create app.py

app.py

@@ -0,0 +1,55 @@
+import gradio as gr 
+import guidance
+from dotenv import load_dotenv
+
+load_dotenv()
+def indiQuesGrade(question, answer, role, exp):
+    evaluatorModel = guidance.llms.OpenAI('gpt-3.5-turbo')
+    evaluationSys = guidance('''
+    {{#system~}}
+    You are an expert system in Evaluating the answer provided by an interviewee in an interview.
+    Based on the question, answer given with information of Applying Role and Years of Experience, you can grade the answer on appropriate grading measures.
+    You are very skilled in grading the answers accurately and justifiably.
+    {{~/system}}
+    {{#user~}}
+    Now, you are provided with Interviewee's Question, his job role he applied to, and his years of experience he has with it.
+    You are now asked to generate suitable/appropriate grading measures for the question and grade his answer according to them.
+    The Question asked as follows:
+
+    {{question}}
+
+    The Role he applied to is as follows :
+
+    {{role}}
+
+    The years of experience he has in it is as follows :
+
+    {{experience}}
+
+    Now, generate the grading measures according to the above question, role and experience values.
+    Do not output the measures yet.
+    {{~/user}}
+    {{#assistant~}}
+    {{gen 'grading_measures' temperature=0.7 max_tokens=150}}
+    {{~/assistant}}
+    {{#user~}}
+    Here's the answer provided by the interviewee in the interview :
+
+    {{answer}}
+
+    Now, perform the evaluation on the answer according to the generated grading measures.
+    Output the evaluation in a JSON Format with the grading measure as key and a dictionary of score and reason as value.
+    The score key contains a numerical measure depicting the answer against grading measure and the reason key contains text information
+    about why the answer was such given such numerical grade in the evaluation measure.
+    {{~/user}}
+    {{#assistant~}}
+    {{gen 'evaluation' temperature=0.5 max_token=1500}}
+    {{~/assistant}}
+    ''', llm = evaluatorModel)
+    return evaluationSys
+k=gr.Interface(
+    fn=gen_text,
+    inputs=['text','text','text','text'],
+    outputs=['json']
+)
+k.launch()