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app.py

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+# -*- coding: utf-8 -*-
+"""Feedaltytics Recruiters Evaluation - FeedView.py
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/10ERbz5c3Zuw50gSoCCDEuqP4wGsvdAaM
+"""
+
+from docx import Document
+import openai
+import gradio as gr
+
+# Set OpenAI API key
+openai.api_key = 'sk-HPlLtHJKP7W3LXMh1-5lCzq9iJAepCFcm-6Ahvb4dmT3BlbkFJj2yl0vrpYL9D1YUHRGjk1RbraVK2Cy4zjYKiBPeooA'
+
+# User credentials
+USER_CREDENTIALS = {"admin": "password123"}  # Replace with desired username/password pairs
+
+# Function to extract text from DOCX files
+def extract_text_from_docx(file):
+    doc = Document(file)
+    return '\n'.join([para.text for para in doc.paragraphs])
+
+# Function to extract top 5 criteria and generate interview questions
+def extract_criteria_and_questions(job_description):
+    try:
+        user_content = (
+            f"Job Description:\n{job_description}\n\n"
+            "Based on this job description, please identify the top 5 key attributes or skills required for this role. "
+            "For each attribute, provide 2-3 suggested interview questions that recruiters can use to assess candidates effectively."
+        )
+
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo-16k",
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are an HR expert tasked with analyzing job descriptions and generating "
+                               "key attributes to assess candidates, along with relevant interview questions. Be precise and concise."
+                },
+                {
+                    "role": "user",
+                    "content": user_content
+                }
+            ],
+            max_tokens=1000,
+            temperature=0.0,
+            top_p=1.0
+        )
+
+        result = response['choices'][0]['message']['content'].strip()
+        return result
+
+    except openai.error.OpenAIError as e:
+        return f"OpenAI API Error: {e}"
+    except Exception as e:
+        return f"Unexpected Error: {e}"
+
+# Function to generate Recruiter's Report
+def generate_recruiter_report(candidate_transcript, job_description, benchmark, criteria):
+    try:
+        user_content = (
+            f"Job Description:\n{job_description}\n\n"
+            f"Benchmark Transcript:\n{benchmark}\n\n"
+            f"Candidate Transcript:\n{candidate_transcript}\n\n"
+            f"Evaluation Criteria:\n{criteria}\n\n"
+            "Evaluate the candidate's overall performance against all criteria. Provide a detailed summary of their "
+            "strengths, areas for improvement, and an overall score based on the following grading scale:\n"
+            "Rate the candidate on a scale of 1-10 for each factor:\n"
+            "- **1**: Very poor, far below expectations, minimal skills or understanding.\n"
+            "- **2-3**: Below average, some understanding but lacking in key areas.\n"
+            "- **4-5**: Average, meets basic expectations with minimal distinction.\n"
+            "- **6-7**: Above average, solid performance with minor gaps.\n"
+            "- **8-9**: Strong, exceeds expectations with well-supported examples.\n"
+            "- **10**: Outstanding, exceptional performance with comprehensive examples.\n"
+            "Provide a total score out of 50 and a recommendation based on their suitability for the role."
+        )
+
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo-16k",
+            messages=[
+                {
+                    "role": "system",
+                    "content": (
+                        "You are a recruitment specialist evaluating candidates for a role. Use the provided criteria "
+                        "and benchmarks to ensure consistency across evaluations. Keep the output professional."
+                    )
+                },
+                {
+                    "role": "user",
+                    "content": user_content
+                }
+            ],
+            max_tokens=1500,
+            temperature=0.0,
+            top_p=1.0
+        )
+
+        report = response['choices'][0]['message']['content'].strip()
+        return report
+
+    except openai.error.OpenAIError as e:
+        return f"OpenAI API Error: {e}"
+    except Exception as e:
+        return f"Unexpected Error: {e}"
+
+# Function to compare candidates and recommend the best fit
+def compare_candidates_for_role(evaluation_reports, job_description):
+    try:
+        user_content = (
+            f"Job Description:\n{job_description}\n\n"
+            f"Recruiter's Evaluation Reports:\n{evaluation_reports}\n\n"
+            "Based on the provided job description and evaluation reports for multiple candidates, "
+            "analyze and compare their performance. Identify the candidate who best matches the role requirements, "
+            "and provide a summary of why this candidate is the best fit. Highlight any specific attributes or skills "
+            "that set them apart from others."
+        )
+
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo-16k",
+            messages=[
+                {
+                    "role": "system",
+                    "content": (
+                        "You are an expert recruiter tasked with analyzing evaluation reports and recommending the best "
+                        "candidate for a role. Provide a detailed analysis with clear justification for your choice."
