Upload app.py

app.py

@@ -1,220 +1,152 @@
-import gradio as gr
-import time
+import subprocess
+import sys
+
+subprocess.check_call([sys.executable,"-m","pip","install",'causal-conv1d'])
+subprocess.check_call([sys.executable, "-m", "pip", "install", 'torch', 'numpy', 'miditok','mamba-ssm','gradio'])
+subprocess.check_call(["apt-get", "install", "timidity", "-y"])
+
+# !pip install pretty_midi midi2audio
+# !pip install miditok
+# !apt-get install fluidsynth
+# !apt install timidity -y
+# !pip install causal-conv1d>=1.1.0
+# !pip install mamba-ssm
+# !pip install gradio
+
+
+
+# !export LC_ALL="en_US.UTF-8"
+# !export LD_LIBRARY_PATH="/usr/lib64-nvidia"
+# !export LIBRARY_PATH="/usr/local/cuda/lib64/stubs"
+
+# subprocess.check_call(['export', 'LC_ALL="en_US.UTF-8"'])
+# subprocess.check_call(['export', 'LD_LIBRARY_PATH="/usr/lib64-nvidia"'])
+# subprocess.check_call(['export', 'LIBRARY_PATH="/usr/local/cuda/lib64/stubs"'])
 import os
-import shutil
-import streamlit as st
-openai_api = st.secrets["OPENAI_API_KEY"]
-
-doc_store_path = os.path.join(os.path.dirname(__file__), "doc_dir")
-if not os.path.isdir(doc_store_path):
-    os.makedirs(doc_store_path)
-
-from llama_index.core import SimpleDirectoryReader, VectorStoreIndex,Settings
-from llama_index.core.node_parser import SentenceSplitter,SemanticSplitterNodeParser
-from llama_index.llms.openai import OpenAI
-from llama_index.llms.openai import OpenAI as OpenAIsum
-from llama_index.embeddings.openai import OpenAIEmbedding
-from llama_index.core.storage import StorageContext
-from llama_index.vector_stores.chroma import ChromaVectorStore
-from llama_index.core.storage.chat_store import SimpleChatStore
-from llama_index.core.memory import ChatMemoryBuffer,ChatSummaryMemoryBuffer
-
-import json
-import chromadb
-import tiktoken
-
-
-chat_store = SimpleChatStore()
-# chat_memory = ChatMemoryBuffer.from_defaults(
-#     token_limit=3000,
-#     chat_store=chat_store,
-#     chat_store_key="user1",
-# )
-
-
-sum_llm = OpenAIsum(api_key=openai_api, model="gpt-3.5-turbo", max_tokens=256)
-chat_summary_memory = ChatSummaryMemoryBuffer.from_defaults(
-    token_limit=256,
-    chat_store=chat_store,
-    chat_store_key="user1",
-    llm = sum_llm,
-    tokenizer_fn = tiktoken.encoding_for_model("gpt-3.5-turbo").encode
-)
-
-
-chat_store = SimpleChatStore.from_persist_path(
-    persist_path="chat_store.json"
-)
-
-
-
-# documents = SimpleDirectoryReader("./data").load_data()
-db = chromadb.PersistentClient(path="./chroma_db")
-
-chroma_collection = db.get_or_create_collection("quickstart")
-
-vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
-storage_context = StorageContext.from_defaults(vector_store=vector_store)
-
-Settings.llm = OpenAI(model="gpt-3.5-turbo",api_key=openai_api,)
-Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002")
-
-vector_index = VectorStoreIndex.from_vector_store(vector_store, storage_context=storage_context,)
-query_engine = vector_index.as_chat_engine(chat_memory=chat_summary_memory,storage_context=storage_context,use_async=True,similarity_top_k=2)
-
-current_refs = ""
-
-def metadata_from_doc(vec_index: VectorStoreIndex) -> dict:
-    qe = vec_index.as_chat_engine()
-    # f_prompt = """
-    # Given the text excerpts, analyze and provide the document's title and creation date in a structured JSON format. Here are a few examples:
-    
-    # In this format:
-
-    # {
-    # "creation_date": "YYYY-MM-DD",
-    # "title": "Title of the Document"
-    # }
-
-    # Text: 'An analysis of historical events. Written by Alex Johnson on 5 March 2019.'
