Update app.py

app.py

@@ -7,11 +7,20 @@ import gradio as gr
 tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
 tokenizer.pad_token = tokenizer.eos_token
 
-# === Load Model + QLoRA Adapter ===
-checkpoint_dir = "umangshikarvar/sentiment-gpt-neo-qlora"  # Update if needed
+# === Load Model + QLoRA Adapter from Hugging Face Hub ===
+checkpoint_dir = "umangshikarvar/sentiment-qlora-gptneo"
 peft_config = PeftConfig.from_pretrained(checkpoint_dir)
-base_model = AutoModelForCausalLM.from_pretrained(peft_config.base_model_name_or_path, torch_dtype=torch.float16)
+
+# Use float16 only if CUDA is available
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    peft_config.base_model_name_or_path,
+    torch_dtype=torch_dtype
+)
+
 model = PeftModel.from_pretrained(base_model, checkpoint_dir)
+
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.eval().to(device)
 
@@ -58,10 +67,13 @@ def predict_sentiment(tweet):
         return "Neutral"
 
 # === Gradio Interface ===
-gr.Interface(
+demo = gr.Interface(
     fn=predict_sentiment,
     inputs=gr.Textbox(lines=2, placeholder="Enter the text", label="Statement"),
-    outputs="text",
+    outputs=gr.Text(label="Predicted Sentiment"),
     title="Sentiment Classifier",
-    description="Classifies the sentiment of a statement, as Positive, Negative, or Neutral."
-).launch(share=True)
+    description="Classifies the sentiment of a statement as Positive, Negative, or Neutral."
+)
+
+# === Launch (set share=False for HF Spaces) ===
+demo.launch(share=False)