Update app.py

app.py

@@ -1,30 +1,26 @@
 import streamlit as st
-from transformers import pipeline
 
-# Load the text classification model pipeline
-classifier = pipeline("text-classification",model='slliac/my_model', return_all_scores=True)
+# Title
+st.title("Streamlit Demo: Buttons, Slider, Image & File Uploader")
 
-# Streamlit application title
-st.title("Text Classification for you")
-st.write("Classification for 6 emotions: sadness, joy, love, anger, fear, surprise")
+# Button Demo
+if st.button("Click Me!"):
+    st.write("Button Clicked!")
 
-# Text input for user to enter the text to classify
-text = st.text_area("Enter the text to classify", "")
+# Slider Demo
+slider_value = st.slider("Select a value", min_value=0, max_value=100, value=50)
+st.write(f"Slider value: {slider_value}")
 
-# Perform text classification when the user clicks the "Classify" button
-if st.button("Classify"):
-    # Perform text classification on the input text
-    results = classifier(text)[0]
+# Text Input Demo
+user_text = st.text_input("Enter some text")
+st.write(f"You entered: {user_text}")
 
-    # Display the classification result
-    max_score = float('-inf')
-    max_label = ''
+# Image Uploader Demo
+uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"])
+if uploaded_image is not None:
+    st.image(uploaded_image, caption="Uploaded Image", use_container_width=True)
 
-    for result in results:
-        if result['score'] > max_score:
-            max_score = result['score']
-            max_label = result['label']
-
-    st.write("Text:", text)
-    st.write("Label:", max_label)
-    st.write("Score:", max_score)
+# File Uploader Demo
+uploaded_file = st.file_uploader("Upload a File", type=["txt", "csv", "pdf"])
+if uploaded_file is not None:
+    st.write("Uploaded File Name:", uploaded_file.name)