diff --git a/prompts/02-scene-everything.txt b/prompts/02-scene-everything.txt deleted file mode 100644 index 90a49a8..0000000 --- a/prompts/02-scene-everything.txt +++ /dev/null @@ -1 +0,0 @@ -Output only comma-separated keywords that cover everything in the scene: colors, objects, people (if present, use 'people' with descriptive count like '2-people'), what they're doing and feeling, the environment (location type, weather, lighting, time), and the general mood or vibe. \ No newline at end of file diff --git a/prompts/04-generate-all-aspects.txt b/prompts/04-generate-all-aspects.txt deleted file mode 100644 index 14b3dcb..0000000 --- a/prompts/04-generate-all-aspects.txt +++ /dev/null @@ -1 +0,0 @@ -Generate comma-separated keywords only: include all colors, objects, number of people, actions, facial expressions or emotions, the environment (location type, weather, lighting, time), and the scene's mood or atmosphere. \ No newline at end of file diff --git a/prompts/06-concise-complete.txt b/prompts/06-concise-complete.txt deleted file mode 100644 index 75f7de6..0000000 --- a/prompts/06-concise-complete.txt +++ /dev/null @@ -1 +0,0 @@ -Comma-separated keywords covering: colors, all objects, people (if humans visible, include 'people' with count like '3-people'), activities, emotions, location type, weather/lighting, time of day, mood. Keywords only. \ No newline at end of file diff --git a/prompts/07-ultra-detailed-scene.txt b/prompts/07-ultra-detailed-scene.txt deleted file mode 100644 index 0bfd451..0000000 --- a/prompts/07-ultra-detailed-scene.txt +++ /dev/null @@ -1,21 +0,0 @@ -Generate comprehensive search keywords for this image as comma-separated values. Analyze and keyword: - -PEOPLE: if humans are visible include 'people' keyword followed by descriptive count (e.g. '3-people', 'group', 'couple'), apparent ages (baby/child/teen/adult/elderly), genders if clear, relationships (couple/family/friends/strangers), body language, facial expressions (smiling/laughing/crying/serious/surprised), activities (eating/walking/sitting/playing/working). If no people are present, skip all people-related keywords - -MOOD & ATMOSPHERE: overall emotional tone (joyful/peaceful/tense/romantic/nostalgic/energetic), energy level (calm/lively/chaotic), formality (casual/formal/ceremonial) - -SETTING: indoor/outdoor, specific location type (beach/mountain/city/restaurant/home/office/park), country/region if identifiable, venue type (public/private) - -TIME & LIGHTING: time of day (early morning/morning/midday/afternoon/golden hour/evening/night), lighting quality (bright/soft/harsh/dim/dramatic), light source (natural/artificial/mixed) - -COLORS: dominant colors (top 3), accent colors, color mood (warm/cool/neutral/vibrant/muted) - -OBJECTS: food types if visible (cuisine type, dishes, drinks), vehicles, furniture, technology, decorations, sports equipment, musical instruments, art - -NATURE: weather conditions, season indicators, landscapes, water bodies, vegetation, animals - -BACKGROUND: what's behind the main subjects, architectural elements, crowds, signage - -EVENTS: if applicable (wedding/birthday/concert/sports/holiday/vacation) - -Output only comma-separated keywords, no explanations. \ No newline at end of file diff --git a/prompts/08-memory-search-optimizer.txt b/prompts/08-memory-search-optimizer.txt deleted file mode 100644 index a7dc609..0000000 --- a/prompts/08-memory-search-optimizer.txt +++ /dev/null @@ -1,13 +0,0 @@ -Create detailed keywords for video diary search. Users might search for: "happy moments", "food experiences", "family time", "adventures", "quiet moments", "celebrations", "daily life", "travel memories". - -Keyword everything visible including: -- People: count, approximate ages, emotions on faces, what they're doing, how they're interacting -- Scene type: where this is happening, indoor/outdoor, public/private space -- Time: morning light, afternoon, golden hour, evening, night time -- Mood: the feeling of the moment (joyful, peaceful, exciting, intimate, festive, contemplative) -- Activities: eating, playing, working, relaxing, traveling, celebrating, exploring -- Details: specific foods visible, drinks, decorations, clothing styles, weather, season -- Colors: main colors that define