Universal: Fix Peft error and add local/HF detection

app.py

@@ -1,14 +1,18 @@
+import os
 import gradio as gr
 import torch
 from PIL import Image
 from peft import PeftModel
 from transformers import AutoTokenizer, TrOCRProcessor, ViTImageProcessor, VisionEncoderDecoderModel
 
-# Configuration
+# --- CONFIGURATION ---
 BASE_MODEL_ID = "paudelanil/trocr-devanagari-2"
 ADAPTER_ID = "manishw10/devgen-trocr-devanagari-lora" 
 
-print("System: Loading model components...")
+# Detect environment
+IS_SPACE = "SPACE_ID" in os.environ
+
+print(f"System: Loading model... (Env: {'Hugging Face Space' if IS_SPACE else 'Local'})")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 try:
@@ -30,14 +34,17 @@ def predict(image):
     try:
         image = image.convert("RGB")
         pixel_values = processor(image, return_tensors="pt").pixel_values.to(device)
+        
+        # --- ROBUST GENERATE CALL ---
+        # Using inputs= as a keyword argument fixes the PeftModel positional argument error.
         with torch.no_grad():
-            generated_ids = model.generate(pixel_values)
+            generated_ids = model.generate(inputs=pixel_values)
             generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
         return generated_text
     except Exception as e:
         return f"Error during inference: {str(e)}"
 
-# Classic Gradio 3.x Interface
+# Interface setup
 demo = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil", label="Upload Handwritten Devanagari Word"),
@@ -48,5 +55,6 @@ demo = gr.Interface(
 )
 
 if __name__ == "__main__":
-    # Required settings for Gradio 3.x on HF Spaces
-    demo.launch(server_name="0.0.0.0")
+    # If running on HF Spaces, use 0.0.0.0. If local, use default localhost.
+    server_name = "0.0.0.0" if IS_SPACE else "127.0.0.1"
+    demo.launch(server_name=server_name)