Update app.py

app.py

@@ -1,65 +1,10 @@
-import subprocess
-subprocess.run(["pip", "install", "-q", "transformers", "datasets", "gradio", "scipy"])
-
-from transformers import AutoModelForSequenceClassification
-from transformers import TFAutoModelForSequenceClassification
-from transformers import AutoTokenizer, AutoConfig
-import numpy as np
-from scipy.special import softmax
-
-
-tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
-
-model_path = f"avichr/heBERT_sentiment_analysis"
-config = AutoConfig.from_pretrained(model_path)
-model = AutoModelForSequenceClassification.from_pretrained(model_path)
-
-# Preprocess text (username and link placeholders)
-def preprocess(text):
-    new_text = []
-    for t in text.split(" "):
-        t = '@user' if t.startswith('@') and len(t) > 1 else t
-        t = 'http' if t.startswith('http') else t
-        new_text.append(t)
-    return " ".join(new_text)
-
-# Input preprocessing
-text = "Covid cases are increasing fast!"
-text = preprocess(text)
-
-# PyTorch-based models
-encoded_input = tokenizer(text, return_tensors='pt')
-output = model(**encoded_input)
-scores = output[0][0].detach().numpy()
-scores = softmax(scores)
-
-# TensorFlow-based models
-# model = TFAutoModelForSequenceClassification.from_pretrained(model_path)
-# model.save_pretrained(model_path)
-# text = "Covid cases are increasing fast!"
-# encoded_input = tokenizer(text, return_tensors='tf')
-# output = model(encoded_input)
-# scores = output[0][0].numpy()
-# scores = softmax(scores)
-
-config.id2label = {0: 'NEGATIVE', 1: 'NEUTRAL', 2: 'POSITIVE'}
-
-# Print labels and scores
-ranking = np.argsort(scores)
-ranking = ranking[::-1]
-print(f"Classified text: {text}")
-for i in range(scores.shape[0]):
-    l = config.id2label[ranking[i]]
-    s = scores[ranking[i]]
-    print(f"{i+1}) {l} {np.round(float(s), 4)}")
-
 from transformers import AutoModelForSequenceClassification
 from transformers import TFAutoModelForSequenceClassification
 from transformers import AutoTokenizer, AutoConfig
 from scipy.special import softmax
 import gradio as gr
-
-
+import numpy as np
+from scipy.special import softmax
 
 # Requirements
 model_path = f"avichr/heBERT_sentiment_analysis"
@@ -95,8 +40,9 @@ def sentiment_analysis(text):
 demo = gr.Interface(
     fn=sentiment_analysis,
     inputs=gr.Textbox(placeholder="Write how you feel about Covid here..."),
-    outputs="label",
-    examples=[["This is something!"]])
+    outputs="text",
+    examples=[["What's up with the Vaccine"]]
+    title = "Tutorial: Sentiment Analysis App"
+    description = "This App assess if a sentiment about Covid is positive or negative")
 
 demo.launch()
-