-    # Output: { "title": "An analysis of historical events", "creation_date": "2019-03-05" }
-
-    # Text: 'Exploring the depths of the ocean. This comprehensive guide was authored by Dr. Emily White, published on 10-July 2021.'
-    # Output: { "title": "Exploring the depths of the ocean", "creation_date": "2021-07-10" }
-
-    # Text: 'The history of the Roman Empire.'
-    # Output: { "title": "The history of the Roman Empire", "creation_date": "Unknown" }
-
-
-    # Now, analyze the context from the provided document and generate json object.
-    # """
-    f_prompt ="""give me a only the data when this document was written and title of this document? in json format parameter (created_date,title),
-        example context: 'An analysis of historical events. Written by Alex Johnson on 5 March 2019.'
-        example output: { "title": "An analysis of historical events", "creation_date": "2019-03-05" }
-    now analyse the context make sure to return output only in json format object only.
-    """
-    res = qe.query(f_prompt)
-    parsed = json.loads(res.response)
-    return parsed
-
-def filter_unsaved(file_paths:list):
-    for i in file_paths:
-        if os.path.isfile(os.path.join(doc_store_path,os.path.basename(i))):
-            file_paths.remove(i)
-            print("File already exist : {}".format(i))
-        else:
-            shutil.copy2(i,doc_store_path)
-    return file_paths
-
-def add_doc(file_paths:list):
-    print(file_paths)
-    file_paths = filter_unsaved(file_paths)
-    print(file_paths)
-    if len(file_paths) == 0:
-        return
-    docs = SimpleDirectoryReader(input_files=file_paths).load_data()
-    splitter = SemanticSplitterNodeParser(buffer_size=1, breakpoint_percentile_threshold=95, embed_model=Settings.embed_model,chunk_size=256)
-    nodes = splitter.get_nodes_from_documents(docs)
-    vector_index2 = VectorStoreIndex(nodes)
-    for i in range (5):
-        try:
-            meta = metadata_from_doc(vector_index2)
-            break
-        except:
-            meta = {
-                "title": "Unknown",
-                "creation_date": "Unknown"
-            }
-            continue
-    
-    print(meta)
-    for i in range(len(nodes)):
-        nodes[i].metadata.update(meta)
-    vector_index.insert_nodes(nodes)
-    
-
-
-
-
-CSS ="""
-.contain { display: flex; flex-direction: column; }
-.gradio-container { height: 100vh !important; }
-#component-0 { height: 100%; }
-#chatbot { flex-grow: 1; overflow: auto;}
+
+os.environ['LC_ALL'] = "en_US.UTF-8"
+os.environ['LD_LIBRARY_PATH'] = "/usr/lib64-nvidia"
+os.environ['LIBRARY_PATH'] = "/usr/local/cuda/lib64/stubs"
+
+
+
+import gradio as gr
+import torch
+from mamba_ssm import Mamba
+from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel
+from mamba_ssm.models.config_mamba import MambaConfig
+import numpy as np
+
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+if torch.cuda.is_available():
+    subprocess.check_call(['ldconfig', '/usr/lib64-nvidia'])
+    # !ldconfig /usr/lib64-nvidia
+
+# !wget "https://huggingface.co/krystv/MIDI_Mamba-159M/resolve/main/MIDI_Mamba-159M_1536VS.pt"
+# !wget "https://huggingface.co/krystv/MIDI_Mamba-159M/resolve/main/tokenizer_1536mix_BPE.json"
+if os.path.isfile("MIDI_Mamba-159M_1536VS.pt") == False:
+    subprocess.check_call(['wget', 'https://huggingface.co/krystv/MIDI_Mamba-159M/resolve/main/MIDI_Mamba-159M_1536VS.pt'])
+
