connect to model

app.py

@@ -1,7 +1,25 @@
 import gradio as gr
+from transformers import pipeline
 
-def greet(name):
-    return "Hello " + name + "!!"
+generator = pipeline("visual-question-answering", model="jihadzakki/blip1-medvqa")
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+def format_answer(image, question):
+    result = generator(image, question)
+    print(result)  # Print the result to see its structure
+    predicted_answer = result[0].get('answer', 'No answer found')  # Adjust this key if necessary
+
+    return f"Predicted Answer: {predicted_answer}"
+
+VisualQAApp = gr.Interface(
+    fn=format_answer,
+    inputs=[
+        gr.Image(label="Upload image", type="pil"),
+        gr.Textbox(label="Question"),
+    ],
+    outputs=[gr.Textbox(label="Answer")],
+    title="Visual Question Answering using BLIP Model",
+    description="VQA",
+    allow_flagging="never"
+)
+
+VisualQAApp.launch(share=True)