Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,113 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import requests
+import json
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+st.set_page_config(
+    page_title="Sentiment Analysis App",
+    page_icon="😊",
+    layout="centered"
+)
+
+st.title("Sentiment Analysis App")
+st.write("Enter text to analyze its sentiment using Hugging Face's API")
+
+# API credentials input
+api_key = st.text_input("Enter your Hugging Face API key:", type="password", help="Your Hugging Face API token")
+
+# Model selection
+model_options = {
+    "DistilBERT (SST-2)": "distilbert/distilbert-base-uncased-finetuned-sst-2-english",
+    "Twitter-roBERTa-base": "cardiffnlp/twitter-roberta-base-sentiment",
+    "BERT-base-multilingual": "nlptown/bert-base-multilingual-uncased-sentiment"
+}
+selected_model = st.selectbox("Select a sentiment analysis model:", options=list(model_options.keys()))
+
+# Text input area
+text_input = st.text_area("Enter text to analyze:", height=150)
+
+# Function to call the Hugging Face API
+def analyze_sentiment(text, model, api_key):
+    API_URL = f"https://api-inference.huggingface.co/models/{model}"
+    headers = {
+        "Authorization": f"Bearer {api_key}"
+    }
+    
+    payload = {
+        "inputs": text,
+    }
+    
+    try:
+        response = requests.post(API_URL, headers=headers, json=payload)
+        return response.json()
+    except Exception as e:
+        return {"error": str(e)}
+
+# Submit button
+if st.button("Analyze Sentiment"):
+    if not api_key:
+        st.error("Please enter your Hugging Face API key")
+    elif not text_input:
+        st.error("Please enter some text to analyze")
+    else:
+        with st.spinner("Analyzing sentiment..."):
+            selected_model_path = model_options[selected_model]
+            result = analyze_sentiment(text_input, selected_model_path, api_key)
+            
+            # Process and display results
+            try:
+                if "error" in result:
+                    st.error(f"Error: {result['error']}")
+                elif isinstance(result, list) and len(result) > 0:
+                    # Process the results
+                    if isinstance(result[0], list):
+                        items = result[0]
+                    else:
+                        items = result
+                    
+                    # Find the highest scoring sentiment
+                    highest_item = max(items, key=lambda x: x['score'])
+                    score = highest_item['score']
+                    label = highest_item['label'].lower()
+                    
+                    # Display emoji based on sentiment and score
+                    st.subheader("Sentiment:")
+                    col1, col2 = st.columns([1, 3])
+                    
+                    # Select emoji based on sentiment label and score
+                    if 'positive' in label or 'pos' in label or '5' in label or '4' in label:
+                        if score > 0.9:
+                            emoji = "😍"
+                        elif score > 0.7:
+                            emoji = "😁"
+                        else:
+                            emoji = "🙂"
+                        sentiment_text = f"Positive ({score:.2f})"
+                    elif 'negative' in label or 'neg' in label or '1' in label or '2' in label:
+                        if score > 0.9:
+                            emoji = "😡"
+                        elif score > 0.7:
+                            emoji = "😠"
+                        else:
+                            emoji = "☹"
+                        sentiment_text = f"Negative ({score:.2f})"
+                    else:  # neutral or '3' in label
+                        emoji = "😐"
+                        sentiment_text = f"Neutral ({score:.2f})"
+                    
+                    with col1:
+                        st.markdown(f"<h1 style='font-size:4rem; text-align:center;'>{emoji}</h1>", unsafe_allow_html=True)
+                    with col2:
+                        st.markdown(f"<h2>{sentiment_text}</h2>", unsafe_allow_html=True)
+                        
+                        # Add confidence meter
+                        st.progress(score)
+                else:
+                    st.warning("Unexpected response format. Please check your API key and try again.")
+                    st.json(result)
+            except Exception as e:
+                st.error(f"Error processing results: {str(e)}")
+                st.json(result)
+
+# Footer
+st.markdown("---")
+st.markdown("Built with Streamlit and Hugging Face API")