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app.py

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+import gradio as gr
+from transformers.utils import logging
+from transformers import BlipForQuestionAnswering, AutoProcessor
+from PIL import Image
+logging.set_verbosity_error()
+
+# Load the model and processor
+model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base")
+processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base")
+
+# Define a function to process inputs and generate outputs
+def predict(image, question):
+    inputs = processor(image, question, return_tensors="pt")
+    out = model.generate(**inputs)
+    answer = processor.decode(out[0], skip_special_tokens=True)
+    return answer
+
+# Create the Gradio interface with custom Markdown and HTML formatting
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Textbox(label="Question", placeholder="Ask a question about the image")
+    ],
+    outputs=gr.Textbox(label="Answer"),
+    description="<h1 style='text-align: center; font-family: Times New Roman;'>Visual Question Answering</h1> \
+                 <p style='text-align: center; font-family: Times New Roman;'><strong>Model name:</strong> Salesforce/blip-vqa-base</p> \
+                 <p style='text-align: center; font-family: Times New Roman;'><strong>Made by:</strong> MD MAHMUDUN NABI</p>"
+)
+
+# Launch the Gradio interface
+demo.launch()

requirements.txt

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+gradio
+transformers
+pillow