Upload app5.py

app5.py

@@ -0,0 +1,202 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Dec 25 18:18:27 2023
+
+@author: alish
+"""
+
+import gradio as gr
+import fitz  # PyMuPDF
+import questiongenerator as qs
+import random
+
+from sentence_transformers import SentenceTransformer, util
+from questiongenerator import QuestionGenerator
+qg = QuestionGenerator()
+
+
+
+
+def highlight_similar_sentence(text1, text2, color='yellow'):
+    # Load the pre-trained sentence-transformers model
+    model = SentenceTransformer("paraphrase-MiniLM-L6-v2")
+
+    # Split text into sentences
+    sentences_text1 = [sentence.strip() for sentence in text1.split('.') if sentence.strip()]
+    sentences_text2 = [sentence.strip() for sentence in text2.split('.') if sentence.strip()]
+
+    # Compute embeddings for text1
+    #embeddings_text1 = model.encode(sentences_text1, convert_to_tensor=True)
+
+    highlighted_text2 = text2
+    max_similarity = 0.0
+
+    # Find the most similar sentence in text2 for each sentence in text1
+    for sentence_text1 in sentences_text1:
+        # Compute embeddings for the current sentence in text1
+        embedding_text1 = model.encode(sentence_text1, convert_to_tensor=True)
+
+        for sentence_text2 in sentences_text2:
+            # Compute cosine similarity between sentence in text1 and text2
+            embedding_text2 = model.encode(sentence_text2, convert_to_tensor=True)
+            similarity = util.pytorch_cos_sim(embedding_text1, embedding_text2).item()
+
+            # Highlight the most similar sentence in text2
+            if similarity > max_similarity:
+                max_similarity = similarity
+                highlighted_text2= highlight_text(text2, sentence_text2, color=color)
+                #highlighted_text2 = text2.replace(sentence_text2, f"<span style='background-color: {color};'>{sentence_text2}</span>")
+
+    return highlighted_text2
+
+
+def Extract_QA(qlist,selected_extracted_text):
+        Q_All=''
+        A_All=''
+        xs=['A','B','C','D']
+        h_colors=['yellow', 'red', 'DodgerBlue', 'Orange', 'Violet']
+        for i in range(len(qlist)):
+            question_i= qlist[i]['question']
+            Choices_ans= []
+            Choice_is_correct=[]
+            for j in range(4):
+               Choices_ans= Choices_ans+ [qlist[i]['answer'][j]['answer']]
+               Choice_is_correct= Choice_is_correct+ [qlist[i]['answer'][j]['correct']]
+               
+            Q=f"""
+                 Q_{i+1}: {question_i}
+                 A. {Choices_ans[0]}
+                 B. {Choices_ans[1]}
+                 C. {Choices_ans[2]}
+                 D. {Choices_ans[3]} 
+                                  
+                """
+            
+            result = [x for x, y in zip(xs, Choice_is_correct) if y ]
+            correct_answer= [x for x, y in zip(Choices_ans, Choice_is_correct) if y ]
+            A= f"""
+                <p>Answer_{i+1}: {result[0]} - {correct_answer[0]}<p>
+                
+                
+                """
+            color= h_colors[i]
+            
+            A_sen= f""" The correct answer is {correct_answer[0]}."""
+            
+            A= highlight_text(input_text=A, selcted_text=correct_answer[0], color=color)
+            selected_extracted_text= highlight_similar_sentence(A_sen, selected_extracted_text, color=color)
+            
+            
+            Q_All= Q_All+Q
+            A_All=A_All+ A
+            
+            
+        return (Q_All,A_All,selected_extracted_text)
+
+
+
+
+
+
+def extract_text_from_pdf(pdf_file_path):
+    # Read the PDF file
+    global extracted_text
+    text = []
+    with fitz.open(pdf_file_path) as doc:
+        for page in doc:
+            text.append(page.get_text())
+    extracted_text= '\n'.join(text)
+    extracted_text= get_sub_text(extracted_text)
+    
+    
+    #return ("The pdf is uploaded Successfully from:"+ str(pdf_file_path))
+    return ("File is uploaded Successfuly!")