+if os.path.isfile("tokenizer_1536mix_BPE.json") == False:
+    subprocess.check_call(['wget', 'https://huggingface.co/krystv/MIDI_Mamba-159M/resolve/main/tokenizer_1536mix_BPE.json'])
+
+
+
+mc = MambaConfig()
+mc.d_model = 768
+mc.n_layer = 42
+mc.vocab_size = 1536
+
+from miditok import MIDILike,REMI,TokenizerConfig
+from pathlib import Path
+import torch
+
+tokenizer = REMI(params='tokenizer_1536mix_BPE.json')
+
+
+
+mf = MambaLMHeadModel(config=mc,device=device)
+mf.load_state_dict(torch.load("/content/MIDI_Mamba-159M_1536VS.pt",map_location=device))
+
+
+
+twitter_follow_link = "https://twitter.com/iamhemantindia"
+instagram_follow_link = "https://instagram.com/iamhemantindia"
+
+custom_html = f"""
+<div style='text-align: center;'>
+    <a href="{twitter_follow_link}" target="_blank" style="margin-right: 5px;">
+        <img src="https://img.icons8.com/fluent/24/000000/twitter.png" alt="Follow on Twitter"/>
+    </a>
+    <a href="{instagram_follow_link}" target="_blank">
+        <img src="https://img.icons8.com/fluent/24/000000/instagram-new.png" alt="Follow on Instagram"/>
+    </a>
+</div>
 """
 
 
-def new_chat(chatbot:gr.Chatbot,textbox):
-    query_engine.reset()
-    return gr.update(value=""),[],"",gr.File(visible=False),gr.File(visible=False)
-
-
-def chat(history, input):
-    response = query_engine.chat(str(input))
-    global current_refs
-    files = []
-    current_refs = ""
-    for node in response.source_nodes:
-        try:
-            current_refs += f"{str(node.metadata['title'])},"
-        except:
-            current_refs += ""
-        try:
-            current_refs += f"Pg - {str(node.metadata['page_label'])},"
-        except:
-            current_refs += "Pg - ,"
-        try:
-            current_refs += f"File - {str(node.metadata['file_name'])} \n,"
-        except:
-            current_refs += "File - ,\n"
-        
-        try:
-            files.append({'path':node.metadata['file_path'],'show':True,})
-        except:
-            files.append({'path':None,'show':False,})
-
-    if len(files) < 2:
-        for _ in range(2-len(files)):
-            files.append({'path':None,'show':False,})
-        
-    return gr.update(value=""),history + [(input, response.response)],current_refs,gr.update(visible=files[0]['show'],value=files[0]['path']),gr.update(visible=files[1]['show'],value=files[1]['path'])
-
-def file_upload(file,chatbot):
-    print(file)
-    add_doc(file)
-    return gr.update(value="ChatDoc"),chatbot
-
-with gr.Blocks(fill_height=True, css=CSS) as demo:
+@spaces.GPU(duration=120)
+def generate(number,top_k_selector,top_p_selector, temperature_selector):
+    input_ids = torch.tensor([[1,]]).to(device)
+    out = mf.generate(
+    input_ids=input_ids,
+    max_length=int(number),
+    temperature=temperature_selector,
+    top_p=top_p_selector,
+    top_k=top_k_selector,
+
+    eos_token_id=2,)
+    m = tokenizer.decode(np.array(out[0].to('cpu')))
+    np.array(out.to('cpu')).shape
+    m.dump_midi('output.mid')
+    # !timidity output.mid -Ow -o - | ffmpeg -y -f wav -i - output.mp3
+    timidity_cmd = ['timidity', 'output.mid', '-Ow', '-o', 'output.wav']
+    subprocess.check_call(timidity_cmd)
+
+    # Then convert the WAV to MP3 using ffmpeg
+    ffmpeg_cmd = ['ffmpeg', '-y', '-f', 'wav', '-i', 'output.wav', 'output.mp3']
+    subprocess.check_call(ffmpeg_cmd)
+
+    return "output.mp3"
+
+
+# text_box = gr.Textbox(label="Enter Text")
+
+