the scene -- Special moments: laughter, hugs, cheers, surprises, achievements - -Format: comma-separated keywords only, be specific rather than generic. \ No newline at end of file diff --git a/prompts/09-contextual-story-tagger.txt b/prompts/09-contextual-story-tagger.txt deleted file mode 100644 index f41c08d..0000000 --- a/prompts/09-contextual-story-tagger.txt +++ /dev/null @@ -1,19 +0,0 @@ -Analyze this image as a moment in someone's life story. Generate specific, searchable keywords covering: - -WHO: number of people, their likely relationships, emotions showing on faces, age groups -WHAT: main activity, secondary activities, interactions between people, gestures -WHERE: type of location, indoor/outdoor, specific venue (restaurant/home/beach/etc), geographic hints -WHEN: time of day, season clues, weather conditions, lighting quality -WHY: occasion if apparent (meal/celebration/vacation/work/leisure), mood of gathering - -Include sensory details: -- Visual: dominant colors, lighting (harsh/soft/golden), contrast -- Implied: likely sounds (quiet/loud/music), temperature (hot/cold), atmosphere - -Keyword specific items: -- Food: cuisine type, specific dishes if identifiable, drinks -- Objects: technology, vehicles, sports equipment, decorations -- Nature: landscapes, water, sky conditions, plants, animals -- Clothing: formal/casual, weather-appropriate - -Output format: detailed comma-separated keywords only. \ No newline at end of file diff --git a/prompts/10-moment-finder-pro.txt b/prompts/10-moment-finder-pro.txt deleted file mode 100644 index ea0f48c..0000000 --- a/prompts/10-moment-finder-pro.txt +++ /dev/null @@ -1,21 +0,0 @@ -Generate exhaustive search keywords. Think like someone searching their memories: - -EMOTION KEYWORDS: happy, laughing, smiling, serious, contemplative, excited, surprised, loving, playful, tired, focused, relaxed, celebratory - -PEOPLE KEYWORDS: exact count, "group of friends", "couple", "family", "solo", ages if clear, interactions - -ACTIVITY KEYWORDS: eating, drinking, cooking, playing, sports, reading, working, dancing, hugging, talking, walking, sitting, lying down - -PLACE KEYWORDS: home, restaurant, cafe, beach, mountain, city, countryside, indoor, outdoor, kitchen, living room, bedroom, office, street, park, venue name if visible - -TIME KEYWORDS: sunrise, morning, midday, afternoon, sunset, golden hour, blue hour, evening, night, specific time if visible - -OBJECT KEYWORDS: list everything visible - food items, drinks, furniture, electronics, vehicles, decorations, plants, books, instruments - -ATMOSPHERE KEYWORDS: cozy, energetic, romantic, professional, casual, festive, quiet, busy, crowded, intimate - -COLOR KEYWORDS: list prominent colors - -WEATHER/SEASON KEYWORDS: sunny, cloudy, rainy, snowy, fog, clear, spring, summer, fall, winter indicators - -Comma-separated only, no duplicates. \ No newline at end of file diff --git a/prompts/11-smart-scene-decoder.txt b/prompts/11-smart-scene-decoder.txt deleted file mode 100644 index 17bd4a9..0000000 --- a/prompts/11-smart-scene-decoder.txt +++ /dev/null @@ -1,13 +0,0 @@ -You're analyzing frames for an AI-powered video diary search. Users search with natural language like "dinner with friends", "kids playing", "sunset at the beach", "birthday celebrations", "quiet morning coffee". - -Extract and keyword: -HUMANS: if humans visible include 'people' and descriptive count (e.g. '4-people', 'couple', 'crowd'), estimated ages in decades (20s/30s/etc), primary emotion per person, body language, attire style. Skip if no humans present -ACTIONS: primary action, secondary actions, interactions, gestures -LOCATION: venue type, indoor/outdoor, architectural style, geographic region if evident -TEMPORAL: exact time if visible, otherwise: dawn/morning/noon/afternoon/dusk/night, season indicators -AMBIANCE: energy level 1-10, mood descriptors, lighting quality, color temperature -OBJECTS: enumerate all significant objects, food with cuisine type, beverages, decorations -CONTEXT: occasion type, relationship dynamics, cultural indicators -TECHNICAL: image quality descriptors, composition style - -Output as comma-separated keywords. Prioritize specific over generic (e.g., "pepperoni pizza" not just "food"). \ No newline at end of file