app init

.gitignore

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+.venv/

app.py

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+import gradio as gr
+import numpy as np
+import torch
+from transformers import AutoModelForSequenceClassification
+from transformers import TFAutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoConfig
+from scipy.special import softmax
+
+
+# Setup
+model_path = f"pakornor/roberta-base"
+
+tokenizer = AutoTokenizer.from_pretrained('roberta-base')
+config = AutoConfig.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+
+# Functions
+
+# Preprocess text
+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
+def sentiment_analysis(text):
+    text = preprocess(text)
+
+
+    encoded_input = tokenizer(text, return_tensors='pt')
+    output = model(**encoded_input)
+    scores_ = output[0][0].detach().numpy()
+    scores_ = softmax(scores_)
+
+    # Format output dictionary of scores
+    labels = ['Negative', 'Neutral', 'Positive']
+    scores = {l:float(s) for (l,s) in zip(labels, scores_) }
+
+    return scores
+
+
+# Gradio App
+app = gr.Interface(
+    fn=sentiment_analysis,
+    inputs=gr.Textbox("Input tweet here:"),
+    outputs="label",
+    title="Sentiment Analysis of Tweets on Covid-19 Vaccines",
+    description="With this App, you can type Tweets related to the Covid Vaccine and the app will rate the sentiment of the tweet..!",
+    examples=[["Be careful of covid vaccination"],
+              ["The vaccine can reduce your immunity to diseases"],
+              ["I cant wait for the Covid Vaccine!"]]
+    )
+
+app.launch()

requirements.txt

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+transformers==4.35.0
+gradio==4.2.0
+numpy==1.23.5
+scipy==1.11.3
+scikit-learn==1.2.2