add slide show for multiple images

app.py

@@ -5,6 +5,8 @@ import numpy as np
 import gradio as gr
 
 import PIL
+from PIL import Image
+from moviepy.editor import *
 
 from lib.audio_generation import AudioGeneration
 from lib.image_captioning import ImageCaptioning
@@ -25,35 +27,79 @@ class AudioPalette:
             "Slow": "low"
         }
     
-    def generate(self, input_image: PIL.Image.Image, instrument: typing.Union[str, None], ngrok_endpoint: str):
+    def prompt_construction(self, caption: str, pace: str, instrument: typing.Union[str, None], first: bool = True):
+        instrument = instrument if instrument is not None else ""
+
+        if first:
+            prompt = f"A {instrument} soundtrack for {caption} with {self.pace_map[pace]} beats per minute. High Quality"
+        else:
+            prompt = f"A {instrument} soundtrack for {caption} with {self.pace_map[pace]} beats per minute. High Quality. Transitions smoothely from the previous audio while sounding different."
+        
+        return prompt
+    
+    def generate_single(self, input_image: PIL.Image.Image, instrument: typing.Union[str, None], ngrok_endpoint: str):
         pace = self.pace_model.predict(input_image)
         print("Pace Prediction Done")
 
         generated_text = self.image_captioning.query(input_image)[0].get("generated_text")
         print("Captioning Done")
-
         generated_text = generated_text if generated_text is not None else ""
-        # prompt = f"Generate a soundtrack for {generated_text} with {pace} beats and the instrument of choice is the guitar, High quality"
 
-        if instrument is None:
-            prompt = f"A soundtrack for {generated_text} with {self.pace_map[pace]} beats per minute. High quality."
-        else:
-            prompt = f"A {instrument} soundtrack for {generated_text} with {self.pace_map[pace]} beats per minute. High quality."
-        
+        prompt = self.prompt_construction(generated_text, pace, instrument)
         print("Generated Prompt:", prompt)
 
         audio_file = self.audio_generation.generate(prompt, ngrok_endpoint)
         print("Audio Generation Done")
 
         outputs = [prompt, pace, generated_text, audio_file]
-
         return outputs
+    
+    def stitch_images(self, file_paths: typing.List[str], audio_paths: typing.List[str]):
+        clips = [ImageClip(m).set_duration(5) for m in file_paths]
+        audio_clips = [AudioFileClip(a) for a in audio_paths]
+        concat_audio = concatenate_audioclips(audio_clips)
+        new_audio = CompositeAudioClip([concat_audio])
 
-def main():
-    model = AudioPalette()
+        concat_clip = concatenate_videoclips(clips, method="compose")
+        concat_clip.audio = new_audio
+        
+        file_name = "generated_video.mp4"
+        concat_clip.write_videofile(file_name, fps=24)
+        return file_name
+    
+    def generate_multiple(self, file_paths: typing.List[str], instrument: typing.Union[str, None], ngrok_endpoint: str):
+        images = [Image.open(image_path) for image_path in file_paths]
+        pace = []
+        generated_text = []
+        prompts = []
+
+        # Extracting the pace for all the images
+        for image in images:
+            pace_prediction = self.pace_model.predict(image)
+            pace.append(pace_prediction)
+        print("Pace Prediction Done")
+        
+        # Generating the caption for all the images
+        for image in images:
+            caption = self.image_captioning.query(image)[0].get("generated_text")
+            generated_text.append(caption)
+        print("Captioning Done")
+
+        first = True
+        for generated_caption, pace_pred in zip(generated_text, pace):
+            prompts.append(self.prompt_construction(generated_caption, pace_pred, instrument, first))
+            first = False
+        print("Generated Prompts: ", prompts)
+
+        audio_file = self.audio_generation.generate(prompts, ngrok_endpoint)
+        print("Audio Generation Done")
+
+        video_file = self.stitch_images(file_paths, [audio_file])
+        return video_file
 
