Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+
+# Load the text classification model
+classifier = pipeline("text-generation", model="Steven-GU-Yu-Di/Visual-Question-Answering")
+
+# Create a Streamlit app
+st.title("Image and Text Classification")
+
+# Sidebar for user inputs
+st.sidebar.title("Input")
+uploaded_image = st.sidebar.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
+text_input = st.sidebar.text_input("Enter Text Description")
+
+# Function to classify image and text
+def classify(image, text):
+    if image is not None and text:
+        image = Image.open(image)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+        st.write("Text Description:", text)
+        result = classifier(text)
+        st.write("Classification Result:")
+        st.write(result)
+
+# Button to trigger classification
+if st.sidebar.button("Classify"):
+    classify(uploaded_image, text_input)