Update to POC Version

app.py

@@ -1,13 +1,102 @@
 import openai
 import gradio as gr
 from PyPDF2 import PdfReader
-from dotenv import load_dotenv
+from azure.storage.blob import BlobServiceClient
+import io
+from PyPDF2 import PdfReader
+import json
+import smtplib
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+from email.mime.application import MIMEApplication
+import os
 
 load_dotenv()
 
+os_connection_string = os.getenv("CONNECTION")
+os_mail_password = os.getenv("MAIL_PASSWORD")
+
 with open("sys_prompt.txt") as f:
     sys_prompt = f.read()
 
+get_window_url_params = """
+    function(job, job_params) {
+        console.log(job, job_params);
+        const params = new URLSearchParams(window.location.search);
+        job_params = Object.fromEntries(params);
+        return [job, job_params];
+        }
+    """
+
+def download_and_parse_json_blob(storage_connection_string, container_name, blob_name, encoding='utf-8'):
+    try:
+        # Verbindung zum Blob-Dienst herstellen
+        blob_service_client = BlobServiceClient.from_connection_string(storage_connection_string)
+
+        # Container und Blob-Client erstellen
+        container_client = blob_service_client.get_container_client(container_name)
+        blob_client = container_client.get_blob_client(blob_name)
+
+        # Blob herunterladen
+        blob_data = blob_client.download_blob()
+        blob_bytes = blob_data.readall()
+
+        # JSON-Bytes in einen Python-Datenobjekt umwandeln
+        json_text = blob_bytes.decode(encoding)
+        json_data = json.loads(json_text)
+
+        # Parameter "title" und "email" aus dem JSON-Datenobjekt extrahieren und zurückgeben
+        title = json_data.get("title", "")
+        email = json_data.get("email", "")
+
+        return title, email
+    except Exception as e:
+        print(f"Fehler beim Herunterladen und Verarbeiten der JSON-Datei: {str(e)}")
+        return None, None
+
+def download_pdf_blob_as_text(storage_connection_string, container_name, blob_name):
+    try:
+        # Verbindung zum Blob-Dienst herstellen
+        blob_service_client = BlobServiceClient.from_connection_string(storage_connection_string)
+
+        # Container und Blob-Client erstellen
+        container_client = blob_service_client.get_container_client(container_name)
+        blob_client = container_client.get_blob_client(blob_name)
+
+        # Blob herunterladen und als Binärdaten speichern
+        blob_data = blob_client.download_blob()
+        pdf_bytes = blob_data.readall()
+
+        # PDF-Text extrahieren
+        pdf_text = ""
+        pdf_reader = PdfReader(io.BytesIO(pdf_bytes))
+        for page_num in range(len(pdf_reader.pages)):
+            page = pdf_reader.pages[page_num]
+            pdf_text += page.extract_text()
+
+        return pdf_text
+    except Exception as e:
+        print(f"Fehler beim Herunterladen und Konvertieren der Datei: {str(e)}")
+        return None
+
+def load_job_data(job, job_params):
+    if not job:
+        try:
+            print("Hello there")
+            print(job_params["job"])
+            pdf_filename = job_params["job"]+".pdf"
+            json_filename = job_params["job"]+"_jsondata.json" 
+            storage_connection_string = os_connection_string
+            container_name = "jobdescriptions"  # Der Name des Blob-Containers
+            job = download_pdf_blob_as_text(storage_connection_string, container_name, pdf_filename)
+            job_params = download_and_parse_json_blob(storage_connection_string,container_name,json_filename)
+            return job, job_params, gr.Label.update("Evaluation for the job: "+job_params[0])
+        except:
+            gr.Error("An error occurred, the job description could not be loaded. Please contact the recruiter.")
+            return job, job_params, gr.Label.update("An error occurred and the job description could not be loaded. Please contact the recruiter.", color="red") 
+        # print(job)
+        # print(job_params)
+
 
 def add_file(file, chat, job, resume):
     if file.name.endswith(".pdf"):
@@ -20,11 +109,13 @@ def add_file(file, chat, job, resume):
             text = f.read()
 
     if job:
+        print("im cv")
         chat += [["📄 " + file.name.split("/")[-1], None]]
         resume = text
-    else:
-        chat += [["📄 " + file.name.split("/")[-1], "Thanks. Please upload the resume."]]
-        job = text
+    # else:
+    #     print("im job")
+    #     chat += [["📄 " + file.name.split("/")[-1], "Thanks. Please upload the resume."]]
+    #     job = text
 
     return chat, job, resume
 
@@ -33,12 +124,13 @@ def user(message, history):
     return "", history + [[message, None]]
 
 
-def bot(history, job, resume):
-    if not resume:
+def bot(history, job, resume, job_params):
+
+    if not resume or not job:
         yield history
         return
 
-    system = sys_prompt.format(job=job, resume=resume, n=8)
+    system = sys_prompt.format(job=job, resume=resume, n=10)
     messages = [{"role": "system", "content": system}]
     for user, assistant in history:
         if user:
@@ -58,27 +150,122 @@ def bot(history, job, resume):
         if len(chunk["choices"][0]["delta"]) != 0:
             history[-1][1] = history[-1][1] + chunk["choices"][0]["delta"]["content"]
             yield history
+    if history[-1][1] == "Thank you for conducting the evaluation! We will get back to you shortly.":
+        print("finished")
+        send_evaluation(history, job, resume, job_params)
     return
 
