Update app.py

app.py

@@ -1,28 +1,51 @@
+# import gradio as gr
+# from transformers.utils import logging
+# logging.set_verbosity_error()
+
+# import warnings
+# warnings.filterwarnings("ignore", message="Using the model-agnostic default `max_length`")
+
+# from transformers import BlipForQuestionAnswering
+# from transformers import AutoProcessor
+
+# def qa(image, question):
+#     model = BlipForQuestionAnswering.from_pretrained(
+#     "./models/Salesforce/blip-vqa-base")
+#     processor = AutoProcessor.from_pretrained(
+#     "./models/Salesforce/blip-vqa-base")
+    
+#     inputs = processor(image, question, return_tensors="pt")
+
+#     out = model.generate(image, question)
+
+#     result  = processor.decode(out[0], skip_special_tokens=True)
+#     return result
+
+# # def greet(name):
+# #     return "Hello " + name + "!!"
+
+# iface = gr.Interface(fn=qa, inputs=["image","text"], outputs="textbox")
+# iface.launch()
+
+
+
 import gradio as gr
 from transformers.utils import logging
-logging.set_verbosity_error()
+from transformers import BlipForQuestionAnswering, AutoProcessor
 
+logging.set_verbosity_error()
 import warnings
 warnings.filterwarnings("ignore", message="Using the model-agnostic default `max_length`")
 
-from transformers import BlipForQuestionAnswering
-from transformers import AutoProcessor
-
 def qa(image, question):
-    model = BlipForQuestionAnswering.from_pretrained(
-    "./models/Salesforce/blip-vqa-base")
-    processor = AutoProcessor.from_pretrained(
-    "./models/Salesforce/blip-vqa-base")
+    model = BlipForQuestionAnswering.from_pretrained("./models/Salesforce/blip-vqa-base")
+    processor = AutoProcessor.from_pretrained("./models/Salesforce/blip-vqa-base")
+    
+    inputs = processor(image=image, question=question, return_tensors="pt")
+    out = model.generate(**inputs)
+    result = processor.decode(out[0], skip_special_tokens=True)
     
-    inputs = processor(image, question, return_tensors="pt")
-
-    out = model.generate(image, question)
-
-    result  = processor.decode(out[0], skip_special_tokens=True)
     return result
 
-# def greet(name):
-#     return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=qa, inputs=["image","text"], outputs="textbox")
+iface = gr.Interface(fn=qa, inputs=["image", "text"], outputs="textbox")
 iface.launch()