Upload app.py

app.py

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+import json
+import math
+import random
+import os
+import streamlit as st
+import lyricsgenius
+import transformers
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+
+st.set_page_config(page_title="HuggingArtists")
+
+
+st.title("HuggingArtists")
+st.sidebar.markdown(
+    """
+<style>
+.aligncenter {
+    text-align: center;
+}
+</style>
+<p class="aligncenter">
+    <img src="https://raw.githubusercontent.com/AlekseyKorshuk/huggingartists/master/img/logo.jpg" width="420" />
+</p>
+""",
+    unsafe_allow_html=True,
+)
+st.sidebar.markdown(
+    """
+<style>
+.aligncenter {
+    text-align: center;
+}
+</style>
+
+<p style='text-align: center'>
+<a href="https://github.com/AlekseyKorshuk/huggingartists" target="_blank">GitHub</a> | <a href="https://wandb.ai/huggingartists/huggingartists/reportlist" target="_blank">Project Report</a>
+</p>
+
+<p class="aligncenter">
+    <a href="https://github.com/AlekseyKorshuk/huggingartists" target="_blank"> 
+        <img src="https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social"/>
+    </a>
+</p>
+<p class="aligncenter">
+    <a href="https://t.me/joinchat/_CQ04KjcJ-4yZTky" target="_blank"> 
+        <img src="https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram"/>
+    </a>
+</p>
+<p class="aligncenter">
+  <a href="https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb" target="_blank"> 
+        <img src="https://colab.research.google.com/assets/colab-badge.svg"/>
+    </a>
+</p>
+    """,
+    unsafe_allow_html=True,
+)
+
+
+
+st.sidebar.header("Generation settings:")
+num_sequences = st.sidebar.number_input(
+    "Number of sequences to generate",
+    min_value=1,
+    value=5,
+    help="The amount of generated texts",
+)
+min_length = st.sidebar.number_input(
+    "Minimum length of the sequence",
+    min_value=1,
+    value=100,
+    help="The minimum length of the sequence to be generated",
+)
+max_length= st.sidebar.number_input(
+    "Maximum length of the sequence",
+    min_value=1,
+    value=160,
+    help="The maximum length of the sequence to be generated",
+)
+temperature = st.sidebar.slider(
+    "Temperature",
+    min_value=0.0,
+    max_value=3.0,
+    step=0.01,
+    value=1.0,
+    help="The value used to module the next token probabilities",
+)
+top_p = st.sidebar.slider(
+    "Top-P",
+    min_value=0.0,
+    max_value=1.0,
+    step=0.01,
+    value=0.95,
+    help="If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.",
+)
+
+top_k= st.sidebar.number_input(
+    "Top-K",
+    min_value=0,
+    value=50,
+    step=1,
+    help="The number of highest probability vocabulary tokens to keep for top-k-filtering.",
+)
+
+caption = (
+    "In [HuggingArtists](https://github.com/AlekseyKorshuk/huggingartist), we can generate lyrics by a specific artist. This was made by fine-tuning a pre-trained [HuggingFace Transformer](https://huggingface.co) on parsed datasets from [Genius](https://genius.com)."