+def send_evaluation(history, job, resume, job_params):
+
+    # Chatverlauf in einen Textstring umwandeln
+    chat_text = ""
+    for entry in history:
+        if entry[0]:
+            chat_text += "Applicant: " + entry[0] + "\n"
+        if entry[1]:
+            chat_text += "Chatbot: " + entry[1] + "\n"
+
+    # Einstellungen für den SMTP-Server
+    smtp_server = "smtp.gmail.com"
+    smtp_port = 587
+    smtp_username = "[email protected]"
+    smtp_password = os_mail_password
+
+    # Sender- und Empfänger-E-Mail-Adressen
+    sender_email = "[email protected]"
+    receiver_email = job_params[1]
+
+    ai_summary = "TEST Summary"
+    prompt = "You are a professional recruiter who has been given a CV and a job description and has created 10 questions based on that. The eventual applicant has entered his answers to the questions. Now you have to evaluate on the basis of the answers if the applicant fits the job in principle. This is the case when about 70percent of all questions have been answered satisfactorily and positively. Keep in mind that an answer must always be fact-based, so if, for example, the question asks for examples, the potential applicant must also give such examples. Please also provide details of which questions were answered positively and why."
+    res = openai.ChatCompletion.create(
+            model="gpt-4",
+            temperature=0.2,
+            messages=[
+                {
+                    "role": "system",
+                    "content": prompt,
+                },
+                {"role": "system", "content": "Job description: "+job+"; Resume: "+resume},
+                {"role": "system", "content": "Chathistory: "+chat_text},
+            ],
+    )   
+    ai_summary = res.choices[0]["message"]["content"]
+    # E-Mail-Nachricht erstellen
+    subject = "Evaluation for the job: "+job_params[0] 
+    message = f"""
+Dear Recruiter,
+
+Please find attached the complete chat history for this evaluation and resume.
+
+The evaluation AI-supported summarized:
+
+{ai_summary}
+
+Sincerely,
+Your Evaluation Tool
+"""
+
+    msg = MIMEMultipart()
+    msg['From'] = sender_email
+    msg['To'] = receiver_email
+    msg['Subject'] = subject
+    msg.attach(MIMEText(message, 'plain'))
+
+    # Chatverlauf in eine Textdatei schreiben
+    chat_file_path = "chat_history.txt"
+    with open(chat_file_path, 'w', encoding='utf-8') as chat_file:
+        chat_file.write(chat_text)
+
+    # Chatverlauf als Anhang hinzufügen
+    with open(chat_file_path, 'r', encoding='utf-8') as chat_file:
+        chat_attachment = MIMEText(chat_file.read(), _subtype='plain')
+        chat_attachment.add_header('Content-Disposition', 'attachment', filename='chat_history.txt')
+        msg.attach(chat_attachment)
+
+    # Resume-Bytes in eine TXT-Datei schreiben und als Anhang hinzufügen
+    resume_file = io.BytesIO()
+    resume_file.write(resume.encode('utf-8'))
+    resume_file.seek(0)
+    resume_attachment = MIMEText(resume_file.read().decode('utf-8'), _subtype='plain')
+    resume_attachment.add_header('Content-Disposition', 'attachment', filename='resume.txt')
+    msg.attach(resume_attachment)
+
+    # SMTP-Verbindung herstellen und E-Mail senden
+    try:
+        server = smtplib.SMTP(smtp_server, smtp_port)
+        server.starttls()
+        server.login(smtp_username, smtp_password)
+        text = msg.as_string()
+        server.sendmail(sender_email, receiver_email, text)
+        server.quit()
+        print("E-Mail wurde erfolgreich gesendet.")
+    except Exception as e:
+        print("Fehler beim Senden der E-Mail:", str(e))
 
 css = "footer {visibility: hidden} #component-0{height: 90vh !important} #chatbot{height: 85vh !important}}"
 
 with gr.Blocks(css=css) as app:
+    job_params = gr.JSON({}, visible=False, label="URL Params")
     job = gr.State("")
     resume = gr.State("")
+    gr.Markdown(
+        f"<div style='display: flex; justify-content: space-between;align-items: center;margin-bottom: 1rem' ><h1>CV Evaluation</h1><img width='150' src='https://www.workgenius.com/wp-content/uploads/2023/03/WorkGenius_navy-1.svg' alt='WorkGeniusLogo' /></div>"
+    )
+    input_test = gr.Label("An error occurred and the job description could not be loaded. Please contact the recruiter.", show_label=False)
+    app.load(load_job_data,[job, job_params], [job, job_params, input_test], _js=get_window_url_params)
     chat = gr.Chatbot(
-        [[None, "Please upload the job description."]], height=600, elem_id="chatbot"
+        [[None, "Please upload your resume to begin the evaluation process. After uploading, approximately 10 questions will be asked to determine if the position is a good fit for you. The entire process takes about 10 to 15 minutes."]], height=600, elem_id="chatbot"
     )
     with gr.Row():
         clr = gr.Button("Clear", scale=0)
         msg = gr.Textbox(lines=1, show_label=False, scale=1)
         file = gr.UploadButton("Browse", file_types=[".pdf", ".txt"], scale=0)
     msg.submit(user, [msg, chat], [msg, chat], queue=False).then(
-        bot, [chat, job, resume], chat
+        bot, [chat, job, resume, job_params], chat
     )
     file.upload(
         add_file, [file, chat, job, resume], [chat, job, resume], queue=False
-    ).then(bot, [chat, job, resume], chat)
+    ).then(bot, [chat, job, resume, job_params], chat)
     clr.click(lambda: None, None, chat, queue=False)
 
 app.queue()