+                    )
+                },
+                {
+                    "role": "user",
+                    "content": user_content
+                }
+            ],
+            max_tokens=1500,
+            temperature=0.0,
+            top_p=1.0
+        )
+
+        recommendation = response['choices'][0]['message']['content'].strip()
+        return recommendation
+
+    except openai.error.OpenAIError as e:
+        return f"OpenAI API Error: {e}"
+    except Exception as e:
+        return f"Unexpected Error: {e}"
+
+# Function to process multiple transcripts
+def process_transcripts(job_description_file, benchmark_file, candidate_files):
+    job_description = extract_text_from_docx(job_description_file)
+    benchmark = extract_text_from_docx(benchmark_file)
+    candidate_transcripts = [extract_text_from_docx(candidate_file) for candidate_file in candidate_files]
+
+    # Step 1: Extract top 5 criteria
+    criteria_and_questions = extract_criteria_and_questions(job_description)
+
+    # Step 2: Evaluate each candidate using all criteria
+    criteria = criteria_and_questions.split("\n\n")[:5]  # Extract top criteria only
+    criteria_text = "\n\n".join(criteria)
+
+    all_reports = []
+    individual_reports = {}
+
+    for i, transcript in enumerate(candidate_transcripts):
+        report = generate_recruiter_report(transcript, job_description, benchmark, criteria_text)
+        all_reports.append(f"--- Candidate {i + 1} ---\n{report}\n")
+        individual_reports[f"candidate_{i + 1}"] = report
+
+    # Step 3: Compare candidates and recommend the best fit
+    comparison_report = compare_candidates_for_role("\n".join(all_reports), job_description)
+
+    return criteria_and_questions, "\n".join(all_reports), individual_reports, comparison_report
+
+# Gradio interface setup
+def main():
+    with gr.Blocks(css="""
+    #company-logo img {
+        width: 150px;
+        height: auto;
+        margin: 0 auto;
+        display: block;
+    }
+    """) as interface:
+        username = gr.Textbox(label="Username")
+        password = gr.Textbox(label="Password", type="password")
+        login_button = gr.Button("Login")
+        auth_status = gr.Textbox(label="Login Status", interactive=False)
+
+        with gr.Group(visible=False) as app_interface:
+            gr.Image(
+                value="https://drive.google.com/uc?id=1tgTSBVCm6gvg6EGEsgHsjsqyiGefxqHP",
+                show_label=False,
+                elem_id="company-logo"
+            )
+            gr.Markdown("### Recruiter's Evaluation Tool")
+
+            mode = gr.Radio(choices=["Generate Criteria", "Evaluate Candidates"], label="Choose Mode")
+
+            job_description_file = gr.File(label="Job Description (.docx)")
+
+            # Criteria Generation Outputs
+            criteria_output = gr.Textbox(label="Generated Criteria", lines=15)
+
+            # Candidate Evaluation Inputs and Outputs
+            benchmark_file = gr.File(label="Benchmark Transcript (.docx)")
+            candidate_files = gr.File(label="Candidate Transcripts (.docx)", file_count="multiple")
+            recruiter_output = gr.Textbox(label="Evaluation Reports", lines=30)
+            comparison_output = gr.Textbox(label="Best Fit Recommendation", lines=10)
+
+            def generate_criteria(job_description_file):
+                job_description = extract_text_from_docx(job_description_file)
+                return extract_criteria_and_questions(job_description)
+
+            def evaluate_candidates(job_description_file, benchmark_file, candidate_files):
+                _, reports, _, comparison = process_transcripts(job_description_file, benchmark_file, candidate_files)
+                return reports, comparison
+
+            mode.change(
+                lambda x: gr.update(visible=(x == "Generate Criteria")),
+                inputs=[mode],
+                outputs=[criteria_output]
+            )
+
+            # Generate Criteria
+            gr.Button("Generate Criteria").click(
+                generate_criteria,
+                inputs=[job_description_file],
+                outputs=[criteria_output]
+            )
+
+            # Evaluate Candidates
+            gr.Button("Evaluate Candidates").click(
+                evaluate_candidates,
+                inputs=[job_description_file, benchmark_file, candidate_files],
+                outputs=[recruiter_output, comparison_output]
+            )
+
+        def authenticate(username_value, password_value):
+            if username_value in USER_CREDENTIALS and USER_CREDENTIALS[username_value] == password_value:
+                return gr.update(visible=True), "Login Successful!"
+            else:
+                return gr.update(visible=False), "Login Failed. Please check your credentials."
+
+        login_button.click(
+            authenticate,
+            inputs=[username, password],
+            outputs=[app_interface, auth_status]
+        )
+
+    interface.launch()
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+openai==0.28
+gradio==3.11
+python-docx
+bcrypt
+PyPDF2
+httpx==0.23.0
+httpcore==0.15.0