Feat: Integrate Gradio frontend

Added initial frontend development

src/app.py

@@ -1,5 +1,6 @@
 import gradio as gr
 from typing import Dict
+import pandas as pd
 
 # from src.application.services import InterviewAnalyzer
 # from src.infrastructure.llm import LangchainService
@@ -46,27 +47,156 @@ from typing import Dict
 
 # Testing to setup the simple interface
 class GradioInterface:
-    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",
+    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
         )
 
-        return interface
+    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():