs,
-      padding=True, truncation=True,
-      return_tensors="pt",
-  )
-
-
-  model.to(device)
-
-  with torch.no_grad():
-    inputs = {k: v.to(device) for k, v in inputs.items()}
-    output_sequences = model.generate(input_ids=inputs["input_ids"], max_new_tokens=10, num_beams=k, num_return_sequences=k, #max_length=3,
-                                              top_p = 50, output_scores=True, return_dict_in_generate=True) #repetition_penalty=10000.0
-    #print("output_sequences", output_sequences)
-    logits = output_sequences['sequences_scores'].clone().detach()
-    decoded_probabilities = torch.softmax(logits, dim=0)
-
-
-    #all word predictions
-    predictions = [tokenizer.decode(tokens, skip_special_tokens=True) for tokens in output_sequences['sequences']]
-    probabilities = [round(float(prob), 2) for prob in decoded_probabilities]
-
-  return predictions
-
-
-st.title("You name it!")
-
-# adding the text that will show in the text box as default
-default_value = "Type the description of the word you have in mind!"
-
-sent = st.text_area("Text", default_value, height = 275)
-
-result = return_top_k(sent)
-st.write("Here are my guesses about your word:")
-st.write(result)
-