ss

app.py

@@ -86,33 +86,6 @@ 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:
@@ -217,7 +190,7 @@ 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, emotion, theme)
+            beat_match_analysis = analyze_lyrics_rhythm_match(lyrics, lyric_templates, primary_genre)
         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."
@@ -254,9 +227,6 @@ def generate_lyrics(music_analysis, genre, duration):
 ONLY WRITE THE ACTUAL LYRICS. NO EXPLANATIONS OR META-TEXT.
 """
         else:
-            # Create phrase examples
-            num_phrases = len(lyric_templates)
-            
             # Calculate the typical syllable range for this genre
             if num_phrases > 0:
                 # Get max syllables per line from templates
@@ -268,46 +238,82 @@ ONLY WRITE THE ACTUAL LYRICS. NO EXPLANATIONS OR META-TEXT.
                 max_syllables = 7
                 avg_syllables = 4
             
+            # Create random examples based on the song's theme and emotion
+            # to avoid the LLM copying our examples directly
+            example_themes = [
+                {"emotion": "love", "fragments": ["I see your face", "across the room", "my heart beats fast", "can't look away"]},
+                {"emotion": "sadness", "fragments": ["tears fall like rain", "on empty streets", "memories fade", "into the dark"]},
+                {"emotion": "nostalgia", "fragments": ["old photographs", "dusty and worn", "remind me of when", "we were young"]},
+                {"emotion": "hope", "fragments": ["dawn breaks through clouds", "new day begins", "darkness recedes", "light fills my soul"]},
+                {"emotion": "longing", "fragments": ["miles apart now", "under same stars", "thinking of you", "across the distance"]}
+            ]
+            
+            # Select a theme that doesn't match the song's emotion to avoid copying
+            selected_themes = [t for t in example_themes if t["emotion"].lower() != emotion.lower()]
+            if not selected_themes:
+                selected_themes = example_themes
+                
+            import random
+            example_theme = random.choice(selected_themes)
+            example_fragments = example_theme["fragments"]
+            random.shuffle(example_fragments)  # Randomize order
+            
+            # Create example 1 - grammatical connection with conjunction
+            ex1_line1 = example_fragments[0] if len(example_fragments) > 0 else "The morning sun"
+            ex1_line2 = example_fragments[1] if len(example_fragments) > 1 else "breaks through clouds"
+            ex1_line3 = example_fragments[2] if len(example_fragments) > 2 else "as birds begin"
+            ex1_line4 = example_fragments[3] if len(example_fragments) > 3 else "their dawn chorus"
+            
+            # Create example 2 - prepositional connection
+            ex2_fragments = [
+                "She walks alone",
+                "through crowded streets",
+                "with memories",
+                "of better days"
+            ]
+            random.shuffle(ex2_fragments)
+            
             # Create a more direct prompt with examples and specific syllable count guidance
-            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.
-
-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})
-
-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
-
-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
+            prompt = f"""Write song lyrics for a {genre} song in {key} {mode} with tempo {tempo} BPM.
+
+PRIMARY THEME: {theme}
+EMOTION: {emotion}
+
+I need EXACTLY {num_phrases} lines of lyrics with these requirements:
+
+KEY INSTRUCTIONS:
+1. CONNECTED THOUGHTS: Spread complete sentences across multiple lines (2-3 lines)
+2. SHORT LINES: Keep each line between {min_syllables}-{max_syllables} syllables
+3. NATURAL FLOW: Use conjunctions, prepositions and fragments that flow together
+4. AVOID CLICHÉS: Don't use phrases like "moonlight shimmers" or "time slips away"
+5. ORIGINALITY: Create fresh, original imagery related to {theme} and {emotion}
+6. DISTINCTIVE VOICE: Use specific, concrete details rather than generic phrases
 
 FORMAT:
-- Plain text, one line per musical phrase
-- No annotations, explanations, or labels
+- Write exactly {num_phrases} plain text lines of lyrics (just the lyrics, no annotations)
+- Don't include explanations, section markers, or line numbers
+
+===== EXAMPLES OF CONNECTED THOUGHTS =====
 
-Here's a simplified structural example of connecting thoughts across lines:
+Example 1 - Connection with conjunction:
+{ex1_line1}
+{ex1_line2}
+{ex1_line3}
+{ex1_line4}
 
-Line 1 (introduces an image)
-Line 2 (extends the image)
-Line 3 (completes the thought)
+↑ Notice how these lines connect grammatically to form complete thoughts.
 
