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import gradio as gr
from typing import Dict
import pandas as pd
# from src.application.services import InterviewAnalyzer
# from src.infrastructure.llm import LangchainService
# from src.infrastructure.emotion import DeepFaceService
# from src.infrastructure.speech import GoogleSpeechService
# class GradioInterface:
# def __init__(self):
# # Initialize services
# self.emotion_service = DeepFaceService()
# self.speech_service = GoogleSpeechService()
# self.llm_service = LangchainService()
#
# # Initialize analyzer
# self.analyzer = InterviewAnalyzer(
# emotion_service=self.emotion_service,
# speech_service=self.speech_service,
# llm_service=self.llm_service,
# )
#
# def create_interface(self) -> gr.Interface:
# def process_submission(
# video_file: str, resume_file: str, job_requirements: str
# ) -> Dict:
# # Implementation for processing submission
# pass
#
# # Create Gradio interface
# interface = gr.Interface(
# fn=process_submission,
# inputs=[
# gr.Video(label="Interview Recording"),
# gr.File(label="Resume"),
# gr.Textbox(label="Job Requirements", lines=5),
# ],
# outputs=gr.JSON(label="Analysis Results"),
# title="HR Interview Analysis System",
# description="Upload interview recording and resume to analyze candidate performance",
# )
#
# return interface
# Testing to setup the simple interface
class GradioInterface:
def __init__(self):
# DataFrame to List All Users' Feedbacks
self.candidate_feedback = pd.DataFrame(columns=["Name", "Score", "Feedback"])
def validate_file_format(self, file_path: str, valid_extensions: list) -> bool:
return isinstance(file_path, str) and any(
file_path.endswith(ext) for ext in valid_extensions
)
def process_video(self, video_path: str) -> str:
# Process transcript from the video
return "### Transcript\nExample of transcript of the interview video."
def process_resume(self, resume_path: str) -> str:
# Resume Parsing
return "### Resume Analysis\n- **Skills**: NLP, Machine Learning, Computer Vision\n- **Experience**: 5 years."
def analyze_emotions(self, video_path: str) -> str:
# Emotion Analysis
return "### Emotion Analysis\n- **Overall Emotion**: Positive\n- **Details**: Candidate displayed confidence and engagement."
def get_feedback(self, name: str, score: int, feedback: str) -> pd.DataFrame:
return pd.DataFrame({"Name": [name], "Score": [score], "Feedback": [feedback]})
def save_report(self):
# Save report
report_path = "report_path.docx"
with open(report_path, "w") as f:
# Pass fields to include in report here
f.write("Example report")
return report_path
def create_interface(self) -> gr.Blocks:
def process_submission(
video_path, resume_path, interview_questions, job_requirements
):
# Validate inputs and formats
if not video_path:
return (
"Please upload an interview video.",
None,
None,
self.candidate_feedback,
)
if not resume_path:
return (
"Please upload a resume (PDF).",
None,
None,
self.candidate_feedback,
)
if not interview_questions:
return (
"Please provide interview questions.",
None,
None,
self.candidate_feedback,
)
if not job_requirements:
return (
"Please provide job requirements.",
None,
None,
self.candidate_feedback,
)
if not self.validate_file_format(video_path, [".mp4", ".avi", ".mkv"]):
return "Invalid video format.", None, None, self.candidate_feedback
if not self.validate_file_format(resume_path, [".pdf"]):
return (
"Please submit resume in PDF format.",
None,
None,
self.candidate_feedback,
)
# Mock outputs for this submission
video_transcript = self.process_video(video_path)
emotion_analysis = self.analyze_emotions(video_path)
resume_analysis = self.process_resume(resume_path)
# Example of Feedback
feedback_list = self.get_feedback(
name="Johnson",
score=88,
feedback="Outstanding technical and soft skills.",
)
# Append the new candidate feedback to the DataFrame
self.candidate_feedback = pd.concat(
[self.candidate_feedback, feedback_list], ignore_index=True
)
# Return both the individual result and the list result
return (
video_transcript,
emotion_analysis,
resume_analysis,
self.candidate_feedback,
)
# Build the interface using Blocks
with gr.Blocks() as demo:
gr.Markdown("## HR Interview Analysis System")
# Inputs section
with gr.Row():
video_input = gr.Video(label="Upload Interview Video")
resume_input = gr.File(label="Upload Resume (PDF)")
with gr.Row():
question_input = gr.Textbox(
label="Interview Questions",
lines=5,
placeholder="Enter the interview question here",
)
requirements_input = gr.Textbox(
label="Job Requirements",
lines=5,
placeholder="Enter the job requirements here",
)
submit_button = gr.Button("Submit")
with gr.Tabs():
with gr.Tab("Result"):
transcript_output = gr.Markdown(label="Video Transcript")
emotion_output = gr.Markdown(label="Emotion Analysis")
resume_output = gr.Markdown(label="Resume Analysis")
with gr.Tab("List of Candidates"):
feedback_output = gr.Dataframe(
label="Candidate Feedback Lists", interactive=False
)
save_button = gr.Button("Save Report")
save_button.click(
fn=self.save_report,
inputs=[],
outputs=gr.File(label="Download Report"),
)
# Connect the button to the function
submit_button.click(
fn=process_submission,
inputs=[video_input, resume_input, question_input, requirements_input],
outputs=[
transcript_output,
emotion_output,
resume_output,
feedback_output,
],
)
return demo
def launch_app():
print(gr.__version__)
app = GradioInterface()
interface = app.create_interface()
interface.launch()
if __name__ == "__main__":
launch_app()
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