ss

app.py

@@ -86,6 +86,33 @@ except Exception as e:
 # Create music analyzer instance
 music_analyzer = MusicAnalyzer()
 
+# Define emotion and theme lexicons for content analysis
+emotion_lexicons = {
+    "happy": ["joy", "happy", "smile", "laugh", "light", "bright", "sun", "dance", "celebrate", "glow", "warm"],
+    "sad": ["cry", "tear", "pain", "loss", "grief", "dark", "alone", "miss", "gone", "sorrow", "heart", "break"],
+    "calm": ["peace", "quiet", "still", "gentle", "soft", "slow", "breath", "serene", "tranquil", "relax", "flow"],
+    "energetic": ["run", "fast", "beat", "pulse", "jump", "fire", "alive", "spark", "rush", "wild", "free"],
+    "tense": ["fear", "worry", "wait", "edge", "grip", "tight", "storm", "break", "shadow", "threat", "doubt"],
+    "nostalgic": ["memory", "remember", "past", "time", "ago", "once", "childhood", "return", "old", "familiar", "home"],
+    "reflective": ["think", "ponder", "wonder", "question", "search", "mind", "deep", "self", "mirror", "path", "journey"],
+    "triumphant": ["win", "rise", "stand", "overcome", "above", "victory", "summit", "conquer", "champion", "succeed"],
+    "yearning": ["want", "need", "desire", "reach", "seek", "dream", "hope", "wish", "long", "hunger", "thirst"],
+    "peaceful": ["calm", "rest", "still", "quiet", "harmony", "balance", "ease", "gentle", "soft", "float", "drift"]
+}
+
+theme_lexicons = {
+    "love": ["love", "heart", "touch", "together", "hold", "kiss", "embrace", "feel", "close", "intimate", "passion"],
+    "loss": ["gone", "away", "empty", "missing", "leave", "without", "never", "forever", "lost", "memory", "shadow"],
+    "freedom": ["free", "fly", "open", "release", "escape", "chain", "break", "boundless", "space", "breathe", "wings"],
+    "triumph": ["victory", "overcome", "win", "rise", "mountain", "climb", "top", "struggle", "strength", "succeed"],
+    "reflection": ["mirror", "water", "see", "self", "face", "look", "inside", "truth", "reality", "soul", "mind"],
+    "journey": ["road", "path", "walk", "step", "travel", "distance", "far", "way", "wander", "search", "find"],
+    "time": ["clock", "moment", "second", "hour", "pass", "wait", "forever", "instant", "eternity", "memory", "future"],
+    "conflict": ["fight", "battle", "against", "oppose", "between", "war", "struggle", "clash", "resist", "enemy"],
+    "nature": ["earth", "wind", "fire", "water", "sky", "tree", "flower", "mountain", "river", "ocean", "stars"],
+    "change": ["transform", "become", "different", "shift", "turn", "evolve", "grow", "new", "begin", "end", "cycle"]
+}
+
 # Process uploaded audio file
 def process_audio(audio_file):
     if audio_file is None:
@@ -190,7 +217,7 @@ def process_audio(audio_file):
         # Generate lyrics only for supported genres
         if genre_supported:
             lyrics = generate_lyrics(music_analysis, primary_genre, duration)
-            beat_match_analysis = analyze_lyrics_rhythm_match(lyrics, lyric_templates, primary_genre)
+            beat_match_analysis = analyze_lyrics_rhythm_match(lyrics, lyric_templates, primary_genre, emotion, theme)
         else:
             supported_genres_str = ", ".join([genre.capitalize() for genre in beat_analyzer.supported_genres])
             lyrics = f"Lyrics generation is only supported for the following genres: {supported_genres_str}.\n\nDetected genre '{primary_genre}' doesn't have strong syllable-to-beat patterns required for our lyric generation algorithm."
@@ -242,44 +269,45 @@ ONLY WRITE THE ACTUAL LYRICS. NO EXPLANATIONS OR META-TEXT.
                 avg_syllables = 4
             
             # Create a more direct prompt with examples and specific syllable count guidance
-            prompt = f"""Write song lyrics for a {genre} song in {key} {mode} with tempo {tempo} BPM. The emotion is {emotion} and theme is {theme}.
+            prompt = f"""Write song lyrics for a {genre} song that truly captures the emotional essence of "{emotion}" and explores the theme of "{theme}". The song is in {key} {mode} with tempo {tempo} BPM.
 
-I need EXACTLY {num_phrases} lines of lyrics - one line for each musical phrase. Not one more, not one less.
+I need EXACTLY {num_phrases} lines of lyrics - one line for each musical phrase.
 