-Line 4 (starts a new thought)
-Line 5 (continues it)
-And so on...
+Example 2 - Connection with prepositions:
+{ex2_fragments[0]}
+{ex2_fragments[1]}
+{ex2_fragments[2]}
+{ex2_fragments[3]}
 
-Remember: YOUR LYRICS SHOULD DEEPLY EXPRESS "{emotion}" AND EXPLORE "{theme}" - make every word count toward these goals.
+↑ These lines use prepositions to create flow across multiple lines.
+
+IMPORTANT: DO NOT copy my examples or use their wording. Create ENTIRELY NEW, ORIGINAL lyrics about {theme} with {emotion} feeling that have your own distinctive voice and perspective.
+
+Just write {num_phrases} lines of original lyrics, focusing on connecting your lines with thought flow.
 """
 
         # Generate lyrics using the LLM model
@@ -491,7 +497,37 @@ Remember: YOUR LYRICS SHOULD DEEPLY EXPRESS "{emotion}" AND EXPLORE "{theme}" -
         # 9. Filter out any remaining empty lines after tag removal
         clean_lines = [line for line in clean_lines if line.strip() and not line.isspace()]
         
-        # 10. If we have lyric templates, ensure we have the correct number of lines
+        # 10. NEW: Check for template copying or clichéd phrases
+        cliched_patterns = [
+            r'moonlight (shimmers?|falls?|dances?)',
+            r'shadows? (dance|play|fall|stretch)',
+            r'time slips? away',
+            r'whispers? (fade|in the)',
+            r'silence speaks',
+            r'stars? shine',
+            r'hearts? beat',
+            r'footsteps (fade|echo)',
+            r'gentle wind',
+            r'(old|empty) (roads?|chair)',
+            r'night (holds?|falls?)',
+            r'memories fade',
+            r'dreams (linger|drift)'
+        ]
+        
+        cliche_count = 0
+        for line in clean_lines:
+            for pattern in cliched_patterns:
+                if re.search(pattern, line.lower()):
+                    cliche_count += 1
+                    break
+        
+        # Calculate percentage of clichéd lines
+        if clean_lines:
+            cliche_percentage = (cliche_count / len(clean_lines)) * 100
+        else:
+            cliche_percentage = 0
+            
+        # 11. If we have lyric templates, ensure we have the correct number of lines
         if lyric_templates:
             num_required = len(lyric_templates)
             
@@ -507,76 +543,107 @@ Remember: YOUR LYRICS SHOULD DEEPLY EXPRESS "{emotion}" AND EXPLORE "{theme}" -
                     template = lyric_templates[i]
                     target_syllables = min(max_syllables, (template.get('min_expected', 2) + template.get('max_expected', 7)) // 2)
                     
-                    # Create a diverse set of placeholders that match the theme/emotion
-                    placeholders = {
-                        # 2-3 syllables
+                    # Generate more creative, contextual placeholders with specificity
+                    # Avoid clichés like "moonlight shimmers" or "time slips away"
+                    specific_placeholders = {
+                        # 2-3 syllables - specific, concrete phrases
                         2: [
-                            "Night falls", 
-                            "Time stops", 
-                            "Hearts beat", 
-                            "Rain falls", 
-                            "Stars shine"
+                            "Phone rings twice", 
+                            "Dogs bark loud", 
+                            "Keys dropped here", 
+                            "Train rolls by", 
+                            "Birds take flight"
                         ],
-                        # 3-4 syllables
+                        # 3-4 syllables - specific contexts
                         3: [
-                            "Empty chair", 
-                            "Shadows dance", 
-                            "Whispers fade", 
-                            "Memories", 
-                            "Silent room"
+                            "Coffee's getting cold", 
+                            "Fan blades spinning", 
+                            "Pages turning slow", 
+                            "Neighbors argue loud", 
+                            "Radio plays soft"
                         ],
-                        # 4-5 syllables
+                        # 4-5 syllables - specific details
                         4: [
-                            "Moonlight shimmers", 
-                            "Echoes of time", 
-                            "Footsteps fading", 
-                            "Memories drift", 
-                            "Silence speaks loud"
+                            "Fingers tap the table", 
+                            "Taxi waits in rain", 
+                            "Laptop screen flickers", 
+                            "Wedding ring forgotten", 
+                            "Sunday church bells ring"
                         ],
-                        # 5-6 syllables
+                        # 5-6 syllables - context rich
                         5: [
-                            "Walking in the rain", 
-                            "Whispers in the dark", 
-                            "Echoes of your voice", 
-                            "Traces left behind", 
-                            "Time moves ever on"
+                            "Letters sent without stamps", 
+                            "Broken watch shows wrong time", 
+                            "Faded jeans with torn knees", 
+                            "Neighbor's dog keeps barking", 
+                            "Smoke detector beeping"
                         ],
-                        # 6-7 syllables
+                        # 6-7 syllables - specific scenarios
                         6: [
-                            "Dancing in the moonlight", 
-                            "Shadows play on the wall", 
-                            "Memories fade to silence", 
-                            "Moments lost in the wind", 
-                            "Whispers of a better time"
+                            "Fingerprints on dusty frames", 
+                            "Afternoon bus running late", 
+                            "Yellowed photos in a box", 
+                            "Backyard swing rocks in the wind", 
+                            "Curtains move with summer breeze"
                         ]
                     }
                     
