Update app.py

app.py

@@ -1,4 +1,3 @@
-import streamlit as st
 import random
 import spacy
 import requests
@@ -6,63 +5,22 @@ from bs4 import BeautifulSoup
 import re
 import spacy
 import language_tool_python
-import json
-from gradio_client import Client
-
-API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-mnli"
-headers = {"Authorization": "Bearer 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"}
-
-def query(payload):
-    response = requests.post(API_URL, headers=headers, json=payload)
-    return response.json()
-
-# Define the grammar_sense function
-def grammar_sense(sentence):
-    sense = query({
-        "inputs": sentence,
-        "parameters": {"candidate_labels": ["Make Sense", "Not Make Sense"]},
-    })
-    grammar = query({
-        "inputs": sentence,
-        "parameters": {"candidate_labels": ["Correct Grammar", "Incorrect Grammar"]},
-    })
-    objects = ["Sense", "Grammar"]
-    ans = []
-    for i in objects:
-        if i == "Sense":
-            response_data = json.loads(json.dumps(sense))
-            labels = response_data['labels']
-            scores = response_data['scores']
-            index_of_highest_score = scores.index(max(scores))
-            highest_score_label = labels[index_of_highest_score]
-            ans.append(highest_score_label)
-        else:
-            response_data = json.loads(json.dumps(grammar))
-            labels = response_data['labels']
-            scores = response_data['scores']
-            index_of_highest_score = scores.index(max(scores))
-            highest_score_label = labels[index_of_highest_score]
-            ans.append(highest_score_label)
-
-    if not 'Not' in ans[0] and ans[1] == 'Correct Grammar':
-        return True
-    else:
-        return False
-
+import streamlit as st
 # Initialize LanguageTool
-tool = language_tool_python.LanguageToolPublicAPI('en-US')
+tool = language_tool_python.LanguageTool('en-US')
 
-# Define the Streamlit app
-st.title("NLP Testing and Scoring App")
-
-# Ask for the topic at the start
-topic = st.text_input("Enter a topic:")
+# Helper function to check grammar and sense
+def grammar_sense(sentence):
+    sense = tool.correct(sentence)
+    grammar = "Correct Grammar" if not tool.check(sentence) else "Incorrect Grammar"
+    return "Make Sense" if "Not" not in sense and grammar == "Correct Grammar" else "Not Make Sense"
 
 # Web scraping and text cleaning
-entity = "Florida"  # You can replace this with the user's topic input
-if topic:
-    entity = topic  # Use the user's input as the entity
-
+Quiz_Gen = st.form("Quiz Generation")
+Quiz_Gen.write("What topic do you want to get tested on?")
+res = Quiz_Gen.text_box()
+Quiz_Gen.form_submit_button("Submit")
+entity = res
 prefix = "https://wiki.kidzsearch.com/wiki/"
 page = requests.get(f'{prefix}{entity}')
 res = BeautifulSoup(page.content, 'html.parser')
@@ -74,13 +32,14 @@ cleaned_text = re.sub(r'[^a-zA-Z0-9.,]', ' ', cleaned_text)
 paragraphs = [p.strip() for p in re.split(r'\n', cleaned_text) if p.strip()]
 
 # Process text using SpaCy
-nlp = spacy.load("en_core_web_sm")
+nlp = spacy.load("en_core_web_lg")
 doc = nlp(cleaned_text)
 
 sentences = [sent.text for sent in doc.sents]
 
 # Combine sentences into paragraphs
 paragraphs = [f"{sentences[i]} {sentences[i + 1]}" if i + 1 < len(sentences) else sentences[i] for i in range(0, len(sentences), 2)]
+
 class SubjectiveTest:
 
     def __init__(self, data, noOfQues):
@@ -90,11 +49,11 @@ class SubjectiveTest:
 
     def adjust_question_pattern(self, entity_label, topic_placeholder=True):
         question_patterns = {
-            "PERSON": ["Who is {entity}?", "Tell me about {entity}", "What do you know about {entity}"],
-            "ORG": ["What is {entity}?", "Tell me about {entity}", "What do you know about {entity}"],
-            "GPE": ["Tell me about {entity}", "What do you know about {entity}", "Where is {entity}"],
-            "MONEY": ["How much is {entity}?", "Tell me the value of {entity}"],
-            "DATE": ["Why was {entity} important?"],
+            "PERSON": ["Who is {entity}?", "Tell me about {entity}", "Explain {entity}", "What do you know about {entity}"],
+            "ORG": ["What is {entity}?", "Tell me about {entity}", "Explain {entity}", "What do you know about {entity}"],
+            "GPE": ["Tell me about {entity}", "Explain {entity}", "What do you know about {entity}", "Describe {entity}", "Where is {entity}"],
+            "MONEY": ["How much is {entity}?", "Tell me the value of {entity}", "Explain the amount of {entity}"],
+            "DATE": ["Why was {entity} important?", "Explain what happened on {entity}"],
             # Add more entity-label to question-pattern mappings as needed
         }
 
@@ -102,7 +61,7 @@ class SubjectiveTest:
             for key in question_patterns:
                 question_patterns[key] = [pattern + " {topic}" for pattern in question_patterns[key]]
 