+)
+st.markdown("[HuggingArtists](https://github.com/AlekseyKorshuk/huggingartist) - Train a model to generate lyrics 🎵")
+st.markdown(caption)
+
+st.subheader("Settings:")
+artist_name = st.text_input("Artist name:", "Eminem")
+start = st.text_input("Beginning of the song:", "But for me to rap like a computer")
+
+TOKEN = "q_JK_BFy9OMiG7fGTzL-nUto9JDv3iXI24aYRrQnkOvjSCSbY4BuFIindweRsr5I"
+genius = lyricsgenius.Genius(TOKEN)
+
+model_html = """
+
+<div class="inline-flex flex-col" style="line-height: 1.5;">
+    <div class="flex">
+        <div
+\t\t\tstyle="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;USER_PROFILE&#39;)">
+        </div>
+    </div>
+    <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
+    <div style="text-align: center; font-size: 16px; font-weight: 800">USER_NAME</div>
+    <a href="https://genius.com/artists/USER_HANDLE">
+    \t<div style="text-align: center; font-size: 14px;">@USER_HANDLE</div>
+    </a>
+</div>
+"""
+
+
+def post_process(output_sequences):
+    predictions = []
+    generated_sequences = []
+
+    max_repeat = 2
+
+    # decode prediction
+    for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
+        generated_sequence = generated_sequence.tolist()
+        text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True, skip_special_tokens=True)
+        generated_sequences.append(text.strip())
+                    
+    for i, g in enumerate(generated_sequences):
+        res = str(g).replace('\n\n\n', '\n').replace('\n\n', '\n')
+        lines = res.split('\n')
+        # print(lines)
+        # i = max_repeat
+        # while i != len(lines):
+        #   remove_count = 0
+        #   for index in range(0, max_repeat):
+        #     # print(i - index - 1, i - index)
+        #     if lines[i - index - 1] == lines[i - index]:
+        #       remove_count += 1
+        #   if remove_count == max_repeat:
+        #     lines.pop(i)
+        #     i -= 1
+        #   else:
+        #     i += 1
+        predictions.append('\n'.join(lines))
+
+    return predictions
+
+if st.button("Run"):
+    model_name = None
+    with st.spinner(text=f"Searching for {artist_name } in Genius..."):
+        artist = genius.search_artist(artist_name, max_songs=0, get_full_info=False)
+        if artist is not None:
+            artist_dict = genius.artist(artist.id)['artist']
+            artist_url = str(artist_dict['url'])
+            model_name = artist_url[artist_url.rfind('/') + 1:].lower()
+            st.markdown(model_html.replace("USER_PROFILE",artist.image_url).replace("USER_NAME",artist.name).replace("USER_HANDLE",model_name), unsafe_allow_html=True)
+        else:
+            st.markdown(f"Could not find {artist_name}! Be sure that he/she exists in [Genius](https://genius.com/).")
+    if model_name is not None:
+        with st.spinner(text=f"Downloading the model of {artist_name }..."):
+            model = None
+            tokenizer = None
+            try:
+                tokenizer = AutoTokenizer.from_pretrained(f"huggingartists/{model_name}")
+                model = AutoModelForCausalLM.from_pretrained(f"huggingartists/{model_name}")
+            except Exception as ex:
+                # st.markdown(ex)
+                st.markdown(f"Model for this artist does not exist yet. Create it in just 5 min with [Colab Notebook](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb):")
+                st.markdown(
+                        """
+                    <style>
+                    .aligncenter {
+                        text-align: center;
+                    }
+                    </style>
+                    <p class="aligncenter">
+                      <a href="https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb" target="_blank"> 
+                            <img src="https://colab.research.google.com/assets/colab-badge.svg"/>
+                        </a>
+                    </p>
+                        """,
+                        unsafe_allow_html=True,
+                    )
+    if model is not None:
+        with st.spinner(text=f"Generating lyrics..."):
+            encoded_prompt = tokenizer(start, add_special_tokens=False, return_tensors="pt").input_ids
+            encoded_prompt = encoded_prompt.to(model.device)
+            # prediction
+            output_sequences = model.generate(
+                                    input_ids=encoded_prompt,
+                                    max_length=max_length,
+                                    min_length=min_length,
+                                    temperature=float(temperature),
+                                    top_p=float(top_p),
+                                    top_k=int(top_k),
+                                    do_sample=True,
+                                    repetition_penalty=1.0,
+                                    num_return_sequences=num_sequences
+                                    )
+            # Post-processing
+            predictions = post_process(output_sequences)
+            st.subheader("Results")
+            for prediction in predictions:
+                st.text(prediction)
+            st.subheader("Please star this repository and join my Telegram Channel:")
+            st.markdown(
+                        """
+                    <style>
+                    .aligncenter {
+                        text-align: center;
+                    }
+                    </style>
+                    <p class="aligncenter">
+                        <a href="https://github.com/AlekseyKorshuk/huggingartists" target="_blank"> 
+                            <img src="https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social"/>
+                        </a>
+                    </p>
+                    <p class="aligncenter">
+                        <a href="https://t.me/joinchat/_CQ04KjcJ-4yZTky" target="_blank"> 
+                            <img src="https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram"/>
+                        </a>
+                    </p>
+                        """,
+                        unsafe_allow_html=True,
+                    )