+def generate_and_save(number,top_k_selector,top_p_selector, temperature_selector,generate_button,custom_html_wid):
+    output_audio = generate(number,top_k_selector,top_p_selector, temperature_selector)
+    return gr.Audio(output_audio,autoplay=True),gr.File(label="Download MIDI",value="output.mid"),generate_button
+
+
+
+
+
+
+# iface = gr.Interface(fn=generate_and_save,
+#  inputs=[number_selector,top_k_selector,top_p_selector, temperature_selector,generate_button,custom_html_wid],
+#   outputs=[output_box,download_midi_button],
+#    title="MIDI Mamba-159M",submit_btn=False,
+#    clear_btn=False,
+#    description="MIDI Mamba is a Mamba based model trained on MIDI data collected from open internet to train music model.",
+#    allow_flagging=False,)
+
+with gr.Blocks() as b1:
+    gr.Markdown("<h1 style='text-align: center;'>MIDI Mamba-159M <h1/> ")
+    gr.Markdown("<h3 style='text-align: center;'>MIDI Mamba is a Mamba based model trained on MIDI data collected from open internet to train music model. <br> by Hemant Kumar<h3/>")
     with gr.Row():
-        with gr.Column(scale=1):
-            title = gr.Label(value="chatdoc", label="ChatDoc")
-            files = gr.UploadButton(
-                    "📁 Upload PDF or doc files", file_types=[
-                        '.pdf',
-                        '.doc'
-                    ],
-                    file_count="multiple")
-            references = gr.Textbox(label="References",interactive=False)
-            file_down1 = gr.File(visible=False)
-            file_down2 = gr.File(visible=False) 
-            
-
-        
-        with gr.Column(scale=9,):
-            chatbot = gr.Chatbot(
-                elem_id="chatbot",
-                bubble_full_width=False,
-                label="ChatDoc",
-                avatar_images=["https://www.freeiconspng.com/thumbs/person-icon-blue/person-icon-blue-25.png","https://cdn-icons-png.flaticon.com/512/8943/8943377.png"],
-            )
-            with gr.Row():
-                textbox = gr.Textbox(label="Type your message", scale=10)
-                clear = gr.Button(value="New Chat", size="sm", scale=1)
-                clear.click(new_chat,[],[textbox, chatbot,references,file_down1,file_down2])
-                textbox.submit(chat, [chatbot, textbox], [textbox, chatbot,references,file_down1,file_down2])
-                
-
-        files.upload(file_upload,[files,chatbot],[title,chatbot])
-                
-    
-demo.launch(share=True)
+        with gr.Column():
+            number_selector = gr.Number(label="Select Length of output",value=512)
+            top_p_selector = gr.Slider(label="Select Top P", minimum=0, maximum=1.0, step=0.05, value=0.9)
+            temperature_selector = gr.Slider(label="Select Temperature", minimum=0, maximum=1.0, step=0.1, value=0.9)
+            top_k_selector = gr.Slider(label="Select Top K", minimum=1, maximum=1536, step=1, value=30)
+            generate_button = gr.Button(value="Generate",variant="primary")
+            custom_html_wid = gr.HTML(custom_html)
+        with gr.Column():
+            output_box = gr.Audio("output.mp3",autoplay=True,)
+            download_midi_button = gr.File(label="Download MIDI")
+            generate_button.click(generate_and_save,inputs=[number_selector,top_k_selector,top_p_selector, temperature_selector,generate_button,custom_html_wid],outputs=[output_box,download_midi_button,generate_button])
+
+
+
+
+b1.launch(share=True)