Update app.py

app.py

@@ -1,9 +1,9 @@
 import gradio as gr
 import os
-import openai
+from openai import OpenAI
 
 # Set your OpenAI API key
-openai.api_key = os.environ['OPENAI_API_KEY']
+client = OpenAI(api_key=os.environ['OPENAI_API_KEY'])
 
 jd_summary_global = ""  # Global variable to store the job description summary
 
@@ -18,8 +18,8 @@ def process_jd(text):
         prompt = f"Summarize the following job description into its job nature, responsibilities, and requirements:\n\n{text}"
         
         # Uploading text to OpenAI 
-        response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}])
-        jd_summary = response['choices'][0]['message']['content'].strip()
+        response = client.chat.completions.create(model="gpt-3.5-turbo", messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}])
+        jd_summary = response.choices[0].message.content.strip()
         jd_summary_global = jd_summary  # Update the global variable
         return jd_summary
     except Exception as e:
@@ -42,8 +42,8 @@ def cv_rating(cv_data):
         Rate the compatibility of the CV with the job description and provide strengths, weaknesses, and recommendations to strengthen the CV.
         """
         # Uploading text to OpenAI 
-        response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}])
-        cv_rating_global= response['choices'][0]['message']['content'].strip()
+        response = client.chat.completions.create(model="gpt-3.5-turbo", messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}])
+        cv_rating_global= response.choices[0].message.content.strip()
         return cv_rating_global
     except Exception as e:
         return str(e)
@@ -65,8 +65,8 @@ def create_cover_letter(additional_info):
         Create a tailored cover letter based on the job description and CV data provided:
         """
         # Uploading text to OpenAI 
-        response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}])
-        return response['choices'][0]['message']['content'].strip()
+        response = client.chat.completions.create(model="gpt-3.5-turbo", messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}])
+        return response.choices[0].message.content.strip()
     except Exception as e:
         return str(e)
 
@@ -83,8 +83,8 @@ def interview_qa(additional_info):
         Generate at least 10 interview questions and provide potential answers based on the CV data and additional information provided:
         """
         # Uploading text to OpenAI 
-        response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}])
-        return response['choices'][0]['message']['content'].strip()
+        response = client.chat.completions.create(model="gpt-3.5-turbo", messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}])
+        return response.choices[0].message.content.strip()
     except Exception as e:
         return str(e)
         
@@ -107,11 +107,10 @@ def suggest_cv_content(additional_info):
         Additional Information: {additional_info}
         """
         # Uploading text to OpenAI 
-        response = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo", 
+        response = client.chat.completions.create(model="gpt-3.5-turbo",
             messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}]
         )
-        return response['choices'][0]['message']['content'].strip()
+        return response.choices[0].message.content.strip()
     except Exception as e:
         return str(e)