-        return question_patterns.get(entity_label, "Explain")
+        return question_patterns.get(entity_label, ["Explain {entity} {topic}"])
 
     def generate_test(self, topic=None):
         doc = self.nlp(self.summary)
@@ -112,7 +71,7 @@ class SubjectiveTest:
             for ent in sentence.ents:
                 entity_label = ent.label_
                 entity_text = ent.text
-                question_patterns = self.adjust_question_pattern(entity_label, "")
+                question_patterns = self.adjust_question_pattern(entity_label, topic is not None)
                 for pattern in question_patterns:
                     question = pattern.format(entity=entity_text, topic=topic)
                     if entity_label in question_answer_dict:
@@ -124,81 +83,61 @@ class SubjectiveTest:
 
         for entity_label, entity_questions in question_answer_dict.items():
             entity_questions = entity_questions[:self.noOfQues]
-            if "Explain" in entity_questions:
-                continue
-            else:
-                questions.extend(entity_questions)
+            questions.extend(entity_questions)
 
         return questions
 
-with st.form("quiz_form"):
-    # Create a button to initiate quiz generation
-    generate_quiz = st.form_submit_button("Generate Quiz")
-
-if generate_quiz:
-    st.write("Generating the quiz...")
-    data = ' '.join(paragraphs)
-    noOfQues = 5
-
-    subjective_generator = SubjectiveTest(data, noOfQues)
-    questions = subjective_generator.generate_test(topic)  # Use the user's input topic here
-
-    # Filter out invalid and empty questions
-    x = 0
-    while x > len(questions):
-        for i in questions:
-            if len(i) == 1:
-                questions.pop(x)
-                x = x + 1
-            else:
-                x = x + 1
-    # Ensure you have valid questions
-    if not questions:
-        st.write("No valid questions to process.")
+# Example usage
+data = ' '.join(paragraphs)
+noOfQues = 5
+
+subjective_generator = SubjectiveTest(data, noOfQues)
+question_list = subjective_generator.generate_test("")
+questions = []
+Quiz = st.form("Quiz")
+for i, question in enumerate(question_list):
+  if "Explain" not in question and len(tool.check(question)) == 0 and grammar_sense(question) == "Make Sense":
+    questions.append(f"Question: {question}")
+scores = []
+client = Client("https://billbojangeles2000-zephyr-7b-alpha-chatbot-karki.hf.space/")
+for i in questions:
+    res = Quiz.text_input(i)
+    result = client.predict(
+        f'What would you rate this answer to the question :"{i}" as a percentage? Here is the answer: {res}. Make sure to write your answer as "Score" and then write your score of the response.',  # Fixed formatting issue
+        0.9,
+        256,
+        0.9,
+        1.2,
+        api_name="/chat"
+    )
+    print(result)
+    pattern = r'(\d+)%'
+
+    match = re.search(pattern, result)
+    if match:
+        score = match.group(1)
+        scores.append(f'{int(score)}')
     else:
-        answers = {}  # Dictionary to store answers
+        scores.append(f'N/A')
 
-        # Use the filtered questions in your code
-        for i, question in enumerate(questions):
-            res = st.text_input(f'Q{i + 1}: {question}')  # Get user input for each question
-            answers[f'Q{i + 1}'] = res  # Store the user's answer
+Quiz_Gen.form_submit_button("Submit")
 
-        scores = []
-        client = Client("https://billbojangeles2000-zephyr-7b-alpha-chatbot-karki.hf.space/")
+x = 0
+new_scores = []
+for i in scores:
+  if i == 'N/A':
+    scores.pop(x)
+    scores.append(85)
+    x = x+1
+  else:
+    x = x+1
 
-        question_list = subjective_generator.generate_test(topic)  # Define 'questions' here
-        questions = []
-        for i, question in enumerate(question_list):
-            if (question != "") and (len(tool.check(question)) == 0) and (grammar_sense(question)):
-                questions.append(f"Question: {question}")
-
-        for i, question in enumerate(questions):
-            res = answers[f'Q{i + 1}']
-            if res:
-                result = client.predict(
-                    f'What would you rate this answer to the question: "{question}" as a percentage? Here is the answer: {res}. Make sure to write your answer as "Score" and then write your score of the response.',
-                    0.9,
-                    256,
-                    0.9,
-                    1.2,
-                    api_name="/chat"
-                )
-                pattern = r'(\d+)%'
-                match = re.search(pattern, result)
-                if match:
-                    score = int(match.group(1))
-                    scores.append(score)
-                else:
-                    scores.append(85)
-
-        def calculate_average(numbers):
-            if not numbers:
-                return 0  # Return 0 for an empty list to avoid division by zero.
-
-            total = sum(numbers)
-            average = total / len(numbers)
-            return average
-
-        # Calculate and display the average score
-        average_score = calculate_average(scores)
-        st.write(f'Your average score is {average_score}')
+def calculate_average(numbers):
+    if not numbers:
+        return 0  # Return 0 for an empty list to avoid division by zero.
+
+    total = sum(numbers)
+    average = total / len(numbers)
+    return average
+
+st.write(f'Your average score is {calculate_average(scores)}')