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README.md

-**Main objective**
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-Build the best modern solution for practice English Public Speaking in a proper way.
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-**Main Research questions**
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-What is the best modern solution for practice English Public Speaking in a proper way?
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-** Individual research questions**
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-* How to analyze content of the speech and give proper feedback for the speaker?
-* How to check grammar and richness of grammar use and give proper feedback for the speaker?
-* How to check the user’s facial expressions according to the speech content and give proper feedback for the speaker?
-* How to check the user’s eye contact continuity throughout the speech and give proper feedback for the speaker?
-* How to  check identify the user’s pause fillers, filler words, interjections, exclamations, etc
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-**Individual Objectives**
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-* Analyze content of the speech and give proper feedback for the speaker.
-* Check grammar and richness of grammar use and give proper feedback for the speaker
-* Check the user’s facial expressions according to the speech content and give proper feedback for the speaker.
-* Check the user’s eye contact continuity throughout the speech and give proper feedback for the speaker
-* Check identify the user’s pause fillers, filler words, interjections, exclamations, etc.
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grammar_ef_7_2 (1).py

-import spacy
-from gingerit.gingerit import GingerIt
-nlp = spacy.load("en_core_web_sm")
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-text1 = ("We needed to play Cricket, but we was unable to buy ball, then we came back to hostel and start study")
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-parser = GingerIt()
-result = parser.parse(text1)
-print(result)
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-scoreForGrammerAccracy = 70/100
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-text = ("I'll be there in time, but I also have to do my homhomework too,although I should finish them,so I may be late. And one more thing, can I use your car ?. Anyway after today I also need to work hard because my exam is in the next month.")
-doc = nlp(text)
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-Mcount = 0
-for token in doc:
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-    (token.text, token.dep_, token.head.text, token.head.pos_,
-            [child for child in token.children])
-    if token.dep_ == 'mark':
-        Mcount = Mcount + 1
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-#print(f"\n tagged as mark {Mcount}\n\n\n\n")
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-for sent in doc.sents:
-        word_count = 0
-        (sent.text)
-        print(f"\n >>>> SENTECNSENTENCE : {(sent.text)}.\n")
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-        for words in sent:
-            # print(words.text)
-            word_count = word_count + 1
-        # print(word_count)
-        
-        if word_count < 20: 
-         print(f"\n**** GOOD SENTECNSENTENCE **** This sentence contains efficient number of words\n")
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-        if word_count >= 20:
-            print(f"\n**** LENGTHY SENTECNSENTENCE **** This sentence contains {word_count} WORDS.\n")
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-        nlp = spacy.load("en_core_web_sm")
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-        if word_count >= 20:
-         transitionWordCount = 0
-         for token in doc:
-            if token.text == "also" or token.text == "although" or token.text == "so" or token.text == "however" or token.text == "then" or token.text == "because":
-                transitionWordCount = transitionWordCount + 1
-              
-                a = token.text
-                print(f"\n** {a.upper()}")
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-         if word_count >= 20 and Mcount >= 1 :
-            print(f"\n**** This sentence contained above {transitionWordCount} TRANSITION words in it, and {Mcount} CONJUNCTION words \n")
-            print("## Sentence is too long! You shoud not use more than 20 words in a single sentence!\n")
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-         if word_count >= 20:
-            print(f"\n**** This sentence contained above {transitionWordCount} transition words in it.\n")
-            print("## Sentence is too long! You shoud not use more than 20 words in a single sentence!\n")   
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-        if transitionWordCount == 1 and word_count > 20:
-            print("## Try to split senetce to two sesentences from transition word!\n")
-            
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-        if transitionWordCount >= 2 and word_count > 20:
-           print("## To make your English perfect make sure you don't use two transition words in a single sentence. Try to split senetce to two sesentences from transition words or conjunction!\n\n")
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-scoreForLengthySentences = 80/100
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