Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import emoji
+import requests
+
+# Convert Google Drive link to a direct image link
+logo_url = "https://drive.google.com/uc?export=view&id=1cKAxqifPx3ytEsjpzFuHs7NGe7Ml2toW"
+
+# Load the emotion detection pipeline (Hugging Face model)
+emotion_detector = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
+
+# Function to convert emojis to text
+def emoji_to_text(text):
+    return emoji.demojize(text, delimiters=(" ", " "))  # Convert emojis to text descriptions
+
+# Function to detect emotion from text
+def detect_emotion(text):
+    text_with_emojis = emoji_to_text(text)  # Convert emojis to text
+    result = emotion_detector(text_with_emojis)[0]  # Use the emotion detection model
+    return result['label']
+
+# Function to append emoji to input text
+def append_emoji(text, selected_emoji):
+    return text + selected_emoji  # Append the selected emoji to the text input
+
+# Function to get mood description from slider value
+def get_mood_from_slider(mood_value):
+    if mood_value < 0.1:
+        return "very sad"
+    elif mood_value < 0.2:
+        return "sad"
+    elif mood_value < 0.3:
+        return "slightly sad"
+    elif mood_value < 0.4:
+        return "neutral"
+    elif mood_value < 0.5:
+        return "calm"
+    elif mood_value < 0.6:
+        return "slightly happy"
+    elif mood_value < 0.7:
+        return "happy"
+    elif mood_value < 0.8:
+        return "very happy"
+    elif mood_value < 0.9:
+        return "excited"
+    else:
+        return "ecstatic"
+
+# Function to get tempo description from slider value
+def get_tempo_from_slider(tempo_value):
+    if tempo_value < 0.1:
+        return "very slow"
+    elif tempo_value < 0.2:
+        return "slow"
+    elif tempo_value < 0.3:
+        return "moderately slow"
+    elif tempo_value < 0.4:
+        return "medium slow"
+    elif tempo_value < 0.5:
+        return "medium"
+    elif tempo_value < 0.6:
+        return "medium fast"
+    elif tempo_value < 0.7:
+        return "fast"
+    elif tempo_value < 0.8:
+        return "very fast"
+    elif tempo_value < 0.9:
+        return "rapid"
+    else:
+        return "extremely fast"
+
+# Function to search YouTube for a video based on mood and tempo
+def search_youtube_music(mood_value, tempo_value):
+    mood_query = get_mood_from_slider(mood_value)
+    tempo_query = get_tempo_from_slider(tempo_value)
+    search_query = f"{mood_query} {tempo_query} music"
+    
+    # YouTube API request (API_KEY to be added if required)
+    params = {
+        "part": "snippet",
+        "q": search_query,
+        "key": "YOUR_API_KEY",  # Replace with a valid YouTube API key
+        "type": "video",
+        "videoCategoryId": "10",  # Music category
+        "maxResults": 1  # Only get 1 result
+    }
+    
+    response = requests.get("https://www.googleapis.com/youtube/v3/search", params=params)
+    if response.status_code != 200:
+        return "Error fetching YouTube data"
+    
+    json_response = response.json()
+    if "items" not in json_response or len(json_response["items"]) == 0:
+        return "No videos found"
+    
+    video_id = json_response["items"][0]["id"]["videoId"]
+    video_url = f"https://www.youtube.com/embed/{video_id}"
+    
+    youtube_video_link = f"https://www.youtube.com/watch?v={video_id}"
+    iframe_html = f'<iframe width="560" height="315" src="{video_url}" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>'
+    
+    return iframe_html
+
+# Streamlit UI
+st.set_page_config(page_title="Emotion Detector & Music Finder", layout="centered")
+
+# Display logo
+st.image(logo_url, use_column_width=True)
+
+st.title("🎭 AI Emotion Detector & Music Finder")
+
+# Tabs for emotion detection and music finder
+tab1, tab2 = st.tabs(["Emotion Detection", "Mood & Tempo Music Finder"])
+
+# Tab 1: Emotion Detection
+with tab1:
+    st.subheader("Emotion Detection from Text")
+
+    text_input = st.text_input("Enter your text here", placeholder="Type something here...")
+    emoji_list = ["😊", "😢", "😡", "😂", "😍", "😎", "🤔", "😴", "👍", "🎉"]
+    selected_emoji = st.selectbox("Choose an emoji to add", options=emoji_list)
+
+    if st.button("Add Emoji to Text"):
+        text_input = append_emoji(text_input, selected_emoji)
+
+    if st.button("Analyze Emotion"):
+        if text_input:
+            detected_emotion = detect_emotion(text_input)
+            st.success(f"Detected Emotion: **{detected_emotion}**")
+        else:
+            st.error("Please enter some text before analyzing.")
+
+# Tab 2: Mood & Tempo Music Finder
+with tab2:
+    st.subheader("Find Music Based on Mood and Tempo")
+    
+    mood_slider = st.slider("Mood", min_value=0.0, max_value=1.0, step=0.1)
+    tempo_slider = st.slider("Tempo", min_value=0.0, max_value=1.0, step=0.1)
+
+    if st.button("Find Music"):
+        youtube_embed = search_youtube_music(mood_slider, tempo_slider)
+        st.markdown(youtube_embed, unsafe_allow_html=True)