Update app_funtions.py

app_funtions.py

@@ -1,150 +1,145 @@
-import re
-
-from PwsResumeClassifier import PwsResumeClassifier as pws
-from openai import OpenAI
-import app_funtions as appfun
-import os
-import shutil
-import glob
-import gradio as gr
-import json
-from pathlib import Path
-
-os.environ['PROJECT'] = 'proj_ct77YbVBWyY3cNDoGA4Xkpzk'
-os.environ['OPENAIORG'] = 'org-Qc0wKzpN6ewg8NpSc9s7cTtS'
-os.environ['MILPASSWORD'] = 'MILRESUME'
-os.environ['APIKEY'] = 'sk-proj-24nTWywXfoK0f4fEd2jdT3BlbkFJLaAZTDMkNcY8HP20Asf2'
-
-job_names = []
-job_descriptions = []
-
-client_pws = pws(organization_id=os.getenv('OPENAIORG'), project_id=os.getenv('PROJECT'), api_key=os.getenv('APIKEY'))
-
-client = OpenAI(
-    organization=os.getenv('OPENAIORG'),
-    project=os.getenv('PROJECT'),
-    api_key=os.getenv('APIKEY')
-)
-
-def check_password(password):
-    if password == os.getenv('MILPASSWORD'):
-
-        return [gr.Textbox(visible=False), gr.Button(visible=False), gr.Row.update(visible=False), gr.Row.update(visible=True)]
-    else:
-        return [gr.Textbox(visible=True), gr.Button(visible=True), gr.Row.update(visible=True), gr.Row.update(visible=False)]
-
-
-def add_resume(resume_list):
-    for resume in resume_list:
-        shutil.copy2(os.path.abspath(resume), os.path.abspath('Resumes'))
-
-
-def remove_resumes(file_explorer):
-    for file in file_explorer:
-        os.remove(file)
-
-
-def job_selected(selected_name):
-    print(job_names)
-    if selected_name in job_names:
-        return [selected_name, job_descriptions[job_names.index(selected_name)], gr.Row.update(visible=True),
-                gr.Markdown()]
-    elif selected_name == 'Custom':
-        return ['', '', gr.Row.update(visible=True), gr.Markdown()]
-    else:
-        return ['', '', gr.Row.update(visible=False), gr.Markdown()]
-
-
-def send_to_openai(name, description):
-
-    try:
-        print('Sending to AI')
-        client_pws.set_vector_store(os.path.abspath('Resumes'))
-        resumes_output = client_pws.get_best_resumes('Job Name: ' + name + '. Job Description: ' + description)
-        response = gr.Markdown('<p>' + resumes_output + '</p>')
-        return [response, gr.Row.update(visible=True),
-                gr.Textbox(label="Input", info="", lines=1, placeholder="Ask the chatbot.", interactive=True, scale=1),
-                gr.Button("SEND", interactive=True, scale=1)]
-    except Exception as e:
-        response = gr.Markdown('<p>An error occurred. Please try again.</p>')
-        return [response, gr.Row.update(visible=True),
-                gr.Textbox(label="Input", info="", lines=1, placeholder="Please process resumes", interactive=False, scale=1),
-                gr.Button("SEND", interactive=False, scale=1)]
-
-
-def extract_jobs(filepath):
-    try:
-        description = r"""You are a document information extraction assistant that extract job names and job description 
-        precisely and accurately from a provided file."""
-
-        instructions = r"""You will be provided a document that contains information about one or more job positions. 
-        You will extract all of these job positions and return them in a JSON Array. Each item in the JSON Array will 
-        have a field "job_name" for the name of the job position and "job_description" for the description and 
-        requirements of the job position. Your response 
-        should only be a JSON Array with all items."""
-
-        file_streams = [open(filepath, "rb")]
-        vector_store = client.beta.vector_stores.create(
-            name="Job Description File",
-        )
-
-        file_batch = client.beta.vector_stores.file_batches.upload_and_poll(
-            vector_store_id=vector_store.id,
-            files=file_streams
-        )
-
-        assistant = client.beta.assistants.create(
-            name="Job Description Extract Assistant",
-            description=description,
-            instructions=instructions,
-            model="gpt-4o",
-            tools=[{"type": "file_search"}],
-            tool_resources={"file_search": {"vector_store_ids": [vector_store.id]}},
-            temperature=0
-        )
-        thread = client.beta.threads.create()
-
-        messages = client.beta.threads.messages.create(
-            thread_id=thread.id,
-            role="user",
-            content="Please extract all job descriptions"
-        )
-
-        run = client.beta.threads.runs.create_and_poll(
-            thread_id=thread.id,
-            assistant_id=assistant.id,
-        )
-
-        if run.status == 'completed':
-            messages = client.beta.threads.messages.list(
-                thread_id=thread.id
-            )
-
-        messages = client.beta.threads.messages.list(
-            thread_id=thread.id
-        )
-        print(messages.data[0].content[0].text.value)
-        json_array = messages.data[0].content[0].text.value
-        json_array = re.sub(r"^```json", "", json_array)
-        json_array = re.sub(r"```$", "", json_array)
-        json_array = re.sub(r"【.*?】", "", json_array)
-        print(json_array)
-        print(json.loads(json_array))
-
-        for job in json.loads(json_array):
-            job_names.append(job['job_name'])
-            job_descriptions.append(job['job_description'])
-        return [gr.Markdown("""<p><center>Extraction completed successfully</center></p>"""),
-                gr.Dropdown(label='Select Job', choices=job_names + ['Custom'], allow_custom_value=True)]
-    except Exception as e:
-        return [gr.Markdown("""<p><center>Parsing failed. Error message: """ + repr(e) + """</center></p>"""),
-                gr.Dropdown(label='Select Job', choices=appfun.job_names + ['Custom'], allow_custom_value=True)]
-
-
-def my_chatbot(user_input, history):
-    text = ""
