Update app.py

app.py

@@ -1,46 +1,3 @@
-import gradio as gr
-import numpy as np
-import pandas as pd
-import pickle
-import transformers
-from transformers import AutoTokenizer 
-from transformers import AutoConfig
-from transformers import AutoModelForSequenceClassification
-from transformers import TFAutoModelForSequenceClassification
-from transformers import pipeline
-from scipy.special import softmax
-
-# Requirements
-model_path ="HOLYBOY/Sentiment_Analysis"
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-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)
-
-# ---- Function to process the input and return prediction
-def sentiment_analysis(text):
-    text = preprocess(text)
-
-    encoded_input = tokenizer(text, return_tensors = "pt") # for PyTorch-based models
-    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
-
-
 # ---- Gradio app interface
 app = gr.Interface(fn = sentiment_analysis,
                    inputs = gr.Textbox("Input your tweet to classify or use the example provided below..."),