+                    # Make theme and emotion specific placeholders to add to the list
+                    theme_specific = []
+                    if theme.lower() in ["love", "relationship", "romance"]:
+                        theme_specific = ["Lipstick on a glass", "Texts left on read", "Perfume on your coat"]
+                    elif theme.lower() in ["loss", "grief", "sadness"]:
+                        theme_specific = ["Empty chair remains", "Photos face-down now", "Clothes still in the closet"]
+                    elif theme.lower() in ["hope", "inspiration", "triumph"]:
+                        theme_specific = ["Seeds begin to sprout", "Finish line in sight", "Mountain peak awaits"]
+                    
                     # Get the closest matching syllable group
-                    closest_group = min(placeholders.keys(), key=lambda k: abs(k - target_syllables))
+                    closest_group = min(specific_placeholders.keys(), key=lambda k: abs(k - target_syllables))
+                    
+                    # Create pool of available placeholders from both specific and theme specific options
+                    all_placeholders = specific_placeholders[closest_group] + theme_specific
                     
                     # Choose a placeholder that hasn't been used yet
-                    available_placeholders = [p for p in placeholders[closest_group] 
-                                             if p not in clean_lines]
+                    available_placeholders = [p for p in all_placeholders if p not in clean_lines]
                     
                     if available_placeholders:
-                        placeholder = available_placeholders[i % len(available_placeholders)]
+                        # Use modulo for more variation
+                        idx = (i * 17 + len(clean_lines) * 13) % len(available_placeholders)
+                        placeholder = available_placeholders[idx]
                     else:
-                        # If we've used all placeholders in this group, create a custom one
-                        if emotion.lower() in ["sad", "nostalgic", "calm"]:
-                            placeholder = f"Memories of {emotion}"
-                        elif emotion.lower() in ["happy", "energetic"]:
-                            placeholder = f"Dancing through {emotion}"
+                        # If we've used all placeholders, create something random and specific
+                        subjects = ["Car", "Dog", "Child", "Clock", "Phone", "Tree", "Book", "Door", "Light"]
+                        verbs = ["waits", "moves", "stops", "falls", "breaks", "turns", "opens", "runs", "sleeps"]
+                        modifiers = ["slowly", "loudly", "brightly", "quickly", "gently", "badly", "forever", "never", "always"]
+                        
+                        # Ensure randomness with seed that changes with each call
+                        import random
+                        random.seed(len(clean_lines) * 27 + i * 31)
+                        
+                        subj = random.choice(subjects)
+                        verb = random.choice(verbs)
+                        
+                        if target_syllables <= 3:
+                            placeholder = f"{subj} {verb}"
                         else:
-                            placeholder = f"Feeling {emotion} now"
+                            mod = random.choice(modifiers)
+                            placeholder = f"{subj} {verb} {mod}"
                 else:
-                    placeholder = "Silence speaks volumes"
+                    placeholder = "Final page turns now"
                     
                 clean_lines.append(placeholder)
         
         # Assemble final lyrics
         final_lyrics = '\n'.join(clean_lines)
         
-        # 11. Final sanity check - if we have nothing or garbage, return an error
+        # Add a warning if we detected too many clichés
+        if cliche_percentage >= 40:
+            final_lyrics = f"""WARNING: These lyrics contain several overused phrases and clichés.
+Try regenerating for more original content.
+
+{final_lyrics}"""
+        
+        # 12. Final sanity check - if we have nothing or garbage, return an error
         if not final_lyrics or len(final_lyrics) < 10:
             return "The model generated only thinking content but no actual lyrics. Please try again."
         
@@ -587,7 +654,7 @@ Remember: YOUR LYRICS SHOULD DEEPLY EXPRESS "{emotion}" AND EXPLORE "{theme}" -
         print(error_msg)
         return error_msg
 
-def analyze_lyrics_rhythm_match(lyrics, lyric_templates, genre="pop", emotion="reflective", theme="journey"):
+def analyze_lyrics_rhythm_match(lyrics, lyric_templates, genre="pop"):
     """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."
@@ -674,35 +741,12 @@ def analyze_lyrics_rhythm_match(lyrics, lyric_templates, genre="pop", emotion="r
         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**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"
+        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"
         
         # Add genre-specific notes
         result += f"\n**Genre Notes ({genre}):**\n"
@@ -821,66 +865,6 @@ 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: