Update app.py

app.py

@@ -1,17 +1,24 @@
 import streamlit as st
-from transformers import pipeline
+import openai
 
-# Load the Hugging Face model for text generation
-pipe = pipeline("text-generation", model="google/gemma-2-27b-it")
+# Set the API key
+openai.api_key = "sk-proj-uHasdgbriPPlFMm99ZtJT3BlbkFJOl231YfdxCSNmQjVEpMX"
 
-# Function to generate MCQs
+# Function to generate MCQs using OpenAI
 def generate_mcqs(content, num_questions):
-    messages = [{"role": "user", "content": content}]
-    questions = pipe(messages, max_length=512, num_return_sequences=num_questions, num_beams=num_questions)
+    response = openai.Completion.create(
+        engine="davinci-codex",  # You can use a different engine if needed
+        prompt=f"Generate {num_questions} multiple-choice questions from the following content:\n\n{content}\n\n",
+        max_tokens=500,
+        n=num_questions,
+        stop=None,
+        temperature=0.7
+    )
+    questions = [choice["text"].strip() for choice in response.choices]
     return questions
 
 # Streamlit UI
-st.title("MCQ Generator using Hugging Face")
+st.title("MCQ Generator using OpenAI")
 
 content = st.text_area("Enter the content from which MCQs will be generated:", height=200)
 num_questions = st.number_input("Enter the number of MCQs to generate:", min_value=1, max_value=20, value=5, step=1)
@@ -20,7 +27,7 @@ if st.button("Generate MCQs"):
     if content:
         mcqs = generate_mcqs(content, num_questions)
         for i, mcq in enumerate(mcqs):
-            st.write(f"Q{i+1}: {mcq['generated_text']}")
+            st.write(f"Q{i+1}: {mcq}")
     else:
         st.warning("Please enter the content to generate MCQs.")
 
@@ -28,3 +35,4 @@ if st.button("Generate MCQs"):
 
 
 
+