Create app.py

app.py

@@ -0,0 +1,26 @@
+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(inputs):
+    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(**inputs)
+
+    return processor.decode(out[0], skip_special_tokens=True)
+
+# def greet(name):
+#     return "Hello " + name + "!!"
+
+iface = gr.Interface(fn=qa, inputs=["image","text"], outputs="text")
+iface.launch()