Update app.py

app.py

@@ -18,10 +18,13 @@ import io
 app = FastAPI()
 
 # ✅ Load AI Models
-vqa_pipeline = pipeline("image-to-text", model="Salesforce/blip-vqa-base")
-code_generator = pipeline("text-generation", model="openai-community/gpt2-medium")
-table_analyzer = pipeline("table-question-answering", model="google/tapas-large-finetuned-wtq")
-qa_pipeline = pipeline("text2text-generation",model="google/flan-t5-large",tokenizer="google/flan-t5-large",use_fast=True)
+from transformers import pipeline
+
+qa_pipeline = pipeline("text2text-generation",model="google/flan-t5-large",tokenizer="google/flan-t5-large",use_fast=True,device=0)
+table_analyzer = pipeline("table-question-answering",model="google/tapas-large-finetuned-wtq",tokenizer="google/tapas-large-finetuned-wtq",use_fast=True,device=0)
+code_generator = pipeline("text-generation",model="openai-community/gpt2-medium",tokenizer="openai-community/gpt2-medium",use_fast=True,device=0)
+vqa_pipeline = pipeline("image-to-text",model="Salesforce/blip-vqa-base",device=0 )
+
 
 # ✅ Function to truncate text to 450 tokens
 def truncate_text(text, max_tokens=450):
@@ -167,7 +170,7 @@ viz_interface = gr.Interface(
 )
 
 # ✅ Mount Gradio Interfaces
-demo = gr.TabbedInterface([doc_interface, img_interface, viz_interface], ["Document QA", "Image QA", "Data Visualization"])
+demo = gr.TabbedInterface([doc_interface, img_interface, viz_interface], ["Document QA", "Image QA"])
 app = gr.mount_gradio_app(app, demo, path="/")
 
 @app.get("/")