+def single_image_interface(model: AudioPalette):
     demo = gr.Interface(
-        fn=model.generate,
+        fn=model.generate_single,
         inputs=[
             gr.Image(
                 type="pil",
@@ -117,10 +163,64 @@ def main():
         ],
         cache_examples=False,
         live=False,
-        title="Audio Palette",
         description="Provide an image to generate an appropriate background soundtrack",
     )
 
+    return demo
+
+def multi_image_interface(model: AudioPalette):
+    demo = gr.Interface(
+        fn=model.generate_multiple,
+        inputs=[
+            gr.File(
+                file_count="multiple",
+                file_types=["image"],
+                type="filepath",
+                label="Upload images",
+                show_label=True,
+                container=True,
+                visible=True
+            ),
+            gr.Radio(
+                choices=["Piano", "Drums", "Guitar", "Violin", "Flute"],
+                label="Instrument",
+                show_label=True,
+                container=True
+            ),
+            gr.Textbox(
+                lines=1,
+                placeholder="ngrok endpoint",
+                label="colab endpoint",
+                show_label=True,
+                container=True,
+                type="text",
+                visible=True
+            )
+        ],
+        outputs=[
+            gr.Video(
+                format="mp4",
+                label="Generated Video",
+                show_label=True,
+                container=True,
+                visible=True,
+                autoplay=False,
+            )
+        ],
+        cache_examples=False,
+        live=False,
+        description="Provide images to generate an a slideshow of the images with appropriate music as background",
+    )
+
+    return demo
+
+def main():
+    model = AudioPalette()
+
+    tab_1 = single_image_interface(model)
+    tab_2 = multi_image_interface(model)
+
+    demo = gr.TabbedInterface([tab_1, tab_2], ["Single Image", "Slide Show"], "Audio Palette")
     demo.queue().launch()
 
 if __name__ == "__main__":

lib/audio_generation.py

@@ -8,17 +8,14 @@ class AudioGeneration:
     def __init__(self):
         self.session = requests.session()
     
-    def request_single(self, prompt: str):
-        response = self.session.post(self.endpoint + "single", json={
+    def request_generation(self, prompt: typing.Union[str, typing.List[str]]):
+        response = self.session.post(self.endpoint + "generate", json={
             "caption": prompt
         })
 
         file_path = response.json().get("file_path")
         return file_path
     
-    def request_multiple(self, prompt: typing.List[str]):
-        pass
-    
     def request_download(self, file_path: str):
         response = self.session.post(self.endpoint + "download", json={
             "file_path": file_path
@@ -33,9 +30,8 @@ class AudioGeneration:
     
     def generate(self, prompt: typing.Union[str, typing.List[str]], endpoint: str):
         self.endpoint = endpoint
-        if isinstance(prompt, str):
-            stored_file_path = self.request_single(prompt)
-            audio_file = self.request_download(stored_file_path)
-            print(audio_file)
-
-            return audio_file
+        stored_file_path = self.request_generation(prompt)
+        
+        audio_file = self.request_download(stored_file_path)
+        print(audio_file)
+        return audio_file

lib/image_captioning.py

@@ -13,6 +13,7 @@ class ImageCaptioning:
         self.api_endpoint = os.environ["blip_api_url"]
         self.org_token = os.environ["auth_token"]
         self.headers = { "Authorization": f"Bearer {self.org_token}" }
+        self.session = requests.session()
     
     def convert_to_bytes(self, image: PIL.Image.Image):
         data = io.BytesIO()
@@ -20,7 +21,7 @@ class ImageCaptioning:
         return data.getvalue()
 
     def query(self, image: PIL.Image.Image):
-        response = requests.post(
+        response = self.session.post(
             self.api_endpoint,
             headers=self.headers,
             data=self.convert_to_bytes(image)

