grammer analyzing analyzing changers done

Grammer/README.md

+**Main objective**
+
+Build the best modern solution for practice English Public Speaking in a proper way.
+
+
+
+**Main Research questions**
+
+What is the best modern solution for practice English Public Speaking in a proper way?
+
+
+
+** Individual research questions**
+
+* 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
+
+
+
+**Individual Objectives**
+
+* 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.
+

Grammer/grammar.py

+import spacy
+from gingerit.gingerit import GingerIt
+nlp = spacy.load("en_core_web_sm")
+
+scoreForLengthySentences = "Good-60%"
+
+def gingerItParse(speech):
+    text1 = (speech)
+    parser = GingerIt()
+    result = parser.parse(text1)
+    print(result)
+    return {
+        "message": result,
+        "score": scoreForLengthySentences
+    }
+
+scoreForGrammerAccracy = "Good-60%"
+
+
+def processGrammar(speech):
+    retVal = []
+    text = speech
+    doc = nlp(text)
+
+    Mcount = 0
+    for token in doc:
+
+        (token.text, token.dep_, token.head.text, token.head.pos_,
+         [child for child in token.children])
+        if token.dep_ == 'mark':
+            Mcount = Mcount + 1
+
+    # print(f"\n tagged as mark {Mcount}\n\n\n\n")
+
+    for sent in doc.sents:
+        word_count = 0
+        (sent.text)
+        print(f"\n SENTECNSENTENCE : {(sent.text)}.\n")
+
+        for words in sent:
+            # print(words.text)
+            word_count = word_count + 1
+        # print(word_count)
+
+        if word_count < 20:
+            print(f"\nGOOD SENTECNSENTENCE : This sentence contains efficient number of words\n")
+            retVal.append(f"\nGOOD SENTECNSENTENCE : This sentence contains efficient number of words\n")
+        if word_count >= 20:
+            print(f"\n LENGTHY SENTECNSENTENCE: This sentence contains {word_count} WORDS.\n")
+            retVal.append(f"\nLENGTHY SENTECNSENTENCE : This sentence contains {word_count} WORDS.\n")
+
+        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()}*")
+                    retVal.append(f"\n {a.upper()}*")
+
+            if word_count >= 20 and Mcount >= 1:
+                print(
+                    f"\nThis sentence contained mentioned {transitionWordCount} TRANSITION words in it, and {Mcount} CONJUNCTION words \n")
+                retVal.append(f"\n This sentence contained mentioned {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")
+                retVal.append("\n Sentence is too long! You shoud not use more than 20 words in a single sentence!\n")
+
+            if word_count >= 20:
+                print("\n This sentence contained mentioned {transitionWordCount} transition words in it.\n")
+                retVal.append(f"\n This sentence contained mentioned {transitionWordCount} transition words in it.\n")
+                print(" Sentence is too long! You should not use more than 20 words in a single sentence!\n")
+                retVal.append("Sentence is too long! You shoud not use more than 20 words in a single sentence!\n")
+
+        if transitionWordCount == 1 and word_count > 20:
+            print("Try to split senetce to two sesentences from transition word!\n")
+            retVal.append("Try to split senetce to two sesentences from transition word!\n")
+
+        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")
+            retVal.append(" 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")
+        return {
+            "message": retVal,
+            "score": scoreForLengthySentences
+        }
+
+print(processGrammar("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."))
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