Update app.py

app.py

@@ -35,17 +35,17 @@ def model_generate_ans(img,val_q):
     val_image_embeds = projection(clip_val_outputs).to(torch.float16)
 
     img_token_tensor = torch.tensor(IMAGE_TOKEN_ID).to(device)
-    img_token_embeds = peft_model.model.model.embed_tokens(img_token_tensor).unsqueeze(0).unsqueeze(0)
+    img_token_embeds = merged_model.model.model.embed_tokens(img_token_tensor).unsqueeze(0).unsqueeze(0)
 
     val_q_tokenised = tokenizer(val_q, return_tensors="pt", return_attention_mask=False)['input_ids'].squeeze(0)
-    val_q_embeds  = peft_model.model.model.embed_tokens(val_q_tokenised).unsqueeze(0)
+    val_q_embeds  = merged_model.model.model.embed_tokens(val_q_tokenised).unsqueeze(0)
     
     val_combined_embeds = torch.cat([val_image_embeds, img_token_embeds, val_q_embeds], dim=1) # 4, 69, 2560
     
     predicted_caption = torch.full((1,max_generate_length),50256)
 
     for g in range(max_generate_length):
-        phi_output_logits = peft_model(inputs_embeds=val_combined_embeds)['logits'] # 4, 69, 51200
+        phi_output_logits = merged_model(inputs_embeds=val_combined_embeds)['logits'] # 4, 69, 51200
         predicted_word_token_logits = phi_output_logits[:, -1, :].unsqueeze(1) # 4,1,51200
         predicted_word_token = torch.argmax(predicted_word_token_logits, dim = -1) # 4,1
         predicted_caption[:,g] = predicted_word_token.view(1,-1).to(device)