Update app.py

app.py

@@ -1,11 +1,112 @@
-import gradio as gr
-
-title = "papuGaPT2 Demo"
-description = "Demo for polish GPT2 for text generation. To use it, simply add your text, or click one of the examples to load them."
-examples = [
-    ['Dialogi niedobre… Bardzo niedobre dialogi są. W ogóle brak akcji jest. Nic się nie dzieje.'],
-    ["Na przykład w jednym sklepie na półkach po rocznym leżeniu cukier ma 80 proc. cukru w cukrze,"],
-    ["Moja jest tylko racja i to święta racja. Bo nawet jak jest twoja,"]
-]
-
-gr.Interface.load("huggingface/flax-community/papuGaPT2", inputs=gr.inputs.Textbox(lines=5, label="Input Text"),title=title,description=description, examples=examples).launch()
+import json
+import random
+import requests
+from mtranslate import translate
+import streamlit as st
+MODEL_URL = "https://api-inference.huggingface.co/models/flax-community/papuGaPT2"
+PROMPT_LIST = {
+    "Najsmaczniejszy owoc to...": ["Najsmaczniejszy owoc to "],
+    "Cześć, mam na imię...": ["Cześć, mam na imię "],
+    "Największym polskim poetą był...": ["Największym polskim poetą był "],
+}
+def query(payload, model_url):
+    data = json.dumps(payload)
+    print("model url:", model_url)
+    response = requests.request(
+        "POST", model_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="papuGaPT2 (Polish GPT-2) Demo")
+st.title("papuGaPT2 (Polish 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(
+    """
+    papuGaPT2 (Polish GPT-2) model trained from scratch on OSCAR dataset.
+    
+    The models were trained with Jax and Flax using TPUs as 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 = MODEL_name
+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"))