main.py

# import gradio as gr
# from huggingface_hub import InferenceClient
# import random

# models = [
#     "google/gemma-7b",
#     "google/gemma-7b-it",
#     "google/gemma-2b",
#     "google/gemma-2b-it"
# ]

# clients = []
# for model in models:
#     clients.append(InferenceClient(model))


# def format_prompt(message, history):
#     prompt = ""
#     if history:
#         for user_prompt, bot_response in history:
#             prompt += f"<start_of_turn>user{user_prompt}<end_of_turn>"
#             prompt += f"<start_of_turn>model{bot_response}"
#     prompt += f"<start_of_turn>user{message}<end_of_turn><start_of_turn>model"
#     return prompt


# def chat_inf(system_prompt, prompt, history, client_choice, seed, temp, tokens, top_p, rep_p):
#     client = clients[int(client_choice) - 1]
#     if not history:
#         history = []
#         hist_len = 0
#     if history:
#         hist_len = len(history)
#         print(hist_len)

#     generate_kwargs = dict(
#         temperature=temp,
#         max_new_tokens=tokens,
#         top_p=top_p,
#         repetition_penalty=rep_p,
#         do_sample=True,
#         seed=seed,
#     )
#     formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
#     stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True,
#                                     return_full_text=False)
#     output = ""

#     for response in stream:
#         output += response.token.text
#         yield [(prompt, output)]
#     history.append((prompt, output))
#     yield history


# def clear_fn():
#     return None


# rand_val = random.randint(1, 1111111111111111)


# def check_rand(inp, val):
#     if inp is True:
#         return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=random.randint(1, 1111111111111111))
#     else:
#         return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=int(val))


# with gr.Blocks() as app:
#     gr.HTML(
#         """<center><h1 style='font-size:xx-large;'>Google Gemma Models</h1></center>""")
#     with gr.Group():
#         with gr.Row():
#             client_choice = gr.Dropdown(label="Models", type='index', choices=[c for c in models], value=models[0],
#                                         interactive=True)
#     chat_b = gr.Chatbot(height=500)
#     with gr.Group():
#         with gr.Row():
#             with gr.Column(scale=1):
#                 with gr.Group():
#                     rand = gr.Checkbox(label="Random Seed", value=True)
#                     seed = gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, step=1, value=rand_val)
#                     tokens = gr.Slider(label="Max new tokens", value=6400, minimum=0, maximum=8000, step=64,
#                                        interactive=True, visible=True, info="The maximum number of tokens")
#             with gr.Column(scale=1):
#                 with gr.Group():
#                     temp = gr.Slider(label="Temperature", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
#                     top_p = gr.Slider(label="Top-P", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
#                     rep_p = gr.Slider(label="Repetition Penalty", step=0.1, minimum=0.1, maximum=2.0, value=1.0)

#     with gr.Group():
#         with gr.Row():
#             with gr.Column(scale=3):
#                 sys_inp = gr.Textbox(label="System Prompt (optional)")
#                 inp = gr.Textbox(label="Prompt")
#                 with gr.Row():
#                     btn = gr.Button("Chat")
#                     stop_btn = gr.Button("Stop")
#                     clear_btn = gr.Button("Clear")

#     chat_sub = inp.submit(check_rand, [rand, seed], seed).then(chat_inf,
#                                                                [sys_inp, inp, chat_b, client_choice, seed, temp, tokens,
#                                                                 top_p, rep_p], chat_b)
#     go = btn.click(check_rand, [rand, seed], seed).then(chat_inf,
#                                                         [sys_inp, inp, chat_b, client_choice, seed, temp, tokens, top_p,
#                                                          rep_p], chat_b)
#     stop_btn.click(None, None, None, cancels=[go, chat_sub])
#     clear_btn.click(clear_fn, None, [chat_b])
# app.queue(default_concurrency_limit=10).launch()

import gradio as gr
import pandas as pd
import spacy
import re
import time
import plotly.express as px

# Load spaCy model
nlp = spacy.load("en_core_web_sm")

class JobPosting:
    def __init__(self, description):
        self.description = description

    def extract(self):
        text = nlp(self.description)
        
        # Extract key nouns as qualifications
        qualifications = [token.text for token in text if token.pos_ == "NOUN"]

        # Extract salary using regex
        salary_regex = r"\$\d{1,3}K"
        salary = re.findall(salary_regex, self.description)

        # Extract pre-trained ORG entities as companies
        orgs = [ent.text for ent in text.ents if ent.label_ == "ORG"]

        # Define responsibilities extraction logic (replace with actual logic)
        responsibilities = "Define responsibilities here"

        return qualifications, salary, responsibilities

def main(description):
    job = JobPosting(description)
    qualifications, salary, responsibilities = job.extract()
    return f"Qualifications: {qualifications}\nSalary: {salary}\nResponsibilities: {responsibilities}"

iface = gr.Interface(fn=main, 
                      inputs="text",
                      outputs="text",
                      title="Job Posting Extractor",
                      description="Enter a job description to extract qualifications, salary, and responsibilities.")
iface.launch()