-    history = history or []
-    output = client_pws.query_assistant(user_input)
-    history.append((user_input, output))
-
-    return history, history, text
+import re
+
+from PwsResumeClassifier import PwsResumeClassifier as pws
+from openai import OpenAI
+import app_funtions as appfun
+import os
+import shutil
+import glob
+import gradio as gr
+import json
+from pathlib import Path
+
+job_names = []
+job_descriptions = []
+
+client_pws = pws(organization_id=os.getenv('OPENAIORG'), project_id=os.getenv('PROJECT'), api_key=os.getenv('APIKEY'))
+
+client = OpenAI(
+    organization=os.getenv('OPENAIORG'),
+    project=os.getenv('PROJECT'),
+    api_key=os.getenv('APIKEY')
+)
+
+def check_password(password):
+    if password == os.getenv('MILPASSWORD'):
+
+        return [gr.Textbox(visible=False), gr.Button(visible=False), gr.Row.update(visible=False), gr.Row.update(visible=True)]
+    else:
+        return [gr.Textbox(visible=True), gr.Button(visible=True), gr.Row.update(visible=True), gr.Row.update(visible=False)]
+
+
+def add_resume(resume_list):
+    for resume in resume_list:
+        shutil.copy2(os.path.abspath(resume), os.path.abspath('Resumes'))
+
+
+def remove_resumes(file_explorer):
+    for file in file_explorer:
+        os.remove(file)
+
+
+def job_selected(selected_name):
+    print(job_names)
+    if selected_name in job_names:
+        return [selected_name, job_descriptions[job_names.index(selected_name)], gr.Row.update(visible=True),
+                gr.Markdown()]
+    elif selected_name == 'Custom':
+        return ['', '', gr.Row.update(visible=True), gr.Markdown()]
+    else:
+        return ['', '', gr.Row.update(visible=False), gr.Markdown()]
+
+
+def send_to_openai(name, description):
+
+    try:
+        print('Sending to AI')
+        client_pws.set_vector_store(os.path.abspath('Resumes'))
+        resumes_output = client_pws.get_best_resumes('Job Name: ' + name + '. Job Description: ' + description)
+        response = gr.Markdown('<p>' + resumes_output + '</p>')
+        return [response, gr.Row.update(visible=True),
+                gr.Textbox(label="Input", info="", lines=1, placeholder="Ask the chatbot.", interactive=True, scale=1),
+                gr.Button("SEND", interactive=True, scale=1)]
+    except Exception as e:
+        response = gr.Markdown('<p>An error occurred. Please try again.</p>')
+        return [response, gr.Row.update(visible=True),
+                gr.Textbox(label="Input", info="", lines=1, placeholder="Please process resumes", interactive=False, scale=1),
+                gr.Button("SEND", interactive=False, scale=1)]
+
+
+def extract_jobs(filepath):
+    try:
+        description = r"""You are a document information extraction assistant that extract job names and job description 
+        precisely and accurately from a provided file."""
+
+        instructions = r"""You will be provided a document that contains information about one or more job positions. 
+        You will extract all of these job positions and return them in a JSON Array. Each item in the JSON Array will 
+        have a field "job_name" for the name of the job position and "job_description" for the description and 
+        requirements of the job position. Your response 
+        should only be a JSON Array with all items."""
+
+        file_streams = [open(filepath, "rb")]
+        vector_store = client.beta.vector_stores.create(
+            name="Job Description File",
+        )
+
+        file_batch = client.beta.vector_stores.file_batches.upload_and_poll(
+            vector_store_id=vector_store.id,
+            files=file_streams
+        )
+
+        assistant = client.beta.assistants.create(
+            name="Job Description Extract Assistant",
+            description=description,
+            instructions=instructions,
+            model="gpt-4o",
+            tools=[{"type": "file_search"}],
+            tool_resources={"file_search": {"vector_store_ids": [vector_store.id]}},
+            temperature=0
+        )
+        thread = client.beta.threads.create()
+
+        messages = client.beta.threads.messages.create(
+            thread_id=thread.id,
+            role="user",
+            content="Please extract all job descriptions"
+        )
+
+        run = client.beta.threads.runs.create_and_poll(
+            thread_id=thread.id,
+            assistant_id=assistant.id,
+        )
+
+        if run.status == 'completed':
+            messages = client.beta.threads.messages.list(
+                thread_id=thread.id
+            )
+
+        messages = client.beta.threads.messages.list(
+            thread_id=thread.id
+        )
+        print(messages.data[0].content[0].text.value)
+        json_array = messages.data[0].content[0].text.value
+        json_array = re.sub(r"^```json", "", json_array)
+        json_array = re.sub(r"```$", "", json_array)
+        json_array = re.sub(r"【.*?】", "", json_array)
+        print(json_array)
+        print(json.loads(json_array))
+
+        for job in json.loads(json_array):
+            job_names.append(job['job_name'])
+            job_descriptions.append(job['job_description'])
+        return [gr.Markdown("""<p><center>Extraction completed successfully</center></p>"""),
+                gr.Dropdown(label='Select Job', choices=job_names + ['Custom'], allow_custom_value=True)]
+    except Exception as e:
+        return [gr.Markdown("""<p><center>Parsing failed. Error message: """ + repr(e) + """</center></p>"""),
+                gr.Dropdown(label='Select Job', choices=appfun.job_names + ['Custom'], allow_custom_value=True)]
+
+
+def my_chatbot(user_input, history):
+    text = ""
+    history = history or []
+    output = client_pws.query_assistant(user_input)
+    history.append((user_input, output))
+
+    return history, history, text