Upload the model

README.md

@@ -9,4 +9,5 @@ app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+This is the space that implements the [Political Speech model](https://huggingface.co/MarkelFe/PoliticalSpeech2) made for a NLP class in university.
+It uses data from the Basque Parliament, it only works in Spanish, although it may include some Basque Words.

app.py

@@ -0,0 +1,83 @@
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+import torch.nn
+import gradio as gr
+import re
+
+# CONF
+MAX_LENGTH = 1024
+device     = 'cuda' if torch.cuda.is_available() else 'cpu'; print("Using:", device)
+
+tokenizer = AutoTokenizer.from_pretrained("MarkelFe/PoliticalSpeech2", padding_side='left')
+model = AutoModelForCausalLM.from_pretrained("MarkelFe/PoliticalSpeech2").to(device)
+
+def return_conf(max_tokens, conf, ngram, beams, top_k, top_p):
+    if conf == "Ezer":
+      options = {"max_new_tokens": max_tokens, "do_sample": False}
+    elif conf == "Beam Search":
+      options = {"no_repeat_ngram_size": ngram, "num_beams": beams, "max_new_tokens": max_tokens, "do_sample": False}
+    elif conf == "Top K":
+      options = {"top_k": top_k, "max_new_tokens": max_tokens, "do_sample": False}
+    elif conf == "Top P":
+      options = {"top_p": top_p, "max_new_tokens": max_tokens, "do_sample": False}
+    return options
+
+def sortu_testua(alderdia, testua, max_tokens, conf, ngram, beams, top_k, top_p):
+    options = return_conf(max_tokens, conf, ngram, beams, top_k, top_p)
+    prompt = f"[{alderdia}] {testua}"
+    tokens = tokenizer(prompt, return_tensors="pt").to(device)
+    generation = model.generate(inputs=tokens['input_ids'], attention_mask = tokens['attention_mask'], **options)[0]
+    text = tokenizer.decode(generation)
+    print(re.split("\[(.*?)\] ", text))
+    return re.split("\[(.*?)\] ", text)[-1]
+
+def sortu_testu_guztiak(testua, max_tokens, conf, ngram, beams, top_k, top_p):
+    options = return_conf(max_tokens, conf, ngram, beams, top_k, top_p)
+    prompts = [f"[\"EAJ\"] {testua}", f"[\"EH Bildu\"] {testua}", f"[\"PP\"] {testua}", f"[\"PSE-EE\"] {testua}", f"[\"EP\"] {testua}", f"[\"UPyD\"] {testua}"]
+    tokens = tokenizer(prompts, padding = True, return_tensors="pt").to(device)
+    generation = model.generate(inputs=tokens['input_ids'], attention_mask = tokens['attention_mask'], **options)
+    texts = tokenizer.batch_decode(generation)
+    texts = list(map(lambda text: re.split("\[(.*?)\] ", text)[-1], texts))
+    return (texts[0], texts[1], texts[2], texts[3], texts[4], texts[5])
+
+with gr.Blocks() as demo:
+    with gr.Tab("Alderdi guztiak"):
+      with gr.Row():
+        with gr.Column(scale=4, min_width=400):
+          testua2 = gr.Textbox(label="Testua")
+          greet_btn2 = gr.Button("Sortu testuak")
+          gr.Markdown("""Aldatu konfigurazioa""")
+          new_token2 = gr.Slider(minimum=1, maximum=MAX_LENGTH, value=30, label="Luzera", info="Zenbat token berri sortuko diren.")
+          confi2 = gr.Radio(["Ezer", "Beam Search", "Top K", "Top P"], value="Beam Search", label="Estrategia", info="Aukeratu ze estrategia erabiliko den erantzunak hobetzeko")
+          ngram2 = gr.Slider(minimum=1, maximum=50, value=4, step=1, label="ngram kopurua", info="Bakarrik kontuan hartuko da \"Beam Search\" aukeratuta badago")
+          beams2 = gr.Slider(minimum=1, maximum=50, value=5, step=1, label="Beam kopurua", info="Bakarrik kontuan hartuko da \"Beam Search\" aukeratuta badago")
+          top_k2 = gr.Slider(minimum=1, maximum=50, value=5, step=1, label="K-balioa", info="Bakarrik kontuan hartuko da \"Top K\" aukeratuta badago")
+          top_p2 = gr.Slider(minimum=0, maximum=1, value=0.9, step=0.01, label="P-balioa", info="Bakarrik kontuan hartuko da \"Top P\" aukeratuta badago")
+        with gr.Column(scale=3, min_width=200):
+          outputEAJ = gr.Textbox(label="EAJ")
+          outputBildu = gr.Textbox(label="EH Bildu")
+          outputPP = gr.Textbox(label="PP")
+          outputPSE = gr.Textbox(label="PSE-EE")
+          outputEP = gr.Textbox(label="EP")
+          outputUPyD = gr.Textbox(label="UPyD")
+    with gr.Tab("Alderdi bakarra"):
+      with gr.Row():
+        with gr.Column(scale=4, min_width=400):
+          alderdia = gr.Dropdown(["EAJ", "EH Bildu", "PP", "PSE-EE", "EP", "UPyD"], label="Alderdi politikoa",)
+          testua = gr.Textbox(label="Testua")
+          greet_btn = gr.Button("Sortu testua")
+          gr.Markdown("""Aldatu konfigurazioa""")
+          new_token = gr.Slider(minimum=1, maximum=MAX_LENGTH, value=30, label="Luzera", info="Zenbat token berri sortuko diren.")
+          confi = gr.Radio(["Ezer", "Beam Search", "Top K", "Top P"], value="Beam Search", label="Estrategia", info="Aukeratu ze estrategia erabiliko den erantzunak hobetzeko")
+          ngram = gr.Slider(minimum=1, maximum=50, value=4, step=1, label="ngram kopurua", info="Bakarrik kontuan hartuko da \"Beam Search\" aukeratuta badago")
+          beams = gr.Slider(minimum=1, maximum=50, value=5, step=1, label="Beam kopurua", info="Bakarrik kontuan hartuko da \"Beam Search\" aukeratuta badago")
+          top_k = gr.Slider(minimum=1, maximum=50, value=5, step=1, label="K-balioa", info="Bakarrik kontuan hartuko da \"Top K\" aukeratuta badago")
+          top_p = gr.Slider(minimum=0, maximum=1, value=0.9, step=0.01, label="P-balioa", info="Bakarrik kontuan hartuko da \"Top P\" aukeratuta badago")
+        with gr.Column(scale=3, min_width=200):
+          output = gr.Textbox(label="Output")
+
+    greet_btn.click(fn=sortu_testua, inputs=[alderdia, testua, new_token, confi, ngram, beams, top_k, top_p], outputs=output, api_name="sortu_testua")
+    greet_btn2.click(fn=sortu_testu_guztiak, inputs=[testua2, new_token2, confi2, ngram2, beams2, top_k2, top_p2], outputs=[outputEAJ, outputBildu, outputPP, outputPSE, outputEP, outputUPyD], api_name="sortu_testu_guztiak")
+
+
+demo.launch()

requirements.txt

@@ -0,0 +1,2 @@
+transformers
+torch