Uploaded required files

app.py

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+# -*- coding: utf-8 -*-
+"""Sentiment Analysis App.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1t6wAnMPDdEHuioRZofR8_JEPrzuT7KAJ
+"""
+
+
+
+# Import the required Libraries
+import gradio as gr
+import numpy as np
+import transformers
+from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
+from scipy.special import softmax
+
+
+# Requirements
+model_path = "Queensly/finetuned_albert_base_v2"
+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_)
+    
+    #Output of scores by converting a list of labels and scores into a dictionary format
+    labels = ["Negative", "Neutral", "Positive"]
+    scores = {l:float(s) for (l,s) in zip(labels, scores_) }
+    return scores
+
+
+#App interface with gradio
+app = gr.Interface(fn = sentiment_analysis,
+                   inputs = gr.Textbox("Write your text or tweet here..."),
+                   outputs = "label",
+                   title = "Sentiment Analysis of Tweets on COVID-19 Vaccines",
+                   description  = "This app analyzes sentiment of text based on tweets about COVID-19 Vaccines using a fine-tuned albert_base_v2 model",
+                   interpretation = "default",
+                   examples=[["covid vaccines are great!"]]
+                  )
+
+app.launch()

requirements.txt

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+numpy
+scipy
+torch 
+transformers