scaffolding for student input

app.py

@@ -17,6 +17,7 @@ from langchain.schema import (
 #App UI starts here
 st.set_page_config(page_title="FRQ Generator", page_icon=":robot:")
 st.header("FRQs using GPT-4")
+st.subheader("IMPORTANT: Please wait 60-70 seconds between responses. Chat GPT takes time to think.")
 header_text=str('''
                               Paste a JSON file here, with the following information:
 1. The name of the common core standard, 
@@ -42,7 +43,7 @@ if "sessionMessages" not in st.session_state:
 
 
 #Function to return the response
-def load_answer(question):
+def generateQuestion(question):
     ccStandard = question['cc-standard']
     #st.session_state.sessionMessages.append(HumanMessage(content="Common Core standard is: {question[0]}, and the area of interest is: {question[1]}"))
     
@@ -77,9 +78,7 @@ def load_answer(question):
             passage_and_research = chat(st.session_state.sessionMessages)
             st.session_state.sessionMessages.append(AIMessage(content=passage_and_research.content))
 
-            # Print passage
-            st.write(passage_and_research.content)
-
+        
             # Based on the above generated passage and research topic, we will now generate 3 FRQs and their grading rubrics
             frqPrompt='Generate 3 questions based on the previously discussed common core standard, and the above generated passage. The answers should be available either in the above passage, or the student should be able to find them with easy web research.  Good questions have: introduction, context, and open-ended question. Remember, the student is in grade: '+chatResponseDict['grade-level']
             frqPrompt+='Generate a rubric for evaluating the student responses for the above questions. Return the result in JSON format. E.g. { "rubric":[{"question":"..text of the question", "rubric":".. text of the rubric"}] etc.'
@@ -89,8 +88,9 @@ def load_answer(question):
             question_and_rubric = chat(st.session_state.sessionMessages)
             st.session_state.sessionMessages.append(AIMessage(content=question_and_rubric.content))
 
-            # Print rubric & questions
-            st.write(question_and_rubric.content)
+            
+            questionPassageJSON=json.loads(question_and_rubric.content)
+            
 
             ## Do the QC Step
             qcPrompt='For the JSON generated in the previous step, please evaluate for each question and rubric:\
@@ -130,13 +130,12 @@ def load_answer(question):
 
 
 
-    return ""
-# json.loads(assistant_answer.content)
+    return questionPassageJSON
 
 
 #Gets the user input
 def get_text():
-    input_text = st.text_area(label="Paste here")
+    input_text = st.text_area(label="Paste/Write here")
     return input_text
 
 ## Temperature kept at 0.9, so that generated passages and questions are not too similar to each other.
@@ -157,9 +156,28 @@ if submit:
     responseDict=[]
     for row in questionArray:
         st.write(row)
-        response = load_answer(row)
-        st.subheader("Answer:")
-        st.write(response)    
+        st.subheader("AI Generated Question & Rubric (Note - in a real world scenario, the rubric won't be shown to the student):")
+        #response = generateQuestion(row)
+        #st.write(response)    
+        st.subheader("Enter Student's Answer:")
+        st.write("Please use question number headers to enter answers:")
+        eg_text=str('''
+         
+        Question 1:
+        The name of the boy was David, and his main interest was in the game of Cricket.
+        
+        Question 2
+        A successful game of cricket involves a strong bowler and batsman.
+
+        The answers can span multiple lines. Leave empty lines between answers.
+
+        ''')
+        st.markdown(eg_text)
+        studentInput = get_text()
+        answerButton = st.button('Submit Answer')
+        if answerButton:
+            st.write (studentInput)
+