notebooks/AudioPalette.ipynb

@@ -1 +1,369 @@
-{"cells":[{"cell_type":"markdown","metadata":{"id":"w4LtdMb23tZ4"},"source":["## Prerequisites"]},{"cell_type":"markdown","metadata":{"id":"JgJLBIh3fm-W"},"source":["### Install Dependencies"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1701192586082,"user":{"displayName":"Ari T","userId":"03786391891572230771"},"user_tz":-330},"id":"EodUpreufqD-","outputId":"35816992-683c-486b-c174-8f4ab5b30e0b"},"outputs":[],"source":["!nvidia-smi"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":55044,"status":"ok","timestamp":1701192641119,"user":{"displayName":"Ari T","userId":"03786391891572230771"},"user_tz":-330},"id":"bOn11huvfuXc","outputId":"c6f34e65-6253-4942-a779-a74ce6357aab"},"outputs":[],"source":["!pip install --upgrade --quiet pip\n","!pip install --quiet git+https://github.com/huggingface/transformers.git"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":69977,"status":"ok","timestamp":1701192711087,"user":{"displayName":"Ari T","userId":"03786391891572230771"},"user_tz":-330},"id":"Nl0CQxwHCrFd","outputId":"a8c56186-23d3-4531-ff6a-11d6f61126b7"},"outputs":[],"source":["!pip install typing-extensions==4.5.0\n","!pip install python-multipart\n","!pip install kaleido\n","!pip install notebook>=6.5.5\n","!pip install click>=8.0\n","!pip install fastapi\n","!pip install \"uvicorn[standard]\"\n","!pip install pyngrok"]},{"cell_type":"markdown","metadata":{"id":"ROxnljVbf6_o"},"source":["### Load the models"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":0,"referenced_widgets":["d4f059066f534812aca7e55fe6ba37e0","1b1fb646414f463a941e757091c424cd","79e2820e80f4485e9b0a5117c1b46ede","605f44b988aa4f0895befca0d3b3fb44","b341f1596ac04560b3b2622261b9d94d","a034a4e24e8448889cbc5299e736c21a","147b03786e5f475b804ffa0e4b3126f3","6dbce370c76643369b7bdd438055bbbc","b2f87a2a117e400da584b23e6bacd500","5b3123bd83fa4c618faf8c131de662ab","ec39db95dd1046839acc37bdb914897c","dd15ac5529c44fabb55b0b3a87f16775","83aea96092f94262b6023ca8f7fcd6ca","9b0fa2f21bf544529302e47b5051d277","f6bfec4da9ea48c3981ed9421cc5c4b1","75d31df6137146ba820e60a2c16d4411","461aa7211959411ab116bc82fd2165c1","e6a9861c661b47faab1b834ec2aa4c78","7f77fd6745a84a2da02d7d41170d7af1","bac33fb303c04630a8efdf0bfb9790fe","2de4bb7ced1e4e10b51c82df173c987e","bdbc056ec0144e8bb01ab20be4f2706f","69d12e4b48f444c48895818a0828cad4","c50604d9526e400d9356392517a322df","b45c4923d4e449e28261bb4396081154","36ae6a59a3c24c7b8cc01cf5139f7616","a2f296abcc044dbb891813180e99876b","5786b1750d0646d9a6dd4fd56cb8b37c","0743e86fd2144c598a6a523fdb91ced0","dd02cad84ab44023bb44d8c0e17f3eb6","6dbbf1304d934c399dd02138541f7931","011336465e784f298036ea6790fed052","ef8943bc8ff34a0a97b900767614b6c0","8f133aeec44844e0a8f2105ca25537ee","bc115429cdc34c96b6cb4f40bade275e","76503f0db07c43538621c313f229e665","ca9d933e16fa4fccb95378aa79e1ed23","c7558a6ad7bf46209766ba35400f6c71","a81e59481edd4684b65945a5a0e15b7e","de60244a16444f8190c964742c78bcee","106dfc82e3b8458db4d1d79cb9018ce9","bc7379793bcc4a9daacccd8e20fc4939","70fc7a6ff58c4b918dcd402ea47c71ed","256a058561ab454f88011d1ef5067dc3","b2ed07e5aaa843a1b03f91cec6f292c5","9edd2781f80d4ea1b44b3c0d9c6228cb","dca965fb49054b2994504063823d1e45","985f60b8f775420fbf96756681edeaa1","7c13143aaf9e4ea5a981c3e01255db09","067a2fb78e2f4880a7859f3d4443efc7","01d6152aa0a84b5797c0d8d7485ece6b","5e18669c53b34717a7ab136987993f8d","c560c9cadfec4b4d9712061e4f131d9a","5fd1148bd5b147748f308b096110b195","38fd91db4ae74f3ca1fb4c93a61b4e4a","56d1aa41461841f28cf66f4a4a36e4c5","a5a135f9d5c3400bb11e97d8701a0c46","ce2c8f75d3fe4a7ab74ff36f0c933d74","4efae1e9896d49d8924c653b99f88506","425da6ccf8a848edb50b9a9795863651","cf90e11688aa47dd9987e25e15c42ae9","7eb8c7361089468da8ac4ba8b5706e78","ca32f9fcad8c455b95e9be177b5b3c53","35fd990491554b4bb55ff