Delete app.py

app.py

@@ -1,48 +0,0 @@
-pip3 install -q transformers gradio
-
-from transformers import AutoModelForSequenceClassification
-from transformers import TFAutoModelForSequenceClassification
-from transformers import AutoTokenizer, AutoConfig
-import numpy as np
-from scipy.special import softmax
-import gradio as gr
-
-# Requirements
-model_path = f"Calistus/test_trainer"
-tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
-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)
-
-
-def sentiment_analysis(text):
-    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_)
-
-    # Format output dict of scores
-    labels = ['Negative', 'Neutral', 'Positive']
-    scores = {l:float(s) for (l,s) in zip(labels, scores_) }
-
-    return scores
-
-app = gr.Interface(
-    fn=sentiment_analysis,
-    inputs=gr.Textbox(placeholder="Write your tweet here..."),
-    outputs="label",
-    interpretation="default",
-    examples=[["Please don't listen to anyone. Vaccinate your child"],['My kid has a lump on his hand because of the vaccine']])
-
-app.launch()