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app.py

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+import gradio as gr
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from PIL import Image
+
+# Load BLIP model and processor
+processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-vqa-base")
+
+def answer_question(image, question):
+    """
+    Generates an answer based on the given image and question.
+    """
+    try:
+        if not question.strip():
+            return "Please enter a question."
+
+        # Prepare inputs for the model
+        inputs = processor(images=image, text=question, return_tensors="pt")
+
+        # Generate answer
+        outputs = model.generate(**inputs)
+        answer = processor.decode(outputs[0], skip_special_tokens=True)
+
+        return answer
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=answer_question,  # Function to process image + question
+    inputs=[gr.Image(type="pil"), gr.Textbox(label="Ask a question about the image")],
+    outputs="text",
+    title="Visual Question Answering with BLIP",
+    description="Upload an image and ask a question about it. The model will generate an answer."
+)
+
+iface.launch()

requirement.txt

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+gradio
+transformers
+pillow