1deb67f6085","9d327383466748548cd8300dc3db05c5","04e7660db17e436f8382a180d7add4d3","36e7a8aa309a4bcf901c8acc185bca8e","06afc51d1e5744df9809e20a9ed593c9","d4b778e99dc243358b8851d87433bb05","b63328001a7d42b1b763b088a022ff0b","0404cdaca76544809f91837630728dba","c9119148bfea46f0b518e0ad0c61cb20","80012bda0de7476fa223598421d046a9","155a5102300f47c894b2a11bcbba0e7d","27284fcd59974e9baa1cd6f42bbd317a","09e27493d427431a9d449289234f9bba","92645005948c4cd98f036838361a0f9e","46fb87d0ea4f4460bed643a196ca41b4","c69ee7b1381f43fbba5c0304d64f23ab","cb8343cd0b8240e49c12bb7f8cf8d41c","3911de4376a2466683f0696fae258e24","d4cd5a793b3c4a1898e28303e5e2f991","0a7f528a0e204b3288755779cc300c56","c0c8ba97b8004a8e8332122ec297022b","69ec4d4dff864a648e8d6c03a2439b11","52f77fbf33354baf9f881f76276b4764","dc6cee3737f74e34b291a3d63d8842ba","d6981569cb5341ff9e9166c5fc0d513f"]},"executionInfo":{"elapsed":43348,"status":"ok","timestamp":1701192754410,"user":{"displayName":"Ari T","userId":"03786391891572230771"},"user_tz":-330},"id":"ToxW1gbLf6tr","outputId":"6bc6b281-0f66-447c-946e-44f6985fff4a"},"outputs":[],"source":["from transformers import MusicgenForConditionalGeneration, MusicgenProcessor, set_seed\n","\n","model = MusicgenForConditionalGeneration.from_pretrained(\"facebook/musicgen-small\")\n","processor = MusicgenProcessor.from_pretrained(\"facebook/musicgen-small\")"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":10922,"status":"ok","timestamp":1701192765308,"user":{"displayName":"Ari T","userId":"03786391891572230771"},"user_tz":-330},"id":"omP9Hg1ajUKM"},"outputs":[],"source":["import torch\n","from IPython.display import Audio\n","\n","sampling_rate = model.config.audio_encoder.sampling_rate\n","device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n","model.to(device)\n","None"]},{"cell_type":"markdown","metadata":{"id":"852jZKSqiKoT"},"source":["## Music Generation functionality"]},{"cell_type":"markdown","metadata":{"id":"8nydshMdxKab"},"source":["#### Model Class"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":615,"status":"ok","timestamp":1701192778417,"user":{"displayName":"Ari T","userId":"03786391891572230771"},"user_tz":-330},"id":"4V49E7xpxNPu"},"outputs":[],"source":["import numpy as np\n","import typing\n","\n","class AudioPalette:\n","    def __init__(self):\n","        pass\n","\n","    def set_prompt(self, caption: str | typing.List[str]):\n","        self.caption = caption\n","\n","    def generate(self):\n","        if isinstance(self.caption, str):\n","            return self.generate_single()\n","        else:\n","            return self.generate_multiple()\n","\n","    def generate_single(self):\n","        inputs = processor(\n","            text=[self.caption],\n","            padding=True,\n","            return_tensors=\"pt\",\n","            sampling_rate=sampling_rate\n","        )\n","\n","        audio_values = model.generate(**inputs.to(device), do_sample=True, guidance_scale=3, max_new_tokens=256)\n","        return audio_values\n","\n","    def generate_multiple(self):\n","        return None"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":507,"status":"ok","timestamp":1701192783021,"user":{"displayName":"Ari T","userId":"03786391891572230771"},"user_tz":-330},"id":"qW65Q68o-R7f"},"outputs":[],"source":["audiopalette = AudioPalette()"]},{"cell_type":"markdown","metadata":{"id":"P3OmxnaBA9E-"},"source":["#### API Creation"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":555,"status":"ok","timestamp":1701195121766,"user":{"displayName":"Ari T","userId":"03786391891572230771"},"user_tz":-330},"id":"Dxlw3ODsTtuB"},"outputs":[],"source":["from fastapi import FastAPI\n","from pydantic import BaseModel, Field\n","from fastapi.middleware.cors import CORSMiddleware\n","\n","app = FastAPI()\n","\n","app.add_middleware(\n","    CORSMiddleware,\n","    allow_origins=['*'],\n","    allow_credentials=True,\n","    allow_methods=['*'],\n","    allow_headers=['*'],\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1701195124083,"user":{"displayName":"Ari T","userId":"03786391891572230771"},"user_tz":-330},"id":"iYUH3-GpfbN8"},"outputs":[],"source":["import typing\n","import numpy as np\n","\n","class Prompt(BaseModel):\n","    caption: str | typing.List[str]\n","\n","class FileData(BaseModel):\n","    file_path: str\n","\n","# class Melody(BaseModel):\n","#     audio: np.ndarray\n","\n","#     class Config:\n","#         arbitrary_types_allowed = True"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1701195126268,"user":{"displayName":"Ari T","userId":"03786391891572230771"},"user_tz":-330},"id":"PTQCdon0A9FA"},"outputs":[],"source":["import tempfile\n","import scipy\n","\n","from fastapi.responses import FileResponse\n","\n","@app.get('/')\n","async def root():\n","    return {\"message\": \"Hello World\"}\n","\n","@app.post('/download')\n","async def download(file_data: FileData):\n","    file_path = file_data.file_path\n","    return FileResponse(file_path)\n","\n","@app.post('/single')\n","async def single_image(prompt: Prompt):\n","    audiopalette.set_prompt(prompt.caption)\n","    audio = audiopalette.generate()\n","\n","    file_path = None\n","    with tempfile.NamedTemporaryFile(delete=False) as f:\n","        scipy.io.wavfile.write(f, rate=sampling_rate, data=audio[0, 0].cpu().numpy())\n","        file_path = f.name\n","\n","    if not file_path:\n","        return {\"error\": \"There has been an error\"}\n","    return {\"file_path\": f\"{file_path}\"}\n"]},{"cell_type":"markdown","metadata":{"id":"ALpNtVpHA9FA"},"source":["#### Run the API"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1701195128719,"user":{"displayName":"Ari T","userId":"03786391891572230771"},"user_tz":-330},"id":"w3eG8rfRA9FB"},"outputs":[],"source":["import nest_asyncio\n","from pyngrok import ngrok\n","import uvicorn"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":517685,"status":"ok","timestamp":1701195648057,"user":{"displayName":"Ari T","userId":"03786391891572230771"},"user_tz":-330},"id":"yOhMLxA5A9FB","outputId":"75844d00-b615-4a09-ac76-6b7dc212942d"},"outputs":[],"source":["ngrok_tunnel = ngrok.connect(8000)\n","print(\"Public URL:\", ngrok_tunnel.public_url)\n","nest_asyncio.apply()\n","uvicorn.run(app, port=8000)"]},{"cell_type":"markdown","metadata":{"id":"606pRql4A9FC"},"source":["#### Kill ngrok Connection"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":537,"status":"ok","timestamp":1701195650871,"user":{"displayName":"Ari T","userId":"03786391891572230771"},"user_tz":-330},"id":"k7Tbq8w-A9FC"},"outputs":[],"source":["ngrok.kill()"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":["w4LtdMb23tZ4"],"gpuType":"T4","provenance":[{"file_id":"https://github.com/csoneoneone/notebooks/blob/main/AudioPalette.ipynb","timestamp":1701164927931}]},"kernelspec":{"display_name":"Python 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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w4LtdMb23tZ4"
+      },
+      "source": [
+        "## Prerequisites"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JgJLBIh3fm-W"
+      },
+      "source": [
+        "### Install Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EodUpreufqD-"
+      },
+      "outputs": [],
+      "source": [
+        "!nvidia-smi"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bOn11huvfuXc"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install --upgrade --quiet pip\n",
+        "!pip install --quiet git+https://github.com/huggingface/transformers.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Nl0CQxwHCrFd"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install typing-extensions==4.5.0\n",
