Add application file

app.py

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+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
+import numpy as np
+from scipy.special import softmax
+
+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)
+
+MODEL = "cardiffnlp/twitter-roberta-base-sentiment-latest"
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+config = AutoConfig.from_pretrained(MODEL)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL, output_attentions=False, output_hidden_states=False)
+
+def predict_sentiment(text):
+    text = preprocess(text)
+    encoded_input = tokenizer(text, return_tensors='pt')
+    output = model(**encoded_input)
+    scores = output.logits[0].detach().numpy()
+    scores = softmax(scores)
+    ranking = np.argsort(scores)[::-1]
+    results = []
+    for i in range(scores.shape[0]):
+        label = config.id2label[ranking[i]]
+        score = np.round(float(scores[ranking[i]]), 4)
+        results.append(f"{label}: {score}")
+    return "\n".join(results)
+
+examples = [
+    ["I feel happy!"],
+    ["Had a lovely day at the park 🌳"],
+    ["Feeling down after today's news 😞"],
+    ["Just landed a new job, super excited!!"]
+]
+
+footer_text = """
+<b>About the Model</b><br>
+This sentiment analysis model is based on the roberta-base architecture and has been fine-tuned for sentiment analysis on tweets. For more information, check out the model's repository on Hugging Face:
+<a href="https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest" target="_blank">cardiffnlp/twitter-roberta-base-sentiment-latest</a>.
+"""
+
+iface = gr.Interface(fn=predict_sentiment, 
+                     inputs=gr.components.Textbox(lines=2, placeholder="Enter Text Here..."), 
+                     outputs="text",
+                     title="Sentiment Analysis",
+                     description="This model predicts the sentiment of a given text. Enter text to see its sentiment.",
+                     examples=examples,
+                     article=footer_text)
+
+if __name__ == "__main__":
+    iface.launch()