Create app.py

app.py

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+from googletrans import Translator
+import spacy
+import gradio as gr
+
+spacy.cli.download("en_core_web_sm")
+
+nlp = spacy.load('en_core_web_sm')
+translator = Translator()
+
+def Sentencechunker(sentence):
+    Sentchunks = sentence.split(" ")
+    chunks = []
+    for i in range(len(Sentchunks)):
+        chunks.append(" ".join(Sentchunks[:i+1]))
+    return " | ".join(chunks)
+
+def ReverseSentenceChunker(sentence):
+    reversed_sentence = " ".join(reversed(sentence.split()))
+    chunks = Sentencechunker(reversed_sentence)
+    return chunks
+
+def three_words_chunk(sentence):
+    words = sentence.split()
+    chunks = [words[i:i+3] for i in range(len(words)-2)]
+    chunks = [" ".join(chunk) for chunk in chunks]
+    return " | ".join(chunks)
+
+def keep_nouns_verbs(sentence):
+    doc = nlp(sentence)
+    nouns_verbs = []
+    for token in doc:
+        if token.pos_ in ['NOUN','VERB','PUNCT']:
+            nouns_verbs.append(token.text)
+    return " ".join(nouns_verbs)
+
+def unique_word_count(text="", state=None):
+    if state is None:
+        state = {}
+    words = text.split()
+    word_counts = state
+    for word in words:
+        if word in word_counts:
+            word_counts[word] += 1
+        else:
+            word_counts[word] = 1
+    sorted_word_counts = sorted(word_counts.items(), key=lambda x: x[1], reverse=True)
+    return sorted_word_counts,    
+
+"""
+sentence = "Please help me create a sentence chunker"
+sentencechunks = Sentencechunker(sentence)
+reversed_chunks = ReverseSentenceChunker(sentence)
+TWchunks = three_words_chunk(sentence)
+nouns_verbs = keep_nouns_verbs(sentence)
+"""
+
+# Translate from English to French
+
+langdest = gr.Dropdown(choices=["af", "de", "es", "ko", "ja", "zh-cn"], label="Choose Language", value="de")
+
+"""
+def VarTrans(text, langdest):
+  translated = translator.translate(text, dest=langdest)
+  SCtranslated = translator.translate(sentencechunks, dest=langdest)
+  RCtranslated = translator.translate(reversed_chunks, dest=langdest)
+  TWCtranslated = translator.translate(TWchunks, dest=langdest)
+  return translated, SCtranslated, RCtranslated, TWCtranslated
+"""
+
+ChunkModeDrop = gr.Dropdown(choices=["Chunks", "Reverse", "Three Word Chunks"], label="Choose Chunk Type")
+
+def FrontRevSentChunk (Chunkmode, Translate, Text, langdest):
+  FinalOutput = ""
+  TransFinalOutput = ""
+  if Chunkmode=="Chunks": 
+    FinalOutput += Sentencechunker(Text)
+  if Chunkmode=="Reverse":
+    FinalOutput += ReverseSentenceChunker(Text)
+  if Chunkmode=="Three Word Chunks": 
+    FinalOutput += three_words_chunk(Text) 
+  
+  if Translate: 
+    TransFinalOutput = FinalOutput
+    translated = translator.translate(TransFinalOutput, dest=langdest)
+    FinalOutput += "\n" + translated.text
+  return FinalOutput
+
+"""
+print(translated.text)
+print(sentencechunks)
+print(SCtranslated.text)
+print(reversed_chunks)
+print(RCtranslated.text)
+print(TWchunks)
+print(TWCtranslated.text)
+print(nouns_verbs)
+"""
+
+def Wordchunker(word):
+    chunks = []
+    for i in range(len(word)):
+        chunks.append(word[:i+1])
+    return chunks
+
+word = "please"
+wordchunks = Wordchunker(word)
+print("\n") 
+print(wordchunks)
+
+#random_chunk_display(TWCtranslated.text)
+
+with gr.Blocks() as lliface:
+    gr.HTML("<p> Still Undercontruction </p> <> Arrows app json creator for easy knowledge graphing and spacy POS graph? </p> <p> https://huggingface.co/spaces/RASMUS/Whisper-youtube-crosslingual-subtitles, https://huggingface.co/spaces/vumichien/whisper-speaker-diarization,  Maybe duplicate these, private them and then load into spaces? --> Whisper space for youtube, Clip Interrogator, load here and all my random functions esp. text to HTML </p>")
+    gr.Interface(fn=FrontRevSentChunk, inputs=[ChunkModeDrop, "checkbox", "text", langdest], outputs="text")
+    gr.Interface(fn=keep_nouns_verbs, inputs=["text"], outputs="text", title="Noun and Verbs only (Plus punctuation")
+    gr.HTML("Add a codepen pen page here")
+    gr.Interface(fn=unique_word_count, inputs="text", outputs="text", title="Wordcounter")
+
+lliface.launch()