Create gradio.py

gradio.py

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+import gradio as gr
+
+def TextProcessor(txt):
+  # ASCII code greater than 122 will be zh
+  if ord(str(txt)[0]) > 122:
+    # convert to zh_sim
+    sim_text = jio.tra2sim(txt, mode='char')
+    zh2en_trans = pipeline("translation_zh_to_en", model = model, tokenizer = tokenizer)
+    results = zh2en_trans(sim_text)[0]['translation_text']
+  # ASCII code less than 122 will be en
+  else:
+    # if length greater than 1, sentences; otherwise, words
+    if len(txt.split()) < 2:
+      blob = TextBlob(txt)
+      POS_List = blob.tags
+      results = POS_List[0][1]
+    else:
+      # if txt contains ..., do text generation; otherwise do summary, NER, noun and verb phrases
+      if "..." in str(txt):
+        txt = str(txt)
+        text = txt[0:-3]
+        txt_generation = generator(text, max_length = 50, num_return_sequences = 1)
+        results = txt_generation[0]["generated_text"]
+      else:
+        txt = str(txt)
+        txt_summarization = summarizer(txt)
+        result_01 = txt_summarization[0]
+
+        result_02 = ner(txt)
+
+        blob = TextBlob(txt)
+        POS_List = blob.tags
+
+        noun_phrases = [np for np in POS_List if "N" in np[1][0]]
+        result_03 = noun_phrases
+
+        verb_phrases = [vp for vp in POS_List if "V" in vp[1][0]]
+        result_04 = verb_phrases
+        results = ("Summary:", result_01['summary_text'], "NER:", result_02, "noun_phrases:", result_03, "verb_phrases:", result_04)
+  
+  return results
+
+final = gr.Interface(fn = TextProcessor, inputs = "text", outputs = "text")
+final.launch()