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import streamlit as st
from transformers import pipeline
classifier = pipeline(task="text-classification", model="SamLowe/roberta-base-go_emotions", top_k=None)
st.set_page_config(
page_title="Emotion Detection",
page_icon=":bar_chart:",
layout="centered",
)
st.markdown(
"""
<style>
.stButton > button {
background-color: #4CAF50;
color: white;
font-size: 18px;
padding: 10px 20px;
border: none;
cursor: pointer;
}
.stButton > button:hover {
background-color: #86D8DB;
}
.stApp {
background-color: #73D9C8; /* Background color */
}
</style>
""",
unsafe_allow_html=True,
)
st.title("🎭 Emotion Detection")
st.markdown("Choose the input type and enter a sentence or upload an image to classify emotions.")
input_type = st.radio("Select Input Type", ("Text", "Image"))
if input_type == "Text":
user_input = st.text_area("Enter a sentence:")
uploaded_image = None
else:
uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
user_input = ""
if st.button("Analyze"):
with st.spinner("Analyzing..."):
if input_type == "Text" and user_input:
model_outputs = classifier(user_input)
st.subheader("Emotion Classification Results (Text):")
elif input_type == "Image" and uploaded_image is not None:
st.image(uploaded_image, use_column_width=True, caption="Uploaded Image")
model_outputs = classifier("Analyze this image.")
st.subheader("Emotion Classification Results (Image):")
else:
st.warning("Please enter a sentence or upload an image to analyze.")
for label_info in model_outputs[0]:
label = label_info["label"]
score = label_info["score"]
st.write(f"- {label}: {score:.4f}")
if st.button("Clear"):
user_input = ""
uploaded_image = None
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