+        "!pip install python-multipart\n",
+        "!pip install kaleido\n",
+        "!pip install notebook>=6.5.5\n",
+        "!pip install click>=8.0\n",
+        "!pip install fastapi\n",
+        "!pip install \"uvicorn[standard]\"\n",
+        "!pip install pyngrok"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ROxnljVbf6_o"
+      },
+      "source": [
+        "### Load the models"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ToxW1gbLf6tr"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import MusicgenForConditionalGeneration, MusicgenProcessor, set_seed\n",
+        "\n",
+        "model = MusicgenForConditionalGeneration.from_pretrained(\"facebook/musicgen-small\")\n",
+        "processor = MusicgenProcessor.from_pretrained(\"facebook/musicgen-small\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "omP9Hg1ajUKM"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "from IPython.display import Audio\n",
+        "\n",
+        "sampling_rate = model.config.audio_encoder.sampling_rate\n",
+        "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
+        "model.to(device)\n",
+        "None"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "852jZKSqiKoT"
+      },
+      "source": [
+        "## Music Generation functionality"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8nydshMdxKab"
+      },
+      "source": [
+        "#### Model Class"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4V49E7xpxNPu"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import typing\n",
+        "\n",
+        "class AudioPalette:\n",
+        "    def __init__(self):\n",
+        "        pass\n",
+        "\n",
+        "    def set_prompt(self, caption: str | typing.List[str]):\n",
+        "        self.caption = caption\n",
+        "\n",
+        "    def generate(self):\n",
+        "        if isinstance(self.caption, str):\n",
+        "            return self.generate_single()\n",
+        "        else:\n",
+        "            return self.generate_multiple()\n",
+        "\n",
+        "    def generate_single(self, prompt=None, max_new_tokens=512):\n",
+        "        if not prompt:\n",
+        "            prompt = self.caption\n",
+        "        inputs = processor(\n",
+        "            text=[prompt],\n",
+        "            padding=True,\n",
+        "            return_tensors=\"pt\",\n",
+        "            sampling_rate=sampling_rate\n",
+        "        )\n",
+        "\n",
+        "        audio_values = model.generate(**inputs.to(device), do_sample=True, guidance_scale=3, max_new_tokens=max_new_tokens)\n",
+        "        return audio_values\n",
+        "\n",
+        "    def generate_audio_with_melody_conditioning(self, prompt, melody, max_new_tokens=256):\n",
+        "        inputs = processor(\n",
+        "            text=[prompt],\n",
+        "            audio=melody[0, 0].cpu().numpy(),\n",
+        "            padding=True,\n",
+        "            return_tensors=\"pt\",\n",
+        "            sampling_rate=sampling_rate\n",
+        "        )\n",
+        "\n",
+        "        # set_seed(1)\n",
+        "        audio_values = model.generate(**inputs.to(device), do_sample=True, guidance_scale=3, max_new_tokens=max_new_tokens)\n",
+        "        return audio_values\n",
+        "\n",
+        "    def generate_multiple(self):\n",
+        "        for idx, prompt in enumerate(self.caption):\n",
+        "            if idx == 0:\n",
+        "                audio = self.generate_single(prompt, 256)\n",
+        "            else:\n",
+        "                audio = self.generate_audio_with_melody_conditioning(prompt, audio)\n",
