Update appImage.py

appImage.py

@@ -1,4 +1,4 @@
-from fastapi import FastAPI
+"""from fastapi import FastAPI
 from fastapi.responses import RedirectResponse
 import gradio as gr
 from transformers import pipeline, ViltProcessor, ViltForQuestionAnswering, AutoTokenizer, AutoModelForCausalLM
@@ -31,4 +31,21 @@ demo = gr.TabbedInterface( img_interface , "Image QA")
 app = gr.mount_gradio_app(app, demo, path="/")
 @app.get("/")
 def root():
-    return RedirectResponse(url="/")
+    return RedirectResponse(url="/") """
+from transformers import ViltProcessor, ViltForQuestionAnswering
+import torch
+
+# Load image QA model once
+vqa_processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
+vqa_model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
+
+def answer_question_from_image(image, question):
+    if image is None or not question.strip():
+        return "Please upload an image and ask a question."
+    
+    inputs = vqa_processor(image, question, return_tensors="pt")
+    with torch.no_grad():
+        outputs = vqa_model(**inputs)
+    
+    predicted_id = outputs.logits.argmax(-1).item()
+    return vqa_model.config.id2label[predicted_id]