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from groq import Groq
import json
import os
from pydantic import BaseModel, Field
from typing import List, Optional
import textwrap
import pandas as pd
from charset_normalizer import from_path
# Load data from a CSV file
data = pd.read_csv("uploaded_file.csv")
# Get the GROQ API key from environment variables
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
# Initialize the client with the GROQ API key
client = Groq(
api_key=GROQ_API_KEY, # Use the GROQ_API_KEY variable directly
)
# Funktion zum Laden der JobAnalyzed.json Datei
def load_job_analyzed(file_path):
"""
Liest die JobAnalyzed.json Datei und gibt den Inhalt als Python-Objekt zurück.
"""
try:
# Datei öffnen und JSON-Inhalt laden
with open(file_path, "r", encoding="utf-8") as file:
job_data = json.load(file) # Die Datei wird als JSON geladen
return job_data
except FileNotFoundError:
raise FileNotFoundError(f"File '{file_path}' not found.")
except json.JSONDecodeError:
raise RuntimeError(f"Error decoding JSON in the file '{file_path}'.")
except Exception as e:
raise RuntimeError(f"Error reading file: {e}")
# Load the job description from a file
job_analyzed = load_job_analyzed("JobAnalyzed.json") # Adjust the path if necessary
text = data
structure_ranking = {"""
rankedCandidates:
[
{
"name": "",
"email": "",
"rank":"",
"keySkills": [],
"experience": "",
"reasoning": ""
}
]
"""}
# Initialize the Groq API client and make a request to analyze the resumes and job description
client = Groq(api_key=GROQ_API_KEY)
chat_completion = client.chat.completions.create(
model="llama-3.3-70b-versatile",
messages=[
{"role": "system",
"content":
f"You are a Recruiter. Find the top 5 best candidates for the provided Job Description analysis in {job_analyzed} and rank them. Use the {structure_ranking}. Output JSON only."},
{"role": "user",
"content": f"Extract information from the following resumes: {text}.Just output an JSON, we dont need any other text ! DO NOT INVENT THINGS."
},
],
temperature=0.5,
max_tokens=1024,
top_p=1,
stream=True,
stop=None,
)
# Collect the response
response_content = ""
for chunk in chat_completion:
response_content += chunk.choices[0].delta.content or ""
# Remove triple backticks and the json tag
response_content = response_content.strip("```json\n").strip("```")
# Attempt to parse the response content as JSON
try:
analysis_result = json.loads(response_content)
except json.JSONDecodeError as e:
print(f"Error parsing JSON response: {e}")
analysis_result = {"error": "Failed to parse JSON", "raw_response": response_content}
# Save the results to a JSON file
output_path = "bestFit.json"
output_data = {"analysis_result": analysis_result}
try:
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}")
except Exception as e:
print(f"Error saving JSON data: {e}")
print(response_content) |