+        "        return audio"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qW65Q68o-R7f"
+      },
+      "outputs": [],
+      "source": [
+        "audiopalette = AudioPalette()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "P3OmxnaBA9E-"
+      },
+      "source": [
+        "#### API Creation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Dxlw3ODsTtuB"
+      },
+      "outputs": [],
+      "source": [
+        "from fastapi import FastAPI\n",
+        "from pydantic import BaseModel, Field\n",
+        "from fastapi.middleware.cors import CORSMiddleware\n",
+        "\n",
+        "app = FastAPI()\n",
+        "\n",
+        "app.add_middleware(\n",
+        "    CORSMiddleware,\n",
+        "    allow_origins=['*'],\n",
+        "    allow_credentials=True,\n",
+        "    allow_methods=['*'],\n",
+        "    allow_headers=['*'],\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iYUH3-GpfbN8"
+      },
+      "outputs": [],
+      "source": [
+        "import typing\n",
+        "import numpy as np\n",
+        "\n",
+        "class Prompt(BaseModel):\n",
+        "    caption: str | typing.List[str]\n",
+        "\n",
+        "class FileData(BaseModel):\n",
+        "    file_path: str\n",
+        "\n",
+        "# class Melody(BaseModel):\n",
+        "#     audio: np.ndarray\n",
+        "\n",
+        "#     class Config:\n",
+        "#         arbitrary_types_allowed = True"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PTQCdon0A9FA"
+      },
+      "outputs": [],
+      "source": [
+        "import tempfile\n",
+        "import scipy\n",
+        "\n",
+        "from fastapi.responses import FileResponse\n",
+        "\n",
+        "@app.get('/')\n",
+        "async def root():\n",
+        "    return {\"message\": \"Hello World\"}\n",
+        "\n",
+        "@app.post('/download')\n",
+        "async def download(file_data: FileData):\n",
+        "    file_path = file_data.file_path\n",
+        "    return FileResponse(file_path)\n",
+        "\n",
+        "@app.post('/generate')\n",
+        "async def gen_music(prompt: Prompt):\n",
+        "    audiopalette.set_prompt(prompt.caption)\n",
+        "    audio = audiopalette.generate()\n",
+        "\n",
+        "    file_path = None\n",
+        "    with tempfile.NamedTemporaryFile(delete=False) as f:\n",
+        "        scipy.io.wavfile.write(f, rate=sampling_rate, data=audio[0, 0].cpu().numpy())\n",
+        "        file_path = f.name\n",
+        "\n",
+        "    if not file_path:\n",
+        "        return {\"error\": \"There has been an error\"}\n",
+        "    return {\"file_path\": f\"{file_path}\"}\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ALpNtVpHA9FA"
+      },
+      "source": [
+        "#### Run the API"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "w3eG8rfRA9FB"
+      },
+      "outputs": [],
+      "source": [
+        "import nest_asyncio\n",
+        "from pyngrok import ngrok\n",
+        "import uvicorn"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yOhMLxA5A9FB"
+      },
+      "outputs": [],
+      "source": [
+        "ngrok_tunnel = ngrok.connect(8000)\n",
+        "print(\"Public URL:\", ngrok_tunnel.public_url)\n",
+        "nest_asyncio.apply()\n",
+        "uvicorn.run(app, port=8000)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "606pRql4A9FC"
+      },
+      "source": [
+        "#### Kill ngrok Connection"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "k7Tbq8w-A9FC"
+      },
+      "outputs": [],
+      "source": [
+        "ngrok.kill()"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [
+        "w4LtdMb23tZ4",
+        "46yDPBYqpYYN"
+      ],
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.10"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}