Update app.py

app.py

@@ -1,41 +1,20 @@
-# import part
 import streamlit as st
 from transformers import pipeline
-from PIL import Image
-import io
-
-# function part
-def generate_image_caption(image):
-    """Generates a caption for the given image using a pre-trained model."""
-    img2caption = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
-       
-    # Generate caption
-    result = img2caption(image)
-    return result[0]['generated_text']
-
-# text2story
-def text2story(text):
-    pipe = pipeline("text-generation", model="pranavpsv/genre-story-generator-v2")
-    story_text = pipe(text)[0]['generated_text']
-    return story_text
 
 def main():
-    # App title
-    st.title("Streamlit Demo on Hugging Face")
+    sentiment_pipeline = pipeline(model="distilbert-base-uncased-finetuned-sst-2-english")
 
-    # Write some text
-    st.write("Welcome to a demo app showcasing basic Streamlit components!")
-    
-    uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+    st.title("Sentiment Analysis with HuggingFace Spaces")
+    st.write("Enter a sentence to analyze its sentiment:")
 
-    if uploaded_image is not None:
-        image = Image.open(uploaded_image).convert("RGB")
-        st.image(image, caption="Uploaded Image", use_column_width=True)
+    user_input = st.text_input("")
+    if user_input:
+        result = sentiment_pipeline(user_input)
+        sentiment = result[0]["label"]
+        confidence = result[0]["score"]
 
-        # Stage 1: Image to Text
-        st.text('Processing img2text...')
-        image_caption = generate_image_caption(image)
-        st.write(image_caption)
+        st.write(f"Sentiment: {sentiment}")
+        st.write(f"Confidence: {confidence:.2f}")
 
 if __name__ == "__main__":
     main()