-CRITICAL INSTRUCTIONS:
-- Each line MUST be VERY SHORT with only {min_syllables}-{max_syllables} syllables (aim for {avg_syllables} or fewer)
-- PRIORITIZE BREVITY - use fewer syllables rather than more
-- CONNECT YOUR LINES - spread complete thoughts across 2-3 consecutive lines
-- Create SENTENCE FLOW across lines instead of making each line independent
-- Let sentence clauses and phrases flow naturally across multiple lines
-- Each individual line should still fit into one measure of music
-- Use simple, short words whenever possible
+YOUR TOP PRIORITIES (in order):
+1. EXPRESS THE EMOTION: "{emotion}" should be felt through your word choices
+2. DEVELOP THE THEME: "{theme}" should be clearly represented
+3. CONNECT YOUR LINES: spread complete thoughts across 2-3 consecutive lines
+4. KEEP LINES SHORT: {min_syllables}-{max_syllables} syllables per line (aim for {avg_syllables})
 
-FORMAT:
-- Just write {num_phrases} plain text lines
-- Each line should be simple song lyrics (no annotations)
-- Don't include any explanations or commentary
-- Don't use any tags or markers
-- Don't include section labels like [Verse] or [Chorus]
-
-EXAMPLE OF CONNECTED THOUGHTS ACROSS LINES:
+ADDITIONAL REQUIREMENTS:
+- Create original lyrics that reflect this specific emotion and theme
+- Let sentence clauses flow naturally across line breaks
+- Use vivid imagery and sensory details related to the emotion
+- Each line should contribute to the overall theme
+- Don't copy the example structures - be creative and unique
+- Use simple, concise words that evoke strong emotions
 
-Moonlight falls (3 syllables)
-through my window pane (5 syllables)
-as I wait for you (5 syllables)
+AVOID:
+- Generic phrases that could apply to any song
+- Copying patterns from the examples below
+- Complete, independent thoughts on each line
+- Abstract concepts without concrete imagery
 
-Notice how these 3 lines form ONE complete sentence.
+FORMAT:
+- Plain text, one line per musical phrase
+- No annotations, explanations, or labels
 
-Another example:
+Here's a simplified structural example of connecting thoughts across lines:
 
-Whispers fade (3 syllables)
-in the morning light (5 syllables)
-leaving memories (5 syllables)
-of our last goodbye (5 syllables)
+Line 1 (introduces an image)
+Line 2 (extends the image)
+Line 3 (completes the thought)
 
-These 4 lines form a connected thought, not independent statements.
+Line 4 (starts a new thought)
+Line 5 (continues it)
+And so on...
 
-JUST THE PLAIN LYRICS, EXACTLY {num_phrases} LINES, KEEPING EACH LINE TO {min_syllables}-{max_syllables} SYLLABLES, WITH CONNECTED THOUGHTS ACROSS LINES.
+Remember: YOUR LYRICS SHOULD DEEPLY EXPRESS "{emotion}" AND EXPLORE "{theme}" - make every word count toward these goals.
 """
 
         # Generate lyrics using the LLM model
@@ -559,7 +587,7 @@ JUST THE PLAIN LYRICS, EXACTLY {num_phrases} LINES, KEEPING EACH LINE TO {min_sy
         print(error_msg)
         return error_msg
 
-def analyze_lyrics_rhythm_match(lyrics, lyric_templates, genre="pop"):
+def analyze_lyrics_rhythm_match(lyrics, lyric_templates, genre="pop", emotion="reflective", theme="journey"):
     """Analyze how well the generated lyrics match the beat patterns and syllable requirements"""
     if not lyric_templates or not lyrics:
         return "No beat templates or lyrics available for analysis."
@@ -646,12 +674,35 @@ def analyze_lyrics_rhythm_match(lyrics, lyric_templates, genre="pop"):
         result += f"- Average lines per thought: {sentence_flow_analysis['avg_lines_per_group']:.1f}\n"
         result += f"- Flow quality: {sentence_flow_analysis['flow_quality']}\n"
         
