Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
+from scipy.special import softmax
+
+# Load fine-tuned model and tokenizer
+MODEL_PATH = ""  # Ensure the model is uploaded to Hugging Face
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+config = AutoConfig.from_pretrained(MODEL_PATH)
+
+# Preprocess function
+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)
+
+# Prediction function
+def predict_sentiment(text):
+    text = preprocess(text)
+    encoded_input = tokenizer(text, return_tensors='pt')
+    output = model(**encoded_input)
+    scores = output[0][0].detach().numpy()
+    scores = softmax(scores)
+
+    # Get sentiment labels and scores
+    ranking = np.argsort(scores)[::-1]
+    result = {config.id2label[ranking[i]]: round(float(scores[ranking[i]]) * 100, 2) for i in range(scores.shape[0])}
+    return result
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=3, placeholder="Enter text..."),
+    outputs=gr.Label(),
+    title="Fine-Tuned Sentiment Analysis",
+    description="Enter a sentence to analyze its sentiment (Positive, Neutral, Negative).",
+)
+
+# Launch the app
+interface.launch()