Create app.py

app.py

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+
+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Load the model and tokenizer from Hugging Face
+model_name = "poudel/Depression_and_Non-Depression_Classifier"  # Replace with your Hugging Face model name
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+
+#  Define the prediction function
+def predict(text):
+    # Tokenize the input text
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
+
+    # Get model predictions
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # Convert logits to probabilities
+    probabilities = torch.softmax(outputs.logits, dim=-1)
+
+    # Get the predicted class (0 or 1)
+    predicted_class = torch.argmax(probabilities, dim=1).item()
+
+    # Map the predicted class to the label (0 = Depression, 1 = Non-depression)
+    label_mapping = {0: "Depression", 1: "Non-depression"}
+
+    return label_mapping[predicted_class]
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=predict,  # The function to be called for predictions
+    inputs=gr.Textbox(lines=2, placeholder="Enter some text here..."),  # Input textbox for the user
+    outputs="text",  # Output is the predicted class as text
+    title="Depression Classification",  # Title of the app
+    description="Enter a sentence to classify it as 'Depression' or 'Non-depression'.",  # Short description
+)
+
+#  Launch the Gradio app
+interface.launch()