Update app.py

app.py

@@ -6,9 +6,6 @@ import numpy as np
 from PIL import Image
 from transformers import AutoProcessor, VisionEncoderDecoderModel, AutoModelForImageTextToText
 import torch
-# Suppress PyTorch Dynamo compilation errors
-import torch._dynamo
-torch._dynamo.config.suppress_errors = True
 try:
     from sentence_transformers import SentenceTransformer
     import numpy as np
@@ -248,23 +245,13 @@ Provide a descriptive alt text in 1-2 sentences that is informative but not over
             input_len = input_ids["input_ids"].shape[-1]
             
             input_ids = input_ids.to(self.model.device, dtype=self.model.dtype)
-            
-            # Disable compilation to avoid Dynamo errors with Gemma 3n
-            with torch.no_grad():
-                outputs = self.model.generate(
-                    **input_ids,
-                    max_new_tokens=512,
-                    disable_compile=True,
-                    do_sample=True,
-                    temperature=0.1,
-                    top_p=0.9,
-                    top_k=50,
-                    num_beams=1,
-                    pad_token_id=self.processor.tokenizer.pad_token_id,
-                    eos_token_id=self.processor.tokenizer.eos_token_id,
-                    repetition_penalty=1.05,
-                    use_cache=True
-                )
+            outputs = self.model.generate(
+                **input_ids,
+                max_new_tokens=1024,
+                disable_compile=True,
+                do_sample=True,
+                temperature=0.7
+            )
             
             text = self.processor.batch_decode(
                 outputs[:, input_len:],
@@ -272,8 +259,7 @@ Provide a descriptive alt text in 1-2 sentences that is informative but not over
                 clean_up_tokenization_spaces=True
             )
             
-            result = text[0].strip()
-            return result if result else "I apologize, but I couldn't generate a proper response. Please try rephrasing your question."
+            return text[0].strip()
             
         except Exception as e:
             print(f"❌ Error in chat: {e}")