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Image_Generator.py

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+import streamlit as st
+from diffusers import DiffusionPipeline
+import torch
+from PIL import Image
+# Custom CSS to improve the appearance
+st.markdown("""
+<style>
+    .stApp {
+        background-image:linear-gradient(to bottom, #000000 ,#000814 ,#001d3d, #003566);
+    }
+    .main-title {
+        font-size: 3rem !important;
+        color: #f1faee;
+        text-align: center;
+        padding: 1rem 0;
+    }
+    .subtitle {
+        font-size: 1.2rem;
+        color: #f1faee;
+        text-align: center;
+        margin-bottom: 2rem;
+    }
+    .stTextInput > div > div > input {
+        font-size: 1.2rem;
+    }
+    .generate-button {
+        font-size: 1.2rem;
+        border-radius: 10px;
+        padding: 0.5rem 1rem;
+    }
+    .info-section {
+        background-color: #ffffff;
+        padding: 1rem;
+        border-radius: 10px;
+        margin-top: 2rem;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Initialize the Stable Diffusion pipeline
+@st.cache_resource
+def load_image_model():
+    # Ensure that you are using the correct device (GPU if available)
+    pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+    if torch.cuda.is_available():
+        pipe.to("cuda")
+    return pipe
+
+pipe = load_image_model()
+
+# Streamlit interface
+st.markdown("<h1 class='main-title'>🖼️ AI Image Generator</h1>", unsafe_allow_html=True)
+st.markdown("<p class='subtitle'>Describe the image you want to create, and the AI will generate it for you.</p>", unsafe_allow_html=True)
+
+# Main interaction area
+image_description = st.text_input("🖊️ Describe the image you want to create", 
+                                  placeholder="E.g., A sunset over a mountain range")
+
+if st.button("🎨 Generate Image"):
+    if image_description.strip():
+        with st.spinner("🎨 Generating your image..."):
+            st.warning("Image generation may take some time. Please be patient.")  # Display warning message
+            try:
+                # Generate the image
+                image = pipe(image_description).images[0]
+
+                # Display the generated image
+                st.image(image, caption="Generated Image", use_column_width=True)
+
+                # Provide download button for the generated image
+                img_path = "generated_image.png"
+                image.save(img_path)
+
+                with open(img_path, "rb") as file:
+                    st.download_button(
+                        label="📥 Download Image",
+                        data=file,
+                        file_name="generated_image.png",
+                        mime="image/png"
+                    )
+            except Exception as e:
+                st.error(f"😕 Oops! An error occurred: {str(e)}")
+    else:
+        st.warning("🤔 Please enter a description for your image.")
+
+# Information sections
+st.markdown("---")
+
+col1, col2 = st.columns(2)
+
+with col1:
+    st.markdown("### 🌟 How it works")
+    st.markdown("""
+    1. 📝 Describe the image you want
+    2. 🖱️ Click 'Generate Image'
+    3. 🎨 View your AI-generated artwork
+    4. 📥 Download and share!
+    """)
+
+with col2:
+    st.markdown("### 🎨 Tips for great results")
+    st.markdown("""
+    - Be specific about objects and settings
+    - Mention style, genre, or mood
+    - Describe the colors and lighting
+    """)
+
+# Footer
+st.markdown("---")

Music_Generator.py

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+import streamlit as st
+from musicgen import init_musicgen, generate_music
+import numpy as np
+from scipy.io.wavfile import write
+
+# Custom CSS to improve the appearance
+st.markdown("""
+<style>
+    .stApp {
+        background-image:linear-gradient(to bottom, #000000 ,#000814 ,#10002b, #240046);
+    }
+    .main-title {
+        font-size: 3rem !important;
+        color: #f1faee;
+        text-align: center;
+        padding: 1rem 0;
+    }
+    .subtitle {
+        font-size: 1.2rem;
+        color: #f1faee;
+        text-align: center;
+        margin-bottom: 2rem;
+    }
+    .stTextInput > div > div > input {
+        font-size: 1.2rem;
+    }
+    .generate-button {
+        font-size: 1.2rem;
+        border-radius: 10px;
+        padding: 0.5rem 1rem;
+    }
+    .info-section {
+        background-color: #ffffff;
+        padding: 1rem;
+        border-radius: 10px;
+        margin-top: 2rem;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Initialize the MusicGen model and tokenizer
+@st.cache_resource
+def load_model():
+    return init_musicgen('facebook/musicgen-small')
+
+tokenizer, model = load_model()
+
+# Streamlit interface
+st.markdown("<h1 class='main-title'>🎵 AI Music Generator</h1>", unsafe_allow_html=True)
+st.markdown("<p class='subtitle'>Describe the type of music you want, and the AI will generate it for you.</p>", unsafe_allow_html=True)
+
+# Main interaction area
+text_input = st.text_input("🖊️ Describe the music you want to create", 
+                           placeholder="E.g., A happy pop song with guitar and drums")
+
+if st.button("🎼 Generate Music"):
+    if text_input.strip():
+        with st.spinner("🎧 Generating your music..."):
+            try:
+                audio_array = generate_music(text_input, tokenizer, model)
+                
+                # Convert to int16 and save as WAV file
+                audio_int16 = (audio_array * 32767).astype(np.int16)
+                write("generated_music.wav", 44100, audio_int16)
+                
+                st.success("🎉 Your music has been generated successfully!")
+                
+                # Play the generated audio
+                st.audio("generated_music.wav")
+                
+                # Provide download button
+                with open("generated_music.wav", "rb") as file:
+                    st.download_button(
+                        label="📥 Download Music",
+                        data=file,
+                        file_name="ai_generated_music.wav",
+                        mime="audio/wav"
+                    )
+            except Exception as e:
+                st.error(f"😕 Oops! An error occurred: {str(e)}")
+    else:
+        st.warning("🤔 Please enter a description for your music.")
+
+# Information sections
+st.markdown("---")
+
+col1, col2 = st.columns(2)
+
+with col1:
+    st.markdown("### 🌟 How it works")
+    st.markdown("""
+    1. 📝 Describe the music you want
+    2. 🖱️ Click 'Generate Music'
+    3. 🎧 Listen to your AI-created tune
+    4. 📥 Download and share!
+    """)
+
+with col2:
+    st.markdown("### 🎨 Tips for great results")
+    st.markdown("""
+    - Be specific about instruments
+    - Mention genre or mood
+    - Describe tempo or rhythm
+    - Reference famous artists or songs
+    """)
+
+# Footer
+st.markdown("---")

