Fixes:#2 Add DeepFace-based emotion detection with YouTube music recommendation

code/new_models/app_local_streamlit_deepface.py

@@ -0,0 +1,220 @@
+# Required versions:
+# protobuf==3.20.3, tensorflow==2.10
+
+import streamlit as st
+import cv2
+from deepface import DeepFace  
+import numpy as np
+import webbrowser
+import requests
+import re
+import os
+import time
+
+# App config
+st.set_page_config(page_title="Emotion-Based Music Player", layout="centered")
+st.title("Facial Emotion Recognition App")
+st.write("This app detects your facial expression, displays the predicted emotion, and plays a suitable song.")
+
+#Footer
+st.markdown(
+    """
+    <style>
+    .footer {
+        position: fixed;
+        left: 0;
+        bottom: 0;
+        width: 100%;
+        background-color: #f1f1f1;
+        color: #000000;
+        text-align: center;
+        padding: 12px;
+        font-size: 15px;
+        box-shadow: 0 -1px 4px rgba(0,0,0,0.1);
+        border-top: 1px solid #ccc;
+        margin-top: 50px;
+    }
+    </style>
+
+    <div class="footer">
+        <a href="https://github.com/SGCODEX/Music-Recommendation-Using-Facial-Expressions.git" style="color: #0072E3; text-decoration: underline;">
+        Project by SGCODEX. Visit us and give this project a ⭐. Proudly part of open source programs like SWOC, IEEE-IGDTUW, GSSOC and more!!
+        </a>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+
+#st.title("🧠 Detected Emotion KPI")
+
+# App state
+if "last_emotion" not in st.session_state:
+    st.session_state.last_emotion = "Neutral"
+if "show_video" not in st.session_state:
+    st.session_state.show_video = False
+
+# Function to detect emotion
+def detect_emotion(frame):
+    try:
+        results = DeepFace.analyze(frame, actions=["emotion"], enforce_detection=False)
+
+        # Handle single or multiple face results
+        if not isinstance(results, list):
+            results = [results]
+
+        for result in results:
+            emotion = result['dominant_emotion']
+            region = result['region']
+            x, y, w, h = region['x'], region['y'], region['w'], region['h']
+
+            # Draw bounding box around the detected face 
+            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+
+            # label the detected emotion above the face 
+            cv2.putText(frame, emotion, (x, y - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 0), 2)
+
+        # Assume first emotion as dominant for app state
+        st.session_state.last_emotion = results[0]['dominant_emotion']
+
+    except Exception as e:
+        emotion = st.session_state.last_emotion
+        print("DeepFace failed:", e)
+
+    return frame, st.session_state.last_emotion
+
+# ------------------------------
+# 🎥 Live Camera Detection Mode
+# ------------------------------
+if not st.session_state.show_video:
+    col1, col2 = st.columns([1, 2])
+
+    with col1:
+        st.subheader("📊 Current Emotion")
+        emotion_placeholder = st.empty()
+
+    cap = cv2.VideoCapture(0)
+    capture = False
+
+    with col2:
+        st.subheader("📷 Live Feed")
+        image_placeholder = st.empty()
+        st.markdown("<br>", unsafe_allow_html=True)
+        capture = st.button("🎵 Play Song on Captured Emotion")
+
+
+    st.markdown("---")
+
+    while cap.isOpened() and not capture:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        frame = cv2.resize(frame, (320, 240))
+        frame, detected_emotion = detect_emotion(frame)
+        st.session_state.last_emotion = detected_emotion
+
+        # Update live KPI and video feed
+
+        emotion_colors = {
+            "Happy": "#DFF2BF",
+            "Sad": "#FFBABA",
+            "Angry": "#FFAAAA",
+            "Surprise": "#FFFFBA",
+            "Neutral": "#E0E0E0",
+            "Fear": "#D0BAFF",
+            "Disgust": "#B0FFBA"
+        }
+
+        bg_color = emotion_colors.get(detected_emotion, "#f9fff9")
+
+        emotion_placeholder.markdown(
+            f"""
+            <div style="
+                display: inline-block;
+                padding: 10px 24px;
+                border: 2px solid #000000;
+                border-radius: 14px;
+                background-color: {bg_color};
+                font-size: 20px;
+                font-weight: 600;
+                color: #333;
+                text-align: center;
+                margin-top: 10px;
+                min-width: 120px;
+            ">
+                {detected_emotion}
+            </div>
+            """,
+            unsafe_allow_html=True
+        )
+
+
+        image_placeholder.image(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), channels="RGB")
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+        time.sleep(0.1)  # Limit refresh rate
+
+    cap.release()
+    st.session_state.show_video = True  # switch mode
+
+# ------------------------------
+# 🎧 Play Song For Detected Mood
+# ------------------------------
+if st.session_state.show_video:
+    st.markdown("## 🎧 Now Playing Music For Your Mood")
+    st.markdown(f"**Detected Mood:** `{st.session_state.last_emotion}`")
+
+    if st.button("🔁 Detect Emotions Again"):
+        st.session_state.show_video = False
+        st.rerun()
+
+    search_query = f"https://www.youtube.com/results?search_query={st.session_state.last_emotion}+background+tunes"
+
+    # to fetch the search results page
+    response = requests.get(search_query)
+
+    # HTTP status code 200 = request was successful 
+    if response.status_code != 200:
+        print("Failed to retrieve YouTube search results. Status code:", response.status_code)
+
+    html_content = response.text
+
+    match = re.search(r'/watch\?v=([^\"]+)', html_content)
+    if match:
+        video_id = match.group(1)
+        #video_url = f"https://www.youtube.com/watch?v={video_id}"
+        video_url = f"https://www.youtube.com/watch?v={video_id.encode('utf-8').decode('unicode_escape')}"
+
+        # printing the video URL for debugging purposes
+        st.video(video_url)
+        print("Opening YouTube video:", video_url)
+
+
+# Benchmark: Compare DeepFace Inference Time (Optional)
+with st.expander("📊 Benchmark Inference Time"):
+    if st.button("📸 Capture Frame and Benchmark DeepFace"):
+        cap = cv2.VideoCapture(0)
+        ret, frame = cap.read()
+        cap.release()
+
+        if ret:
+            start_time = time.time()
+            try:
+                result = DeepFace.analyze(frame, actions=["emotion"], enforce_detection=False)
+                end_time = time.time()
+
+                detected_emotion = result[0]['dominant_emotion']
+                st.image(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), caption="Captured Frame", channels="RGB")
+                st.success(f"✅ DeepFace Inference Time: `{round(end_time - start_time, 3)}s`")
+                st.write("🎭 Detected Emotion:", detected_emotion)
+            except Exception as e:
+                st.error(f"❌ DeepFace failed: {str(e)}")
+        else:
+            st.error("❌ Failed to capture frame from webcam.")
+
+
+
+
+