+qg = qs.QuestionGenerator()
+
+def get_sub_text(TXT):
+   sub_texts= qg._split_into_segments(TXT)
+   if isinstance(sub_texts, list):
+       return sub_texts
+   else:
+       return [sub_texts]
+
+def highlight_text(input_text, selcted_text, color='yellow'):
+    # Replace 'highlight' with <span> tags for highlighting
+    highlighted_text = input_text.replace(selcted_text, f'<span style="background-color: {color}">{selcted_text}</span>')
+    return highlighted_text
+
+
+def pick_One_txt(sub_texts):
+    global selected_extracted_text
+    N= len(sub_texts)
+    if N==1:
+       selected_extracted_text= sub_texts[0] 
+       return(selected_extracted_text)
+    # Generate a random number between low and high
+    random_number = random.uniform(0, N)    
+    # Pick the integer part of the random number
+    random_number = int(random_number)
+    selected_extracted_text= sub_texts[random_number]
+        
+    return(selected_extracted_text)
+ 
+
+def pipeline(NoQs):
+    global Q,A
+    text= selected_extracted_text
+    qlist= qg.generate(text, num_questions=NoQs, answer_style="multiple_choice")
+    Q,A,highligthed_text= Extract_QA(qlist,text)
+    A= A + '\n'+highligthed_text
+    return (Q,A)
+
+def ReurnAnswer():
+    return A
+
+def GetQuestion(NoQs):
+    NoQs=int(NoQs)
+    pick_One_txt(extracted_text)
+    Q,A=pipeline(NoQs)
+    return Q
+
+with gr.Blocks() as demo:
+    global input_file
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            with gr.Row():
+                gr.Image("PupQuizAI.png")            
+                gr.Markdown(""" 🐶 **PupQuizAI** is an Artificial-Intelligence tool that streamlines the studying process. Simply input a text pdf that you need to study from. Then, PupQuiz will create 1-5 custom questions for you to study from each time you push 'Show Questions'. 
+                             """ )
+            with gr.Row():
+                input_file=gr.UploadButton(label='Select a file!', file_types=[".pdf"])                
+                #status = gr.Textbox(label="Status")
+                status = gr.HTML( )
+                input_file.upload(fn=extract_text_from_pdf, inputs=input_file,outputs=status)
+            
+            #upload_btn = gr.Button(value="Upload the pdf File.")
+            Gen_Question = gr.Button(value="Show Questions")
+            Gen_Answer = gr.Button(value="Show Answers")
+            No_Qs= gr.Slider(minimum=1, maximum=5,value=3, step=1, label='Max # of Questions')
+    
+            gr.Markdown(""" 🐶 
+                        **Instructions:**                     
+                        * Start by selecting a 'pdf' text file you want to upload by clicking the "Select file" button. (PupQuiz currently only supports files that can have highlightable text)
+                        * Select the number of questions you want generated from the "# of Questions" selector. 
+                        * Click "Show Questions" 
+                        * Then, if you want answers to the questions, select "Show Answers" """  )
+         
+            #gr.Image("PupQuizAI.png")
+            
+            
+            
+        with gr.Column(scale=2.0):
+            #file_stat= gr.Textbox(label="File Status")
+            question = gr.Textbox(label="Question(s)")
+            #Answer = gr.Textbox(label="Answer(s)")
+            Answer = gr.HTML(label="Answer(s)")
+
+    Gen_Question.click(GetQuestion, inputs=No_Qs, outputs=question, api_name="QuestioGenerator")
+    Gen_Answer.click(ReurnAnswer, inputs=None, outputs=Answer, api_name="QuestioGenerator")
+   
+
+demo.launch()