save

app.py

@@ -1,4 +1,27 @@
 import streamlit as st
+import openai
+import os
+from openai import OpenAI
+import re
+
+# Initialize the session state for the selection box
+if "selected_option" not in st.session_state:
+    st.session_state["selected_option"] = None  # Default value
+
+if "selected_task" not in st.session_state:
+    st.session_state["selected_task"] = None  # Default value
+
+api_key = os.getenv("NVIDIA_API_KEY")
+
+# Check if the API key is found
+if api_key is None:
+    st.error("NVIDIA_API_KEY environment variable not found.")
+else:
+    # Initialize the OpenAI client
+    client = OpenAI(
+        base_url="https://integrate.api.nvidia.com/v1",
+        api_key=api_key
+    )
 
 def generate_prompt(topic, difficulty, num_questions):
     """Generates an AI prompt based on user input."""
@@ -8,13 +31,72 @@ def generate_prompt(topic, difficulty, num_questions):
     )
     return prompt
 
-def get_ai_response(prompt):
-    """Dummy function to simulate getting a response from an AI model."""
-    response = f"Here are your generated questions based on the prompt: {prompt}"
-    return response
+def generate_ai_response(prompt, enablestreaming):
+    """Generates a response from an AI model
+
+    Args:
+    prompt: The prompt to send to the AI model.
 
-# Streamlit App
-st.title("Nemotron Quizmaster")
+    Returns:
+    response from the AI model.
+    """
+    try:
+        completion = client.chat.completions.create(
+            model="meta/llama-3.3-70b-instruct",
+            temperature=0.5,  # Adjust temperature for creativity
+            top_p=1,
+            max_tokens=1024,            
+                        messages=[
+                {
+                    "role": "system",  
+                    "content": "You are an AI assistant designed to generate educational \
+                        questions that foster higher-order thinking skills in line \
+                        with outcomes-based education. For each question, focus on \
+                        evaluating skills such as analysis, synthesis, application, \
+                        and evaluation rather than simple recall. Create multiple-choice \
+                        questions with four answer options, clearly indicating the \
+                        correct answer. Your output should strictly follow this \
+                        JSON format:\n\n{\n  \"question\": \"<Insert the question \
+                        text here>\",\n  \"options\": [\n    \"<Option A>\",\n \
+                        \"<Option B>\",\n    \"<Option C>\",\n    \"<Option D>\"\n  ],\n  \
+                        \"correct_answer\": \"<Insert the correct option text here>\"\n}\n\n \
+                        Ensure questions are designed to encourage critical thinking \
+                        and align with measurable learning outcomes. The topic can \
+                        range across various disciplines based on provided inputs \
+                        or your general knowledge. For instance, if the topic is \
+                        'AI and Machine Learning,' ensure the question engages \
+                        learners in practical or theoretical applications of the \
+                        subject. Ensure that every question is unique in a set of questions."                    
+                },
+                {
+                    "role": "user",
+                    "content": prompt
+                }
+            ],
+            stream = enablestreaming         
+        )
+
+        if enablestreaming:
+            # Extract and display the response
+            response_container = st.empty()
+            model_response=""
+            for chunk in completion:
+                if chunk.choices[0].delta.content is not None:
+                    model_response += chunk.choices[0].delta.content                
+                    response_container.write(model_response)
+                elif 'error' in chunk:
+                    st.error(f"Error occurred: {chunk['error']}")
+                    break        
+        else:
+            # this is used if output streaming is not enabled
+            model_response = completion.choices[0].message.content
+
+        return model_response
+    except Exception as e:
+        st.error(f"An error occurred: {e}")
+        return None
+
+st.title("Quiz Question Generator")
 
 with st.expander("About"):
     st.write(
@@ -32,18 +114,20 @@ difficulty = st.selectbox("Select difficulty level:", difficulty_levels)
 num_questions = st.selectbox("Select the number of questions:", [5, 10, 15])
 
 # Generate AI prompt
-if st.button("Generate AI Prompt"):
-    if topic.strip():
-        ai_prompt = generate_prompt(topic, difficulty, num_questions)
-        st.write(f"Generated Prompt: {ai_prompt}")
-    else:
-        st.warning("Please enter a topic before generating a prompt.")
+ai_prompt = ""
+
+if topic.strip():
+    ai_prompt = generate_prompt(topic, difficulty, num_questions)
+    st.write(f"Generated Prompt: {ai_prompt}")
+else:
+    st.warning("Please enter a topic before generating a prompt.")
 
 # Get AI response
 if st.button("Get Response"):
     if topic.strip():
-        ai_prompt = generate_prompt(topic, difficulty, num_questions)
-        ai_response = get_ai_response(ai_prompt)
-        st.write(ai_response)
+        print(ai_prompt)
+        with st.spinner("Thinking..."):
+            response = generate_ai_response(ai_prompt, enablestreaming=True)
+        st.success("Response generated successfully.")
     else:
         st.warning("Please generate a prompt first before getting a response.")

requirements.txt

@@ -1,2 +1,2 @@
 streamlit
-transformers
+openai