Update app.py

app.py

@@ -1,23 +1,31 @@
 import torch
 import gradio as gr
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from peft import PeftModel, PeftConfig
 
-# Load model and tokenizer from Hugging Face Hub
-model_name = "shukdevdatta123/twitter-distilbert-base-uncased-sentiment-analysis-lora-text-classification"
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Load PEFT adapter config
+adapter_name = "shukdevdatta123/twitter-distilbert-base-uncased-sentiment-analysis-lora-text-classification"
+config = PeftConfig.from_pretrained(adapter_name)
 
-# Define the label mapping (binary sentiment: 0 = Negative, 1 = Positive)
-id2label = {
-    0: "Negative",
-    1: "Positive"
-}
+# Load base model and tokenizer
+base_model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+# Load the LoRA adapter into the base model
+model = PeftModel.from_pretrained(base_model, adapter_name)
 
 # Set device
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
+model.eval()
+
+# Label mapping
+id2label = {
+    0: "Negative",
+    1: "Positive"
+}
 
-# Define the prediction function
+# Prediction function
 def predict_sentiment(text):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
     
@@ -26,21 +34,19 @@ def predict_sentiment(text):
 
     predicted_class = torch.argmax(logits, dim=1).item()
     label = id2label[predicted_class]
-    
-    # Optional: add confidence score
+
     probs = torch.nn.functional.softmax(logits, dim=1)
     confidence = probs[0][predicted_class].item()
 
     return f"{label} (Confidence: {confidence:.2f})"
 
-# Create Gradio Interface
+# Gradio UI
 interface = gr.Interface(
     fn=predict_sentiment,
     inputs=gr.Textbox(lines=2, placeholder="Enter a sentence to analyze sentiment..."),
     outputs="text",
-    title="Twitter Sentiment Classifier",
-    description="This app uses a fine-tuned DistilBERT model with LoRA adapters to predict whether a tweet or sentence is Positive or Negative."
+    title="Twitter Sentiment Classifier (LoRA + DistilBERT)",
+    description="This app uses a DistilBERT model with LoRA adapters to classify tweet sentiment as Positive or Negative."
 )
 
-# Launch the app
 interface.launch()