Update app.py

app.py

@@ -84,42 +84,7 @@ def inference(input_sentence, max_length, sample_or_greedy, seed=42):
         "",
     )
     '''
-    return generation
-
-
-
-
-
-def create_story(text_seed):
-  #tokenizer = AutoTokenizer.from_pretrained("gpt2")
-  #model = AutoModelForCausalLM.from_pretrained("gpt2")
-  
-  #eleutherAI gpt-3 based
-  tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M")
-  model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M")
-
-  # set pad_token_id to eos_token_id because GPT2 does not have a EOS token
-  model.config.pad_token_id = model.config.eos_token_id
-
-  #input_prompt = "It might be possible to"
-  input_prompt = text_seed
-  input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids
-
-  # instantiate logits processors
-  logits_processor = LogitsProcessorList(
-    [
-        MinLengthLogitsProcessor(10, eos_token_id=model.config.eos_token_id),
-    ]
-  )
-  stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=100)])
-
-  outputs = model.greedy_search(
-    input_ids, logits_processor=logits_processor, stopping_criteria=stopping_criteria
-  )
-
-  result_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
-  return result_text
-
+    return input_sentence + generation
 
 
 
@@ -127,12 +92,7 @@ def create_story(text_seed):
 
 def self_caption(image):
   repo_name = "ydshieh/vit-gpt2-coco-en"
-  #test_image = "cats.jpg"
   test_image = image
-  #url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
-  #test_image = Image.open(requests.get(url, stream=True).raw)
-  #test_image.save("cats.png")
-  
   feature_extractor2 = ViTFeatureExtractor.from_pretrained(repo_name)
   tokenizer = AutoTokenizer.from_pretrained(repo_name)
   model2 = VisionEncoderDecoderModel.from_pretrained(repo_name)
@@ -154,12 +114,11 @@ def self_caption(image):
   pred_dictionary = dict(zip(pred_keys, pred_value))
   print("Pred dictionary")
   print(pred_dictionary)
-  #return(pred_dictionary)
+ 
   preds = ' '.join(preds)
   #inference(input_sentence, max_length, sample_or_greedy, seed=42)
   story = inference(preds, 32, "Sample", 42) 
-  #story = create_story(preds)
-  #story = ' '.join(story)
+
   return story
 
 
@@ -200,7 +159,7 @@ label = gr.outputs.Label(num_top_classes=5)
 #examples = [ ["cats.jpg"], ["batter.jpg"],["drinkers.jpg"] ]
 examples = [ ["batter.jpg"] ] 
 title = "Generate a Story from an Image"
-description = "Demo for classifying images with Perceiver IO. To use it, simply upload an image and click 'submit', a story is autogenerated as well"
+description = "Demo for classifying images with Perceiver IO. To use it, simply upload an image and click 'submit', a story is autogenerated as well, story generated using Bigscience/BLOOM"
 article = "<p style='text-align: center'></p>"
 
 img_info1 = gr.Interface(
@@ -219,5 +178,5 @@ img_info2 = gr.Interface(
 )
 
 Parallel(img_info1,img_info2, inputs=image, title=title, description=description, examples=examples, enable_queue=True).launch(debug=True)
-#Parallel(img_info1,img_info2, inputs=image, outputs=label, title=title, description=description, examples=examples, enable_queue=True).launch(debug=True)
+