coderwithcoffee/EXPERIMENTS

1import streamlit as st2import requests3import json4 5st.set_page_config(6    page_title="Sentiment Analysis App",7    page_icon="😊",8    layout="centered"9)10 11st.title("Sentiment Analysis App")12st.write("Enter text to analyze its sentiment using Hugging Face's API")13 14# API credentials input15api_key = st.text_input("Enter your Hugging Face API key:", type="password", help="Your Hugging Face API token")16 17# Model selection18model_options = {19    "DistilBERT (SST-2)": "distilbert/distilbert-base-uncased-finetuned-sst-2-english",20    "Twitter-roBERTa-base": "cardiffnlp/twitter-roberta-base-sentiment",21    "BERT-base-multilingual": "nlptown/bert-base-multilingual-uncased-sentiment"22}23selected_model = st.selectbox("Select a sentiment analysis model:", options=list(model_options.keys()))24 25# Text input area26text_input = st.text_area("Enter text to analyze:", height=150)27 28# Function to call the Hugging Face API29def analyze_sentiment(text, model, api_key):30    API_URL = f"https://api-inference.huggingface.co/models/{model}"31    headers = {32        "Authorization": f"Bearer {api_key}"33    }34    35    payload = {36        "inputs": text,37    }38    39    try:40        response = requests.post(API_URL, headers=headers, json=payload)41        return response.json()42    except Exception as e:43        return {"error": str(e)}44 45# Submit button46if st.button("Analyze Sentiment"):47    if not api_key:48        st.error("Please enter your Hugging Face API key")49    elif not text_input:50        st.error("Please enter some text to analyze")51    else:52        with st.spinner("Analyzing sentiment..."):53            selected_model_path = model_options[selected_model]54            result = analyze_sentiment(text_input, selected_model_path, api_key)55            56            # Process and display results57            try:58                if "error" in result:59                    st.error(f"Error: {result['error']}")60                elif isinstance(result, list) and len(result) > 0:61                    # Process the results62                    if isinstance(result[0], list):63                        items = result[0]64                    else:65                        items = result66                    67                    # Find the highest scoring sentiment68                    highest_item = max(items, key=lambda x: x['score'])69                    score = highest_item['score']70                    label = highest_item['label'].lower()71                    72                    # Display emoji based on sentiment and score73                    st.subheader("Sentiment:")74                    col1, col2 = st.columns([1, 3])75                    76                    # Select emoji based on sentiment label and score77                    if 'positive' in label or 'pos' in label or '5' in label or '4' in label:78                        if score > 0.9:79                            emoji = "😍"80                        elif score > 0.7:81                            emoji = "😁"82                        else:83                            emoji = "🙂"84                        sentiment_text = f"Positive ({score:.2f})"85                    elif 'negative' in label or 'neg' in label or '1' in label or '2' in label:86                        if score > 0.9:87                            emoji = "😡"88                        elif score > 0.7:89                            emoji = "😠"90                        else:91                            emoji = "☹"92                        sentiment_text = f"Negative ({score:.2f})"93                    else:  # neutral or '3' in label94                        emoji = "😐"95                        sentiment_text = f"Neutral ({score:.2f})"96                    97                    with col1:98                        st.markdown(f"<h1 style='font-size:4rem; text-align:center;'>{emoji}</h1>", unsafe_allow_html=True)99                    with col2:100                        st.markdown(f"<h2>{sentiment_text}</h2>", unsafe_allow_html=True)101                        102                        # Add confidence meter103                        st.progress(score)104                else:105                    st.warning("Unexpected response format. Please check your API key and try again.")106                    st.json(result)107            except Exception as e:108                st.error(f"Error processing results: {str(e)}")109                st.json(result)110 111# Footer112st.markdown("---")113st.markdown("Built with Streamlit and Hugging Face API")