Create app.py

app.py

@@ -0,0 +1,95 @@
+import PyPDF2
+import nltk
+from nltk.tokenize import sent_tokenize
+import random
+import requests
+import streamlit as st
+
+# Download NLTK data (if not already downloaded)
+nltk.download('punkt')
+nltk.download('averaged_perceptron_tagger')
+
+# ChatGPT API endpoint
+CHATGPT_API_ENDPOINT = "https://api.openai.com/v1/chat/completions"
+OPENAI_API_KEY = "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"  # Replace with your ChatGPT API key
+
+def extract_text_from_pdf(pdf_file):
+    pdf_reader = PyPDF2.PdfReader(pdf_file)
+    text = ""
+    for page_num in range(len(pdf_reader.pages)):
+        text += pdf_reader.pages[page_num].extract_text()
+    return text
+
+def generate_mcqs_on_topic(text, topic, num_mcqs=5):
+    # Tokenize the text into sentences
+    sentences = nltk.sent_tokenize(text)
+
+    # Randomly select sentences to create Questions
+    selected_sentences = random.sample(sentences, min(num_mcqs, len(sentences)))
+
+    mcqs = []
+    for sentence in selected_sentences:
+        # Use ChatGPT for interactive question generation
+        chatgpt_question = generate_question_with_chatgpt(sentence)
+        mcqs.append(chatgpt_question)
+
+        # Use LLM for nuanced language modeling
+        llm_question = generate_question_with_llm(sentence, topic)
+        mcqs.append(llm_question)
+
+    return mcqs
+
+def generate_question_with_chatgpt(context):
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {OPENAI_API_KEY}",
+    }
+
+    data = {
+        "model": "gpt-3.5-turbo",
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": f"What is the question for the following? {context}"},
+        ],
+    }
+
+    response = requests.post(CHATGPT_API_ENDPOINT, json=data, headers=headers)
+    result = response.json()
+
+    # Extract the generated question from the response
+    generated_question = result["choices"][0]["message"]["content"]
+    return generated_question
+
+
+    response = requests.post(LLM_API_ENDPOINT, json=data, headers=headers)
+    result = response.json()
+
+    # Extract the generated question from the response
+    generated_question = result["generated_question"]
+    return generated_question
+
+def main(): 
+    # Title of the Application
+    st.title("🤖CB Quiz Generator🧠")
+    st.subheader("☕CoffeeBeans☕")
+
+    # User input 
+    pdf_file = st.file_uploader("Upload PDF Document:", type=["pdf"])
+    num_mcqs = st.number_input("Enter Number of MCQs to Generate:", min_value=1, step=1, value=5)
+    topic = st.text_input("Enter the Topic in which the quiz has to be generated")
+
+    # Button to trigger QUIZ generation
+    if st.button("Generate MCQs"):
+        if pdf_file:
+            text = extract_text_from_pdf(pdf_file)
+            mcqs = generate_mcqs_on_topic(text, topic, num_mcqs)
+
+            # Display the generated Questions
+            st.success(f"Generated {num_mcqs} Questions:")
+            for i, question in enumerate(mcqs, start=1):
+                st.write(f"\nQuestion {i}: {question}")
+        else:
+            st.error("Please upload a PDF document.")
+
+if __name__ == "__main__":
+    main()