create app.py

app.py

@@ -0,0 +1,55 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import pickle
+from scipy.special import softmax
+# import transfomers
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
+# from transformers import AutoModelForSequenceClassification
+# from transformers import TFAutoModelForSequenceClassification
+# from transformers import AutoTokenizer, AutoConfig
+
+# Requirements
+model_path = "IsaacSarps/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)
+
+
+def sent_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 = {0: 'NEGATIVE', 1: 'NEUTRAL', 2: 'POSITIVE'}
+    scores = {labels[i]: float(s) for i, s in enumerate(scores_)}
+    return scores
+
+demo = gr.Interface(
+    fn=sent_analysis,
+    inputs=gr.Textbox(placeholder="Share your thoughts on COVID vaccines..."),
+    outputs="label",
+    interpretation="default",
+    examples=[["I feel confident about covid vaccines."]],
+    title="COVID Vaccine Sentiment Analysis",
+    description="An AI model that predicts sentiment about COVID vaccines, providing labels and probabilities for 'NEGATIVE', 'NEUTRAL', and 'POSITIVE' sentiments.",
+    theme="default", 
+    live=True  
+)
+
+demo.launch()