home.py

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+import streamlit as st
+
+# Set page configuration (only once in main.py)
+st.set_page_config(page_title="AI Generator App", page_icon="🎵", layout="wide")
+
+# Custom CSS to improve the appearance
+st.markdown("""
+    <style>
+    .stApp {
+        background-image:linear-gradient(to bottom, #000000 , #0a0908, #250902, #641220);
+        font-family: 'Helvetica', sans-serif;
+    }
+    .main-title {
+        font-size: 3.5rem !important;
+        color: #f1faee;
+        text-align: center;
+        margin-bottom: 0.5rem;
+        font-weight: bold;
+    }
+    .subtitle {
+        font-size: 1.5rem;
+        color: #f1faee;
+        text-align: center;
+        margin-bottom: 3rem;
+    }
+    .stButton button {
+        background-color: #4CAF50;
+        color: white;
+        padding: 15px 32px;
+        font-size: 1.2rem;
+        margin: 10px;
+        border-radius: 12px;
+        border: none;
+        cursor: pointer;
+        transition: background-color 0.3s ease;
+    }
+    .stButton button:hover {
+        background-color: #45a049;
+    }
+    .section-title {
+        color: #fff;
+        font-size: 1.5rem;
+        margin-bottom: 1rem;
+    }
+    .section-description {
+        color: #ddd;
+        margin-bottom: 1.5rem;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+# Main page content
+st.markdown("<h1 class='main-title'>🎨 AI Music and Image Generator</h1>", unsafe_allow_html=True)
+st.markdown("<p class='subtitle'>Welcome to the AI Generator app! Use the navigation menu to switch between generating music or images based on text prompts.</p>", unsafe_allow_html=True)
+
+# Brief description of each section
+st.markdown("### Available Sections:")
+col1, col2 = st.columns(2)
+
+with col1:
+    st.markdown("<div class='section-title'>🎵 Music Generator</div>", unsafe_allow_html=True)
+    st.markdown("<div class='section-description'>Transform your ideas into music using AI.</div>", unsafe_allow_html=True)
+
+
+with col2:
+    st.markdown("<div class='section-title'>🖼️ Image Generator</div>", unsafe_allow_html=True)
+    st.markdown("<div class='section-description'>Generate stunning images from your text descriptions.</div>", unsafe_allow_html=True)
+

musicgen.py

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+from transformers import AutoTokenizer, AutoModelForTextToWaveform
+import torch
+
+def init_musicgen(model_size='facebook/musicgen-small'):
+    """
+    Initialize the MusicGen model and tokenizer.
+    """
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(model_size)
+        model = AutoModelForTextToWaveform.from_pretrained(model_size)
+        return tokenizer, model
+    except Exception as e:
+        print(f"Error loading model or tokenizer: {str(e)}")
+        raise e
+
+def generate_music(text_prompt, tokenizer, model):
+    """
+    Generate music based on the input text prompt using the pre-initialized model.
+    """
+    print(f"Generating music for prompt: {text_prompt}")
+    
+    # Tokenize the input text
+    inputs = tokenizer(text_prompt, return_tensors='pt')
+    
+    # Check if inputs are valid
+    if 'input_ids' not in inputs or inputs['input_ids'] is None or inputs['input_ids'].size(0) == 0:
+        raise ValueError("Tokenized inputs are empty or invalid. Ensure your input prompt is valid.")
+
+    # Generate the music waveform
+    with torch.no_grad():
+        generated_waveform = model.generate(**inputs)
+
+    return generated_waveform.cpu().numpy().squeeze()