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app.py

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+import os
+os.system("pip install transformers")
+os.system("pip3 install torch==1.10.1+cpu torchvision==0.11.2+cpu torchaudio==0.10.1+cpu -f "
+          "https://download.pytorch.org/whl/cpu/torch_stable.html")
+os.system("pip install mtranslate")
+os.system("pip install requests")
+os.system("pip install random")
+
+import transformers
+import json
+import random
+import requests
+
+from mtranslate import translate
+import streamlit as st
+
+
+MODELS = {
+    "GPT-2 Model Recycled From English": {
+        "url": "https://api-inference.huggingface.co/models/GroNLP/gpt2-small-dutch"
+    },
+}
+
+PROMPT_LIST = {
+    "Er was eens...": ["Er was eens..."],
+    "Dag.": ["Hallo, mijn naam is "],
+    "Te zijn of niet te zijn?": ["Naar mijn mening is 'zijn'"],
+}
+
+
+def query(payload, model_name):
+    data = json.dumps(payload)
+    print("model url:", MODELS[model_name]["url"])
+    response = requests.request(
+        "POST", MODELS[model_name]["url"], headers={}, data=data
+    )
+    return json.loads(response.content.decode("utf-8"))
+
+
+def process(
+    text: str, model_name: str, max_len: int, temp: float, top_k: int, top_p: float
+):
+    payload = {
+        "inputs": text,
+        "parameters": {
+            "max_new_tokens": max_len,
+            "top_k": top_k,
+            "top_p": top_p,
+            "temperature": temp,
+            "repetition_penalty": 2.0,
+        },
+        "options": {
+            "use_cache": True,
+        },
+    }
+    return query(payload, model_name)
+
+
+# Page
+st.set_page_config(page_title="Dutch GPT-2 Demo")
+st.title("Dutch GPT-2")
+
+
+# Sidebar
+st.sidebar.subheader("Configurable parameters")
+
+max_len = st.sidebar.number_input(
+    "Maximum length",
+    value=100,
+    help="The maximum length of the sequence to be generated.",
+)
+
+temp = st.sidebar.slider(
+    "Temperature",
+    value=1.0,
+    min_value=0.1,
+    max_value=100.0,
+    help="The value used to module the next token probabilities.",
+)
+
+top_k = st.sidebar.number_input(
+    "Top k",
+    value=10,
+    help="The number of highest probability vocabulary tokens to keep for top-k-filtering.",
+)
+
+top_p = st.sidebar.number_input(
+    "Top p",
+    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.",
+)
+
+do_sample = st.sidebar.selectbox(
+    "Sampling?",
+    (True, False),
+    help="Whether or not to use sampling; use greedy decoding otherwise.",
+)
+
+
+# Body
+st.markdown(
+    """
+    Dutch GPT-2 model (small) is based on the English GPT-2 model:
+
+    Researches [Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) and [M. Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475)
+    obtained this model by transfering the English GPT-2 model in multiple procedure while exploiting genetic closeness between Dutch and English. 
+
+    During this process, they retrained the lexical embeddings of the original English GPT-2 model and did additional fine-tuning of the full Dutch model
+    for better text generation. 
+    
+    For more information on the model:
+    
+    [arXiv](https://arxiv.org/abs/2012.05628)
+    [GitHub](https://github.com/wietsedv/gpt2-recycle)
+    
+    """
+)
+
+model_name = st.selectbox("Model", (list(MODELS.keys())))
+
+ALL_PROMPTS = list(PROMPT_LIST.keys()) + ["Custom"]
+prompt = st.selectbox("Prompt", ALL_PROMPTS, index=len(ALL_PROMPTS) - 1)
+if prompt == "Custom":
+    prompt_box = "Enter your text here"
+else:
+    prompt_box = random.choice(PROMPT_LIST[prompt])
+
+text = st.text_area("Enter text", prompt_box)
+
+if st.button("Run"):
+    with st.spinner(text="Getting results..."):
+        st.subheader("Result")
+        print(f"maxlen:{max_len}, temp:{temp}, top_k:{top_k}, top_p:{top_p}")
+        result = process(
+            text=text,
+            model_name=model_name,
+            max_len=int(max_len),
+            temp=temp,
+            top_k=int(top_k),
+            top_p=float(top_p),
+        )
+        print("result:", result)
+        if "error" in result:
+            if type(result["error"]) is str:
+                st.write(f'{result["error"]}.', end=" ")
+                if "estimated_time" in result:
+                    st.write(
+                        f'Please try again in about {result["estimated_time"]:.0f} seconds.'
+                    )
+            else:
+                if type(result["error"]) is list:
+                    for error in result["error"]:
+                        st.write(f"{error}")
+        else:
+            result = result[0]["generated_text"]
+            st.write(result.replace("\n", "  \n"))
+            st.text("English translation")
+            st.write(translate(result, "en", "nl").replace("\n", "  \n"))
+ 
+st.sidebar.subheader("Citation")
+st.markdown(
+    """
+    
+```    
+@inproceedings{de-vries-nissim-2021-good,
+    title = "As Good as New. How to Successfully Recycle {E}nglish {GPT}-2 to Make Models for Other Languages",
+    author = "de Vries, Wietse  and
+      Nissim, Malvina",
+    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
+    month = aug,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.findings-acl.74",
+    doi = "10.18653/v1/2021.findings-acl.74",
+    pages = "836--846",
+}
+```
+    
+    
+    """
+)