Update app.py

app.py

@@ -1,26 +1,63 @@
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
-import gradio as gr
+from transformers import AutoModelForSequenceClassification
+from transformers import TFAutoModelForSequenceClassification
+from transformers import AutoTokenizer
+import numpy as np
+from scipy.special import softmax
+import csv
+import urllib.request
+
+# Preprocess text (username and link placeholders)
+def preprocess(text):
+    new_text = []
+ 
+ 
+    for t in text.split(" "):
+        t = '@user' if t.startswith('@') and len(t) > 1 else t
+        t = 'http' if t.startswith('http') else t
+        new_text.append(t)
+    return " ".join(new_text)
+
+# Tasks:
+# emoji, emotion, hate, irony, offensive, sentiment
+# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
+
+task='sentiment'
+MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
 
-# Cargar el tokenizer y el modelo desde el repositorio del modelo preentrenado
-MODEL = 'cardiffnlp/twitter-roberta-base-sentiment'
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
 
-# Crear el pipeline de resumen utilizando los objetos cargados
-resumidor = pipeline("summarization", model=MODEL, tokenizer=tokenizer)
-
-def generar_resumen(texto):
-    # Generar el resumen usando el pipeline
-    resumen = resumidor(texto, max_length=130, min_length=30, do_sample=False)
-    return resumen[0]["summary_text"]
-
-# Crear la interfaz de Gradio
-demo = gr.Interface(
-    fn=generar_resumen,
-    inputs=gr.Textbox(lines=10, placeholder="Pega aquí un texto largo en español..."),
-    outputs="text",
-    title="Generación de Resúmenes Automáticos",
-    description="Introduce un texto largo en español y obtén un resumen generado automáticamente por un modelo preentrenado."
-)
-
-# Lanzar la aplicación
-demo.launch()
+# download label mapping
+labels=[]
+mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt"
+with urllib.request.urlopen(mapping_link) as f:
+    html = f.read().decode('utf-8').split("\n")
+    csvreader = csv.reader(html, delimiter='\t')
+labels = [row[1] for row in csvreader if len(row) > 1]
+
+# PT
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+model.save_pretrained(MODEL)
+
+text = "Good night 😊"
+text = preprocess(text)
+encoded_input = tokenizer(text, return_tensors='pt')
+output = model(**encoded_input)
+scores = output[0][0].detach().numpy()
+scores = softmax(scores)
+
+# # TF
+# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
+# model.save_pretrained(MODEL)
+
+# text = "Good night 😊"
+# encoded_input = tokenizer(text, return_tensors='tf')
+# output = model(encoded_input)
+# scores = output[0][0].numpy()
+# scores = softmax(scores)
+
+ranking = np.argsort(scores)
+ranking = ranking[::-1]
+for i in range(scores.shape[0]):
+    l = labels[ranking[i]]
+    s = scores[ranking[i]]
+    print(f"{i+1}) {l} {np.round(float(s), 4)}")