.

app.py

@@ -1,7 +1,30 @@
 import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load the model and tokenizer from the Hugging Face Hub
+model = AutoModelForSequenceClassification.from_pretrained("preetidav/my_model")
+tokenizer = AutoTokenizer.from_pretrained("preetidav/my_model")
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Function to predict sentiment
+def predict_sentiment(text):
+    # Tokenize the input text
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    # Forward pass through the model
+    outputs = model(**inputs)
+    # Get the prediction (0 or 1 for binary classification)
+    prediction = torch.argmax(outputs.logits, dim=1).item()
+    # Map prediction to sentiment labels
+    return "positive" if prediction == 1 else "negative"
+
+# Set up the Gradio interface
+iface = gr.Interface(
+    fn=predict_sentiment,              # Function to call
+    inputs=gr.Textbox(label="Input Text"),  # Input field for the text
+    outputs=gr.Textbox(label="Sentiment"), # Output field for the sentiment
+    title="Sentiment Analysis Model",    # Title of the app
+    description="This model predicts whether a given text has positive or negative sentiment.",  # Description of the app
+)
+
+# Launch the app
+iface.launch()