Update app.py

app.py

@@ -1,100 +1,12 @@
-
 # imports
 import gradio as gr
 import pandas as pd
+import tempfile
+import itertools
+  # import required packages
+import torch
+import pandas as pd
 import numpy as np
 from numpy import dot
-from numpy.linalg import norm
-
-
-# compute dot product of inputs
-# summary function - test for single gradio function interfrace
-def gr_cosine_similarity(sentence1, sentence2):
-  
-  # load tokenizer and model, create trainer
-  model_name = "j-hartmann/emotion-english-distilroberta-base"
-  tokenizer = AutoTokenizer.from_pretrained(model_name)
-  model = AutoModelForSequenceClassification.from_pretrained(model_name)
-  trainer = Trainer(model=model)  
-  
- 
-  # Create class for data preparation
-  class SimpleDataset:
-      def __init__(self, tokenized_texts):
-          self.tokenized_texts = tokenized_texts
-      
-      def __len__(self):
-          return len(self.tokenized_texts["input_ids"])
-      
-      def __getitem__(self, idx):
-          return {k: v[idx] for k, v in self.tokenized_texts.items()}
-    
-  
-  # sentences in list
-  lines_s = [sentence1, sentence2]
-
-    # Tokenize texts and create prediction data set
-  tokenized_texts = tokenizer(lines_s, truncation=True, padding=True)
-  pred_dataset = SimpleDataset(tokenized_texts)
-
-    # Run predictions -> predict whole df
-  predictions = trainer.predict(pred_dataset)
-
-    # Transform predictions to labels
-  preds = predictions.predictions.argmax(-1)
-  labels = pd.Series(preds).map(model.config.id2label)
-  scores = (np.exp(predictions[0])/np.exp(predictions[0]).sum(-1,keepdims=True)).max(1)
-    # scores raw
-  temp = (np.exp(predictions[0])/np.exp(predictions[0]).sum(-1, keepdims=True)).tolist()
-
-
-  # work in progress
-  # container
-  anger = []
-  disgust = []
-  fear = []
-  joy = []
-  neutral = []
-  sadness = []
-  surprise = []
-
-  # extract scores (as many entries as exist in pred_texts)
-  for i in range(len(lines_s)):
-    anger.append(temp[i][0])
-    disgust.append(temp[i][1])
-    fear.append(temp[i][2])
-    joy.append(temp[i][3])
-    neutral.append(temp[i][4])
-    sadness.append(temp[i][5])
-    surprise.append(temp[i][6])
-
-  # define both vectors for the dot product
-  # each include all values for both predictions
-  v1 = temp[0]
-  v2 = temp[1]
-
-  # compute dot product of all 
-  dot_product = dot(v1, v2)
-
-   # define df
-  df = pd.DataFrame(list(zip(lines_s,labels, anger, disgust, fear, joy, neutral, sadness, surprise)), columns=['text','label', 'anger', 'disgust', 'fear', 'joy', 'neutral', 'sadness', 'surprise'])
-
-  # compute cosine similarity
-  # is dot product of vectors n / norms 1*..*n vectors
-  cosine_similarity = dot_product / (norm(v1) * norm(v2))
-
-
-  # return dataframe for space output
-  return  df, cosine_similarity
-  
-gr.Interface(gr_cosine_similarity,
-                 [
-                  gr.inputs.Textbox(lines=1, placeholder="This movie always makes me cry..", default="", label="Text 1"),
-                  gr.inputs.Textbox(lines=1, placeholder="Her dog is sad.", default="", label="Text 2"),
-
-                    #gr.outputs.Textbox(type="auto", label="Cosine similarity"),
-                                         ],
-                 ["dataframe","text"]
-                 ).launch(debug=True)
-                   
-              
+from numpy.linalg import norm, multi_dot
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer