Create appImage.py

appImage.py

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+from fastapi import FastAPI
+from fastapi.responses import RedirectResponse
+import gradio as gr
+from transformers import pipeline, ViltProcessor, ViltForQuestionAnswering, AutoTokenizer, AutoModelForCausalLM
+from PIL import Image
+import torch
+import fitz  # PyMuPDF for PDF
+app = FastAPI()
+
+# ========== Image QA Setup ==========
+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]
+# ========== Gradio Interfaces ==========
+
+img_interface = gr.Interface(
+    fn=answer_question_from_image,
+    inputs=[gr.Image(label="Upload Image"), gr.Textbox(label="Ask a Question")],
+    outputs="text",
+    title="Image Question Answering"
+)
+# ========== Combine and Mount ==========
+demo = gr.TabbedInterface( img_interface , "Image QA")
+app = gr.mount_gradio_app(app, demo, path="/")
+@app.get("/")
+def root():
+    return RedirectResponse(url="/")