create app.py

app.py

@@ -0,0 +1,61 @@
+import guidance
+import gradio as gr
+from dotenv import load_dotenv
+
+load_dotenv() 
+def finalGradingPrompt(resume_summary, role, exp, ires):
+    # Initialize the guidance model
+    model = guidance.llms.OpenAI('gpt-3.5-turbo')
+    
+    # Define the final grading prompt using the guidance template
+    finalRes = guidance('''
+        {{#system~}}
+        You are now the Final Decision Interview Result Grading Expert. You are provided with an Interview's evaluation details.
+        You need to evaluate the interview scenario and provide an overall score and set of Scope of Improvement statements for the interviewee.
+        {{~/system}}
+        {{#user~}}
+        The interview has been completed and the results of the interview will be provided to you. You need to evaluate the case and 
+        provide an overall score of the interviewee's performance and suggestions for further improvements if required, based on the overall score.
+
+        Here's the Interviewee's Extracted JSON Summary:
+
+        {{resume_summary}}
+
+        {{~/user}}
+        {{#user~}}
+        The interviewee applied to the following role:
+        
+        {{role}}
+
+        and has the following experience in that role:
+
+        {{exp}}
+
+        Here are the list of CSV records made from questions answered with grades under appropriate rubrics. These records also
+        contain the start and end timestamps of the interviewee answering the questions within a 2-minute time constraint.
+        Finally, the records contain a float value of the plagiarism score. We have set the threshold of 0.96 for an answer to be considered plagiarized.
+        
+        The CSV records are as follows:
+
+        {{ires}}
+
+        {{~/user}}
+        {{#user~}}
+        Based on the above inputs of the interview, generate an overall performance score and scope of improvements based on it.
+        {{~/user}}
+        {{#assistant~}}
+        {{gen 'final_evaluation' temperature=0.5 max_tokens=1000}}
+        {{~/assistant}}
+    ''', llm=model)
+    
+    # Calling the final grading prompt with the provided inputs
+    res = finalRes(resume_summary=resume_summary, role=role, exp=exp, ires=ires)
+    
+    # Return the final evaluation from the response
+    return res['final_evaluation']
+def get_shape(csv_file,resume_summary,role,experience):
+    with open(csv_file.name, "rb") as f:
+      k=pd.read_csv(f)
+      l=finalGradingPrompt(resume_summary=resume_summary,role=role,exp=experience,ires=k)
+      return l
+gr.Interface(fn=get_shape, inputs=['file','json','text','text'], outputs='text').launch()