Update app.py

app.py

@@ -1,146 +1,13 @@
 import json
-import random
+
 import requests
-from mtranslate import translate
-import streamlit as st
-LOGO = "https://raw.githubusercontent.com/nlp-en-es/assets/main/logo.png"
-MODELS = {
-    "Model trained on OSCAR": {
-        "url": "https://api-inference.huggingface.co/models/flax-community/gpt-2-spanish"
-    },
-    "Model trained on the Large Spanish Corpus": {
-        "url": "https://api-inference.huggingface.co/models/mrm8488/spanish-gpt2"
-    },
-}
-PROMPT_LIST = {
-    "Érase una vez...": ["Érase una vez "],
-    "¡Hola!": ["¡Hola! Me llamo "],
-    "¿Ser o no ser?": ["En mi opinión, 'ser' es "],
-}
-def query(payload, model_name):
+
+API_URL = "https://api-inference.huggingface.co/models/gpt2"
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+
+def query(payload):
     data = json.dumps(payload)
-    print("model url:", MODELS[model_name]["url"])
-    response = requests.request(
-        "POST", MODELS[model_name]["url"], headers={}, data=data
-    )
+    response = requests.request("POST", API_URL, headers=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="Spanish GPT-2 Demo", page_icon=LOGO)
-st.title("Spanish GPT-2")
-# Sidebar
-st.sidebar.image(LOGO)
-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(
-    """
-    Spanish GPT-2 models trained from scratch on two different datasets. One
-    model is trained on the Spanish portion of
-    [OSCAR](https://huggingface.co/datasets/viewer/?dataset=oscar)
-    and the other on the
-    [large_spanish_corpus](https://huggingface.co/datasets/viewer/?dataset=large_spanish_corpus)
-    aka BETO's corpus.
-    
-    The models are trained with Flax and using TPUs sponsored by Google since this is part of the
-    [Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104)
-    organised by HuggingFace.
-    """
-)
-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", "es").replace("\n", "  \n"))
-st.markdown(
-    """
-    ### Team members
-    - Manuel Romero ([mrm8488](https://huggingface.co/mrm8488))
-    - María Grandury ([mariagrandury](https://huggingface.co/mariagrandury))
-    - Pablo González de Prado ([Pablogps](https://huggingface.co/Pablogps))
-    - Daniel Vera ([daveni](https://huggingface.co/daveni))
-    - Sri Lakshmi ([srisweet](https://huggingface.co/srisweet))
-    - José Posada ([jdposa](https://huggingface.co/jdposa))
-    - Santiago Hincapie ([shpotes](https://huggingface.co/shpotes))
-    - Jorge ([jorgealro](https://huggingface.co/jorgealro))
-    
-    ### More information
-    You can find more information about these models in their cards:
-    - [Model trained on OSCAR](https://huggingface.co/models/flax-community/gpt-2-spanish)
-    - [Model trained on the Large Spanish Corpus](https://huggingface.co/mrm8488/spanish-gpt2)
-    """
-)
+
+data = query("Can you please let us know more details about your ")