Create utils.py

utils.py

@@ -0,0 +1,137 @@
+import os
+import re
+import pandas as pd
+from PyPDF2 import PdfReader
+from huggingface_hub import login
+from langchain.docstore.document import Document
+from langchain_openai import AzureOpenAIEmbeddings
+from openai import AzureOpenAI
+
+client = AzureOpenAI()
+
+def get_pdf_text(file_path):
+
+    text = ''
+    pdf = PdfReader(file_path)
+    # Extract text from each page and pass it to the process_text function
+    for page_number in range(len(pdf.pages)):
+        # Extract text from the page
+        page = pdf.pages[page_number]
+        text += page.extract_text()
+    return text
+
+# that user uploaded PDF files, one by one
+def create_docs(user_pdf_list, unique_id):
+    docs=[]
+    for filename in user_pdf_list:
+
+        chunks=get_pdf_text(filename)
+
+        #Adding items to our list - Adding data & its metadata
+        docs.append(Document(
+            page_content=chunks,
+            metadata={"name": filename.name,"id":filename.file_id,"type=":filename.type,"size":filename.size,"unique_id":unique_id},
+        ))
+
+    return docs
+
+
+def create_embeddings_load_data():
+    embeddings = AzureOpenAIEmbeddings(
+                azure_deployment="AZUREEMBEDDING"
+            )
+    return embeddings
+
+
+def opeani_response(resume, job_description):
+    result = ""
+    # job_description = extract_text_from_file(job_description_path)
+    # resume = extract_text_from_file(resume_path)
+    chat_completion = client.chat.completions.create(
+        model = "GPT35",
+        messages = [
+            {"role": "system", "content": "You are a Detailed Resume Matcher For Given Job description."},
+            {"role": "user", "content": f"""
+            Given the job description and the resume, assess the given job description and the resume with detailed analysis. provide matching percentage.
+            **Job Description:**{job_description}
+            **Resume:**{resume}
+
+            **Detailed Analysis:**
+                  **the result should be in this format:**
+                  '''Matched Percentage: [matching percentage].
+                  Reason: [Reasons for why this resume matched and not matched.].
+                  Skills To Improve: [Mention the skills to improve for the candidate according to the given job description.].
+                  Irrelevant: [mention the irrelevant skills and experience].
+                  Keywords: [Return the matched keywords from resume and job description.]'''
+        """}
+        ],
+        max_tokens=500,
+        temperature=0
+    )
+    generated_text = chat_completion.choices[0].message.content
+    print(generated_text)
+    return generated_text
+
+def get_strip_response(matched_result):
+    lines = matched_result.split('\n')
+    matched_percentage = None
+    reason = []
+    skills_to_improve = []
+    keywords = []
+    irrelevant = []
+
+    section = None
+
+    for line in lines:
+        line = line.strip()
+        if line.startswith('Matched Percentage:'):
+            match = re.search(r"Matched Percentage: (\d+)%", line)
+            if match:
+                matched_percentage = int(match.group(1))
+        elif line.startswith('Reason:'):
+            section = 'reason'
+            reason.append(line.split(':', 1)[1].strip())
+        elif line.startswith('Skills To Improve:'):
+            section = 'skills_to_improve'
+            skills_to_improve.append(line.split(':', 1)[1].strip())
+        elif line.startswith('Keywords:'):
+            section = 'keywords'
+            keywords.append(line.split(':', 1)[1].strip())
+        elif line.startswith('Irrelevant:'):
+            section = 'irrelevant'
+            irrelevant.append(line.split(':', 1)[1].strip())
+        else:
+            if section == 'reason':
+                reason.append(line)
+            elif section == 'skills_to_improve':
+                skills_to_improve.append(line)
+            elif section == 'keywords':
+                keywords.append(line)
+            elif section == 'irrelevant':
+                irrelevant.append(line)
+
+    # Join the lists into strings
+    reason = ' '.join(reason).strip('- ')
+    skills_to_improve = ' '.join(skills_to_improve).strip('- ')
+    keywords = ' '.join(keywords).strip('- ')
+    irrelevant = ' '.join(irrelevant).strip('- ')
+
+    return matched_percentage, reason, skills_to_improve, keywords, irrelevant
+
+
+# Helps us get the summary of a document
+def get_summary(resume):
+
+    chat_completion = client.chat.completions.create(
+        model = "GPT35",
+        messages = [
+            {"role": "system", "content": "You are a Resume summarizer."},
+            {"role": "user", "content": f"""Summarize the given resume within 60 words. resume : {resume}
+        """}
+        ],
+        max_tokens=200,
+        temperature=0
+    )
+    summary = chat_completion.choices[0].message.content
+
+    return summary