Update app.py

app.py

@@ -110,14 +110,41 @@ def analyze_sentiment(text):
         print("Error analyzing sentiment:", e)
         return "neutral", 0.0
 
+# Function to get image prompt based on sentiment
+def get_image_prompt(sentiment, transcribed_text):
+    if sentiment == "positive":
+        return f"Generate a vibrant, uplifting equirectangular 360 image texture with bright colors, joyful atmosphere, and optimistic vibes representing: [{transcribed_text}]. The scene should evoke happiness and positivity."
+    
+    elif sentiment == "negative":
+        return f"Generate a moody, dramatic equirectangular 360 image texture with dark tones, intense atmosphere, and emotional depth representing: [{transcribed_text}]. The scene should convey melancholy and intensity."
+    
+    else:  # neutral
+        return f"Generate a balanced, serene equirectangular 360 image texture with harmonious colors, peaceful atmosphere, and calm vibes representing: [{transcribed_text}]. The scene should evoke tranquility and balance."
+
+# Function to get music prompt based on emotion
+def get_music_prompt(emotion, transcribed_text):
+    emotion_prompts = {
+        'neutral': f"Create ambient, background music with neutral tones, subtle melodies, and unobtrusive atmosphere that complements: {transcribed_text}. The music should be calm and balanced.",
+        'calm': f"Generate soothing, peaceful music with gentle melodies, soft instrumentation, and relaxing vibes that represents: {transcribed_text}. The music should evoke tranquility and serenity.",
+        'happy': f"Create joyful, upbeat music with cheerful melodies, bright instrumentation, and energetic rhythms that celebrates: {transcribed_text}. The music should evoke happiness and positivity.",
+        'sad': f"Generate emotional, melancholic music with poignant melodies, soft strings, and heartfelt atmosphere that reflects: {transcribed_text}. The music should evoke sadness and reflection.",
+        'angry': f"Create intense, powerful music with driving rhythms, aggressive instrumentation, and strong dynamics that expresses: {transcribed_text}. The music should evoke anger and intensity.",
+        'fearful': f"Generate suspenseful, tense music with eerie melodies, atmospheric sounds, and unsettling vibes that represents: {transcribed_text}. The music should evoke fear and anticipation.",
+        'disgust': f"Create dark, unsettling music with dissonant harmonies, unusual sounds, and uncomfortable atmosphere that reflects: {transcribed_text}. The music should evoke discomfort and unease.",
+        'surprised': f"Generate dynamic, unexpected music with sudden changes, playful melodies, and surprising elements that represents: {transcribed_text}. The music should evoke surprise and wonder."
+    }
+    
+    return emotion_prompts.get(emotion.lower(), 
+        f"Create background music with {emotion} atmosphere that represents: {transcribed_text}")
+
 # Function to generate music with MusicGen (using acoustic emotion prediction)
 def generate_music(transcribed_text, emotion_prediction):
     try:
         if processor is None or music_model is None:
             return None
             
-        # Create a prompt that combines the acoustic emotion and transcription
-        prompt = f"Background music that is {emotion_prediction} and represents: {transcribed_text}"
+        # Get specific prompt based on emotion
+        prompt = get_music_prompt(emotion_prediction, transcribed_text)
         
         # Limit prompt length to avoid model issues
         if len(prompt) > 200:
@@ -157,8 +184,8 @@ def generate_image(sentiment_prediction, transcribed_text):
             # fallback white image if no API key
             return Image.new('RGB', (1024, 512), color='white')
             
-        # Create the prompt for text2img using SENTIMENT analysis instead of acoustic emotion
-        prompt = f"Generate an equirectangular 360 image texture with {sentiment_prediction} sentiment, representing the idea of: [{transcribed_text}]."
+        # Get specific prompt based on sentiment
+        prompt = get_image_prompt(sentiment_prediction, transcribed_text)
         
         # Make request to DeepAI text2img API
         response = requests.post(
@@ -257,10 +284,10 @@ def get_predictions(audio_input):
     # Analyze sentiment of transcribed text (for image)
     sentiment, polarity = analyze_sentiment(transcribed_text)
     
-    # Generate image using SENTIMENT analysis
+    # Generate image using SENTIMENT analysis with specific prompt
     image = generate_image(sentiment, transcribed_text)
     
-    # Generate music using ACOUSTIC EMOTION prediction
+    # Generate music using ACOUSTIC EMOTION prediction with specific prompt
     music_path = generate_music(transcribed_text, emotion_prediction)
     
     # Create visualization with both texture and sphere