app added

app.py

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+from transformers import BlipProcessor, BlipForQuestionAnswering 
+import torch 
+import gradio as gr 
+from PIL import Image
+
+# Load the processor and model
+processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
+model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base")
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+def vqa_answer(image, question):
+    # Preprocess the inputs
+    inputs = processor(image, question, return_tensors="pt").to(device)
+    
+    # Generate the answer
+    with torch.no_grad():
+        generated_ids = model.generate(**inputs)
+        answer = processor.decode(generated_ids[0], skip_special_tokens=True)
+    
+    return answer
+
+# Define the input components
+image_input = gr.components.Image(type="pil", label="Upload an Image")
+question_input = gr.components.Textbox(lines=1, placeholder="Enter your question here...", label="Question")
+
+# Define the output component
+answer_output = gr.components.Textbox(label="Answer")
+
+# Create the interface
+iface = gr.Interface(
+    fn=vqa_answer,
+    inputs=[image_input, question_input],
+    outputs=answer_output,
+    title="Visual Question Answering App",
+    description="Ask a question about the uploaded image.",
+    article="This app uses the BLIP model to answer questions about images."
+)
+
+# Launch the app
+iface.launch(share=True)

requirements.txt

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+transformers
+gradio
+torch
+pillow
+accelerate