Update app.py

app.py

@@ -1,7 +1,25 @@
 import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-# Define the Gradio interface using the model you mentioned
-iface = gr.Interface.load("rabiaqayyum/autotrain-mental-health-analysis-752423172")
+# Load model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained("rabiaqayyum/autotrain-mental-health-analysis-752423172")
+tokenizer = AutoTokenizer.from_pretrained("rabiaqayyum/autotrain-mental-health-analysis-752423172")
 
-# Launch the Gradio interface
+# Define function to process inputs and get predictions
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    outputs = model(**inputs)
+    predicted_class = outputs.logits.argmax().item()
+    return "Positive" if predicted_class == 1 else "Negative"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs="text",
+    outputs="text",
+    layout="vertical",
+    description="Enter text to get model predictions."
+)
+
+# Launch the interface
 iface.launch()