Delete dpm1.py

dpm1.py

@@ -1,61 +0,0 @@
-# -*- coding: utf-8 -*-
-"""dpm1
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1PJnjffQq5PpvzMoY1aB5uggxb35r11Ut
-"""
-
-#!pip install streamlit
-
-#!pip install transformers
-
-import streamlit as st
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-
-model_name = "EleutherAI/gpt-neo-1.3B"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
-
-def generate_job_posting(position, job_type, skillset, company_name, work_experience, job_location, job_benefits, field, currency, currency_format, package, communication, notice_period, qualifications, responsibility):
-    context = f"{company_name} is hiring a {position} for a {job_type} position in {job_location}. The ideal candidate will have at least {work_experience} years of experience in {field}, and should be proficient in {skillset}. The job responsibility includes:\n- {responsibility}\nWe offer a {package} package in currency format {currency_format}. The successful candidate will be expected to maintain excellent communication with clients and colleagues. The notice period for this role is {notice_period}, and applicants should have the following qualifications: {qualifications}."
-    output = generator(context, max_length=240, do_sample=True, temperature=0.7, num_return_sequences=3)
-    def score_output(output_text):
-        sentences = output_text.split('.')
-        avg_sentence_length = sum(len(s.strip()) for s in sentences) / len(sentences)
-        return 1.0 / avg_sentence_length
-    sorted_outputs = sorted(output, key=lambda x: score_output(x['generated_text']), reverse=True)
-    best_output = sorted_outputs[0]
-    output_text = best_output['generated_text'].replace('- job responsibility', f"\n\n- {responsibility}")
-    paragraphs = output_text.split('\n\n')
-    output_text = '\n\n'.join('\n' + p for p in paragraphs)
-    return output_text
-
-st.title("Job Posting Generator")
-
-position = st.text_input("Enter Position for Job Posting:")
-job_type = st.text_input("Enter Job Type:")
-skillset = st.text_input("Enter Skillset:")
-company_name = st.text_input("Enter Company Name:")
-work_experience = st.text_input("Enter Required Work Experience (in years):")
-job_location = st.text_input("Enter job Location:")
-job_benefits = st.text_input("Enter job benefits:")
-field = st.text_input("Enter job field:")
-currency = st.text_input("Enter Currency:")
-currency_format = st.text_input("Enter Currency Format:")
-package = st.text_input("Enter Package:")
-communication = st.text_input("Enter Communication:")
-notice_period = st.text_input("Enter Notice Period:")
-qualifications = st.text_input("Enter Qualifications:")
-responsibility = st.text_area("Enter Job Responsibility:")
-
-if st.button("Generate Job Posting"):
-    try:
-        output_text = generate_job_posting(position, job_type, skillset, company_name, work_experience, job_location, job_benefits, field, currency, currency_format, package, communication, notice_period, qualifications, responsibility)
-        st.success(output_text)
-    except ValueError as e:
-        st.error(f"Error: {e}")
-    except Exception as e:
-        st.error(f"An error occurred: {e}")