Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch  # Import torch
+
+# Load the model and tokenizer (same as your original code)
+model_name = "frameai/PersianSentiment"
+loaded_tokenizer = AutoTokenizer.from_pretrained(model_name)
+loaded_model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+
+def predict_sentiment(text):
+    """Predicts the sentiment of a given text."""
+    inputs = loaded_tokenizer(text, return_tensors="pt", padding=True, truncation=True)  # Add padding and truncation
+    outputs = loaded_model(**inputs)
+    # Use softmax to get probabilities and argmax to get the predicted class
+    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predictions = torch.argmax(probabilities, dim=-1).item()
+
+    if predictions == 0:
+        sentiment = "Negative"
+    elif predictions == 1:
+        sentiment = "Positive"
+    else:
+        sentiment = "Neutral"
+
+    #  Return probabilities as well for a more informative output
+    return {
+        "Negative": float(probabilities[0][0]),
+        "Positive": float(probabilities[0][1]),
+        "Neutral": float(probabilities[0][2]),
+    }, sentiment
+
+# Create example sentences
+examples = [
+    ["این فیلم عالی بود!"],  # Positive example
+    ["من این غذا را دوست نداشتم."],  # Negative example
+    ["هوا خوب است."],  # Neutral (could be slightly positive, depends on context)
+    ["کتاب جالبی بود اما کمی خسته کننده هم بود."] , # Mixed/Neutral
+      ["اصلا راضی نبودم."] #negative
+]
+
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(label="Enter Persian Text", lines=5, placeholder="Type your text here..."),
+    outputs=[
+        gr.Label(label="Sentiment Probabilities"),
+        gr.Textbox(label="Predicted Sentiment") # Add output component for the sentiment string
+
+    ],
+    title="Persian Sentiment Analysis",
+    description="Enter a Persian sentence and get its sentiment (Positive, Negative, or Neutral).",
+    examples=examples,
+    live=False  # set to True for automatic updates as you type
+)
+
+
+if __name__ == "__main__":
+    iface.launch()