bestFitNEW.py

from groq import Groq
import json
import os
from pydantic import BaseModel, Field
from typing import List, Optional
import textwrap
import pandas as pd

data=pd.read_csv("uploaded_file.csv")



GROQ_API_KEY = os.getenv("GROQ_API_KEY")

client = Groq(
    api_key=GROQ_API_KEY, # Use the GROQ_API_KEY variable directly
)


def load_job_description(file_path):
    try:
        with open(file_path, "r", encoding="utf-8") as file:
            return file.read().strip()
    except FileNotFoundError:
        raise FileNotFoundError(f"File '{file_path}' not found.")
    except Exception as e:
        raise RuntimeError(f"Error reading file: {e}")

job_description = load_job_description("Job.txt")  # Adjust the path if necessary

# Extraction from CSV for specific person in Dataframe, not really best Fit checker

class ResumeExtractionScheme(BaseModel):
    job_title: str = Field(description="Current or most recent job title of the candidate")
    summary: str = Field(description="Brief summary or professional statement of the candidate") 
    skills: List[str] = Field(description="Key skills or expertise mentioned by the candidate") 
    experience: List[str] = Field(description="List of job roles and responsibilities the candidate has held with relevance to the position")
    education: List[str] = Field(description="Academic qualifications and degrees obtained by the candidate")
    certifications: Optional[List[str]] = Field(description="Certifications or training programs completed by the candidate")
    languages: Optional[List[str]] = Field(description="Languages spoken by the candidate")
    projects: Optional[List[str]] = Field(description="Projects or work experience relevant to the job applied for")
    achievements: Optional[List[str]] = Field(description="Notable accomplishments or awards the candidate has received")
    contact_email: str = Field(description="Email address of the candidate")
    category: str = Field(description="Category or type of job the candidate is applying for (e.g., HR, IT, Marketing)")


 # Convert the Pydantic schema to a JSON schema
json_schema = str(ResumeExtractionScheme.model_json_schema())

text = data


client = Groq(api_key=GROQ_API_KEY)
chat_completion = client.chat.completions.create(
    model="llama-3.3-70b-versatile",
   messages=[
        {"role": "system", 
         "content": "You are a Recruiter. Find the top 10 best candidates for the provided Job Description. Follow the schema provided below to extract the relevant details. Do not invent information that is not in the provided text. Output JSON only."},
        {"role": "user",
        "content": f"Extract information from the following resumes: {text}\nUse the following JSON schema: {json_schema}"
        },
    ],
    temperature=1,
    max_tokens=1024,
    top_p=1,
    stream=True,
    stop=None,
)

# Ausgabe sammeln
response_content = ""
for chunk in chat_completion:
    response_content += chunk.choices[0].delta.content or ""

output_path = "bestFit.json"
output_data = {"analysis_result": response_content,
}

with open(output_path, "w", encoding="utf-8") as f:
    json.dump(output_data, f, ensure_ascii=False, indent=4)

print(f"Analysis result saved to {output_path}")