solving ViLT problem

app.py

@@ -8,8 +8,7 @@ inference = Inference()
 
 with gr.Blocks() as block:
     txt = gr.Textbox(label="Insert a question..", lines=2)
-    outputs = [gr.outputs.Textbox(label="Answer from BLIP saffal model"), gr.outputs.Textbox(label="Answer from BLIP control net"), 
-               gr.outputs.Textbox(label="Answer from ViLT saffal model"), gr.outputs.Textbox(label="Answer from ViLT control net")]
+    outputs = [gr.outputs.Textbox(label="Answer from BLIP saffal model"), gr.outputs.Textbox(label="Answer from BLIP control net")]
 
     btn = gr.Button(value="Submit")
 

inference.py

@@ -5,10 +5,6 @@ import torch
 
 class Inference:
     def __init__(self):
-        self.vilt_processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
-        self.vilt_model_saffal = BlipForQuestionAnswering.from_pretrained("wiusdy/vilt_saffal_model")
-        self.vilt_model_control_net = BlipForQuestionAnswering.from_pretrained("wiusdy/vilt_control_net")
-
         self.blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
         self.blip_model_saffal = BlipForQuestionAnswering.from_pretrained("wiusdy/blip_pretrained_saffal_fashion_finetuning")
         self.blip_model_control_net = BlipForQuestionAnswering.from_pretrained("wiusdy/blip_pretrained_control_net_fashion_finetuning")
@@ -17,27 +13,11 @@ class Inference:
         self.logger = logging.get_logger("transformers")
 
     def inference(self, image, text):
-        self.logger.info(f"Running inference for model ViLT Saffal")
-        ViLT_saffal_inference = self.__inference_vilt_saffal(image, text)
-        self.logger.info(f"Running inference for model ViLT Control Net")
-        ViLT_control_net_inference = self.__inference_vilt_control_net(image, text)
         self.logger.info(f"Running inference for model BLIP Saffal")
         BLIP_saffal_inference = self.__inference_saffal_blip(image, text)
         self.logger.info(f"Running inference for model BLIP Control Net")
         BLIP_control_net_inference = self.__inference_control_net_blip(image, text)
-        return BLIP_saffal_inference, BLIP_control_net_inference, ViLT_saffal_inference, ViLT_control_net_inference
-
-    def __inference_vilt_saffal(self, image, text):
-        encoding = self.vilt_processor(image, text, return_tensors="pt")
-        out = self.vilt_model_saffal.generate(**encoding)
-        generated_text = self.vilt_processor.decode(out[0], skip_special_tokens=True)
-        return f"{generated_text}"
-
-    def __inference_vilt_control_net(self, image, text):
-        encoding = self.vilt_processor(image, text, return_tensors="pt")
-        out = self.vilt_model_control_net.generate(**encoding)
-        generated_text = self.vilt_processor.decode(out[0], skip_special_tokens=True)
-        return f"{generated_text}"
+        return BLIP_saffal_inference, BLIP_control_net_inference
 
     def __inference_saffal_blip(self, image, text):
         encoding = self.blip_processor(image, text, return_tensors="pt")