final commit

app.py

@@ -16,16 +16,16 @@ app = Flask(__name__)
     
 warnings.filterwarnings('ignore') 
 
-model_id = "HuggingFaceH4/vsft-llava-1.5-7b-hf-trl"
-quantization_config = BitsAndBytesConfig(load_in_4bit=True)
-base_model = LlavaForConditionalGeneration.from_pretrained(model_id, quantization_config=quantization_config, torch_dtype=torch.float16)
+# model_id = "HuggingFaceH4/vsft-llava-1.5-7b-hf-trl"
+# quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+# base_model = LlavaForConditionalGeneration.from_pretrained(model_id, quantization_config=quantization_config, torch_dtype=torch.float16)
 
-# Load the PEFT Lora adapter
-peft_lora_adapter_path = "Praveen0309/llava-1.5-7b-hf-ft-mix-vsft-3"
-peft_lora_adapter = PeftModel.from_pretrained(base_model, peft_lora_adapter_path, adapter_name="lora_adapter")
-base_model.load_adapter(peft_lora_adapter_path, adapter_name="lora_adapter")
+# # Load the PEFT Lora adapter
+# peft_lora_adapter_path = "Praveen0309/llava-1.5-7b-hf-ft-mix-vsft-3"
+# peft_lora_adapter = PeftModel.from_pretrained(base_model, peft_lora_adapter_path, adapter_name="lora_adapter")
+# base_model.load_adapter(peft_lora_adapter_path, adapter_name="lora_adapter")
 
-processor = AutoProcessor.from_pretrained("HuggingFaceH4/vsft-llava-1.5-7b-hf-trl")
+# processor = AutoProcessor.from_pretrained("HuggingFaceH4/vsft-llava-1.5-7b-hf-trl")
 # model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
 # tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M")
 
@@ -144,8 +144,8 @@ def get_bot_response():
           image = image_cache['image']
           # print(image)
           query = request.args.get('msg')
-          # output = query
-          output = google_response(image, query)
+          output = query
+          # output = google_response(image, query)
           return output
       else:
           return "Please upload an image to continue"