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solving problemas
Browse files- app.py +1 -1
- inference.py +8 -11
app.py
CHANGED
@@ -7,7 +7,7 @@ inference = Inference()
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with gr.Blocks() as block:
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options = gr.Dropdown(choices=["Blip Saffal", "Blip
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# need to improve this one...
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txt = gr.Textbox(label="Insert a question..", lines=2)
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with gr.Blocks() as block:
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options = gr.Dropdown(choices=["Blip Saffal", "Blip CN"], label="Models", info="Select the model to use..", )
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# need to improve this one...
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txt = gr.Textbox(label="Insert a question..", lines=2)
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inference.py
CHANGED
@@ -6,20 +6,17 @@ class Inference:
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self.blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
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self.blip_model_saffal = BlipForQuestionAnswering.from_pretrained("wiusdy/blip_pretrained_saffal_fashion_finetuning")
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self.blip_model_control_net = BlipForQuestionAnswering.from_pretrained("wiusdy/blip_pretrained_control_net_fashion_finetuning")
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logging.set_verbosity_info()
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self.logger = logging.get_logger("transformers")
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def inference(self,
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self.logger.
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BLIP_control_net_inference = self.__inference_control_net_blip(image, text)
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return BLIP_control_net_inference
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def __inference_saffal_blip(self, image, text):
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encoding = self.blip_processor(image, text, return_tensors="pt")
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self.blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
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self.blip_model_saffal = BlipForQuestionAnswering.from_pretrained("wiusdy/blip_pretrained_saffal_fashion_finetuning")
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self.blip_model_control_net = BlipForQuestionAnswering.from_pretrained("wiusdy/blip_pretrained_control_net_fashion_finetuning")
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logging.set_verbosity_info()
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self.logger = logging.get_logger("transformers")
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def inference(self, selected, image, text):
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self.logger.info(f"selected model {selected}")
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if selected == "Blip Saffal":
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return self.__inference_saffal_blip(image, text)
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elif selected == "Blip CN":
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return self.__inference_control_net_blip(image, text)
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else:
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self.logger.warning("Please select a model to make the inference..")
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def __inference_saffal_blip(self, image, text):
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encoding = self.blip_processor(image, text, return_tensors="pt")
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