Update pages/Auto_Score_Generation.py

pages/Auto_Score_Generation.py

@@ -1,14 +1,46 @@
 import streamlit as st
- 
-st.set_page_config(
-    page_title="Auto_Score_Generation.py",
-    page_icon="👋",
-)
-
+import os
+import openai
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+import numpy as np
+def gpt4_score(m_answer, s_answer):
+    response = openai.ChatCompletion.create(
+      model="gpt-4",
+      messages=[
+        {
+          "role": "system",
+          "content": "You are UPSC answers evaluater. You will be given model answer and student answer. Evaluate it by comparing with the model answer. \n<<REMEMBER>>\nIt is 10 marks question. Student can recieve maximum 5 marks. Give marks in the range of 0.25. (ex. 0,0.25,0.5...)\nThere are 3 parts in the answer. Introduction (1 marks), body (3 marks) and conclusion (1 marks). If the student answer and model answer is not relevant then give 0 marks.\ngive output in json form. Give output in this format {\"intro\":,\"body\":,\"con\":,\"total\":}\n<<OUTPUT>>\n"
+        },
+        {
+          "role": "assistant",
+          "content": f"Model answer: {m_answer}"},
+        {
+          "role": "user",
+          "content": f"Student answer: {s_answer}"}
+      ],
+      temperature=0,
+      max_tokens=701,
+      top_p=1,
+      frequency_penalty=0,
+      presence_penalty=0
+    )
+    return json.loads(response.choices[0].message.content)
 st.write("# Auto Score Generation! 👋")
 
 
-st.markdown(
-    """
-    **Work in progress!!!** """
-)
+if 'score' not in session_state:
+    session_state['score']= ""
+    
+st.title("Core Risk Category Classifier")
+text1= st.text_area(label= "Please write the Model Answer bellow", 
+              placeholder="What does the text say?")
+text2= st.text_area(label= "Please write the Student Answer bellow", 
+              placeholder="What does the text say?")
+def classify(text1,text2):
+    session_state['topic_class'] = gpt4_score(text1,text2)
+
+
+st.text_area("result", value=session_state['score'])
+
+st.button("Classify", on_click=classify, args=[text1,text2])