+        # Analyze theme and emotion expression
+        content_analysis = analyze_theme_emotion_expression(lyrics, theme, emotion)
+        result += f"\n**Theme & Emotion Expression:**\n"
+        result += f"- Emotion ({emotion}) expression: {content_analysis['emotion_score']:.1f}% ({content_analysis['emotion_words_found']} words)\n"
+        result += f"- Theme ({theme}) development: {content_analysis['theme_score']:.1f}% ({content_analysis['theme_words_found']} words)\n"
+        result += f"- Overall expression quality: {content_analysis['expression_quality']}\n"
+        
         # Add guidance on ideal distribution for syllables and sentence flow
-        result += f"\n**Syllable & Flow Guidance:**\n"
-        result += f"- Aim for {min([t.get('min_expected', 3) for t in lyric_templates])}-{max([t.get('max_expected', 7) for t in lyric_templates])} syllables per line\n"
-        result += f"- Break complete thoughts across 2-3 lines for natural flow\n"
-        result += f"- Connect your lyrics with sentence fragments that flow across lines\n"
-        result += f"- Use conjunctions, prepositions, and dependent clauses to connect lines\n"
+        result += f"\n**Improvement Recommendations:**\n"
+        
+        # Syllable recommendations
+        if range_match_rate < 70:
+            result += f"- **Syllable count:** Aim for {min([t.get('min_expected', 3) for t in lyric_templates])}-{max([t.get('max_expected', 7) for t in lyric_templates])} syllables per line\n"
+        
+        # Flow recommendations
+        if sentence_flow_analysis['connected_groups'] < len(lines) / 5:
+            result += f"- **Line connections:** Break complete thoughts across 2-3 lines using conjunctions and prepositions\n"
+            result += f"- **Flow techniques:** Start lines with connecting words like 'as', 'when', 'while', 'through'\n"
+        
+        # Theme/emotion recommendations
+        if content_analysis['emotion_score'] < 20:
+            result += f"- **Emotion expression:** Use more words that evoke '{emotion}' feelings (e.g., {', '.join(emotion_lexicons.get(emotion.lower(), ['expressive words'])[:3])})\n"
+        
+        if content_analysis['theme_score'] < 20:
+            result += f"- **Theme development:** Incorporate more '{theme}' imagery and concepts (e.g., {', '.join(theme_lexicons.get(theme.lower(), ['thematic words'])[:3])})\n"
+        
+        # General recommendations
+        result += f"- **Originality:** Avoid generic phrases and create specific, vivid imagery\n"
+        result += f"- **Sensory details:** Include concrete details that can be seen, heard, or felt\n"
         
         # Add genre-specific notes
         result += f"\n**Genre Notes ({genre}):**\n"
@@ -770,6 +821,66 @@ def analyze_sentence_flow(lines):
         "flow_quality": flow_quality
     }
 
+def analyze_theme_emotion_expression(lyrics, theme, emotion):
+    """Analyze how well the lyrics express the target theme and emotion"""
+    
+    # Normalize inputs
+    lyrics_text = lyrics.lower()
+    emotion = emotion.lower()
+    theme = theme.lower()
+    
+    # Find closest emotion lexicon if exact match not found
+    if emotion not in emotion_lexicons:
+        closest_emotion = "reflective"  # Default
+        for key in emotion_lexicons:
+            if emotion in key or key in emotion:
+                closest_emotion = key
+                break
+        emotion = closest_emotion
+        
+    # Find closest theme lexicon if exact match not found
+    if theme not in theme_lexicons:
+        closest_theme = "journey"  # Default
+        for key in theme_lexicons:
+            if theme in key or key in theme:
+                closest_theme = key
+                break
+        theme = closest_theme
+    
+    # Count emotional and thematic words in lyrics
+    emotion_matches = 0
+    theme_matches = 0
+    
+    for word in emotion_lexicons[emotion]:
+        if word in lyrics_text:
+            emotion_matches += 1
+            
+    for word in theme_lexicons[theme]:
+        if word in lyrics_text:
+            theme_matches += 1
+    
+    # Calculate scores as percentages of available words
+    emotion_score = min(100, (emotion_matches / len(emotion_lexicons[emotion])) * 100)
+    theme_score = min(100, (theme_matches / len(theme_lexicons[theme])) * 100)
+    
+    # Qualitative assessment
+    if emotion_score >= 30 and theme_score >= 30:
+        expression_quality = "Strong"
+    elif emotion_score >= 20 and theme_score >= 20:
+        expression_quality = "Good"
+    elif emotion_score >= 10 and theme_score >= 10:
+        expression_quality = "Fair"
+    else:
+        expression_quality = "Weak"
+    
+    return {
+        "emotion_score": emotion_score,
+        "theme_score": theme_score,
+        "expression_quality": expression_quality,
+        "emotion_words_found": emotion_matches,
+        "theme_words_found": theme_matches
+    }
+
 # Create Gradio interface
 def create_interface():
     with gr.Blocks(title="Music Analysis & Lyrics Generator") as demo: