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import streamlit as st |
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from transformers import pipeline |
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import time |
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st.set_page_config( |
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page_title="Cosmetic Review Analyst", |
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layout="wide", |
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initial_sidebar_state="expanded", |
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) |
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st.session_state.disable_watchdog = True |
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def load_css(): |
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st.markdown(""" |
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<style> |
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.reportview-container .main .block-container{ |
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max-width: 1200px; |
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padding-top: 2rem; |
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padding-bottom: 2rem; |
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} |
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.stTextInput textarea { |
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border-radius: 15px; |
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box-shadow: 0 2px 8px rgba(0,0,0,0.1); |
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} |
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.stProgress > div > div > div > div { |
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background-image: linear-gradient(to right, #ff6b6b, #ff8e53); |
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} |
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.st-bw { |
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background-color: #ffffff; |
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border-radius: 10px; |
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padding: 25px; |
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box-shadow: 0 4px 6px rgba(0,0,0,0.1); |
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} |
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</style> |
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""", unsafe_allow_html=True) |
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@st.cache_resource(show_spinner=False) |
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def load_models(): |
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summarizer = pipeline( |
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"summarization", |
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model="Falconsai/text_summarization", |
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max_length=200, |
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temperature=0.7 |
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) |
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classifier = pipeline( |
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"text-classification", |
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model="clb5114/EPR_emoclass_TinyBERT", |
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return_all_scores=True |
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) |
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return summarizer, classifier |
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def main(): |
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load_css() |
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st.title("💄 Cosmetic Review AI Analyst") |
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st.warning("⚠️ Please keep reviews under 200 words for optimal analysis") |
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user_input = st.text_area( |
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"Input cosmetic product review (Chinese/English supported)", |
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height=200, |
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placeholder="Example: This serum transformed my skin in just 3 days...", |
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help="Maximum 200 characters recommended" |
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) |
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if st.button("Start Analysis", use_container_width=True): |
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if not user_input.strip(): |
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st.error("⚠️ Please input valid review content") |
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return |
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with st.spinner('🔍 Analyzing...'): |
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try: |
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summarizer, classifier = load_models() |
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with st.expander("Original Review", expanded=True): |
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st.write(user_input) |
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summary = summarizer(user_input, max_length=200)[0]['summary_text'] |
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with st.container(): |
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col1, col2 = st.columns([1, 3]) |
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with col1: |
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st.subheader("📝 Summary") |
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with col2: |
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st.markdown(f"```\n{summary}\n```") |
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results = classifier(summary) |
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positive_score = results[0][1]['score'] |
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label = "Positive 👍" if positive_score > 0.5 else "Negative 👎" |
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with st.container(): |
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st.subheader("📊 Sentiment Analysis") |
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col1, col2 = st.columns(2) |
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with col1: |
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st.metric("Verdict", label) |
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st.write(f"Confidence: {positive_score:.2%}") |
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with col2: |
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progress_color = "#4CAF50" if label=="Positive 👍" else "#FF5252" |
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st.markdown(f""" |
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<div style=" |
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background: {progress_color}10; |
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border-radius: 10px; |
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padding: 15px; |
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"> |
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<div style="font-size: 14px; color: {progress_color}; margin-bottom: 8px;">Intensity</div> |
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<div style="height: 8px; background: #eee; border-radius: 4px;"> |
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<div style="width: {positive_score*100}%; height: 100%; background: {progress_color}; border-radius: 4px;"></div> |
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</div> |
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</div> |
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""", unsafe_allow_html=True) |
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except Exception as e: |
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st.error(f"Analysis failed: {str(e)}") |
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if __name__ == "__main__": |
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main() |
