Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+
+# Load Hugging Face models
+@st.cache_resource
+def load_image_classifier():
+    return pipeline("image-classification", model="google/vit-base-patch16-224")
+
+@st.cache_resource
+def load_text_classifier():
+    return pipeline("sentiment-analysis")  # Default model for sentiment analysis
+
+# Initialize models
+image_classifier = load_image_classifier()
+text_classifier = load_text_classifier()
+
+# App title and navigation
+st.title("Hugging Face Classification App")
+st.sidebar.title("Choose Task")
+task = st.sidebar.selectbox("Select a task", ["Image Classification", "Text Classification"])
+
+if task == "Image Classification":
+    st.header("Image Classification")
+    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        # Display uploaded image
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        # Classify the image
+        if st.button("Classify Image"):
+            with st.spinner("Classifying..."):
+                results = image_classifier(image)
+                st.subheader("Classification Results")
+                for result in results:
+                    st.write(f"**{result['label']}**: {result['score']:.2f}")
+
+elif task == "Text Classification":
+    st.header("Text Classification")
+    text_input = st.text_area("Enter text for classification", "Streamlit is an amazing tool!")
+    
+    # Classify the text
+    if st.button("Classify Text"):
+        with st.spinner("Classifying..."):
+            results = text_classifier(text_input)
+            st.subheader("Classification Results")
+            for result in results:
+                st.write(f"**{result['label']}**: {result['score']:.2f}")
+
+st.write("Powered by Streamlit and Hugging Face 🤗")