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Update app.py
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app.py
CHANGED
@@ -4,6 +4,8 @@ from transformers import pipeline
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from sentence_transformers import SentenceTransformer
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from sklearn.metrics.pairwise import cosine_similarity
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import PyPDF2
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# Load local models for inference
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stt_model = pipeline("automatic-speech-recognition", model="openai/whisper-base")
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@@ -12,6 +14,14 @@ conversation_model = pipeline("text-generation", model="facebook/blenderbot-400M
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# Load a pre-trained model for vector embeddings
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embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
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# Parse PDF and create resume content
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def parse_resume(pdf):
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"""Extract text from an uploaded PDF file."""
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@@ -55,21 +65,32 @@ class MockInterview:
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self.resume_embeddings = process_resume(resume)
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self.job_desc_embedding = process_job_description(job_desc)
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self.interview_active = True
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return "Resume and job description processed.
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def conduct_interview(self, audio_file):
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if not self.interview_active:
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return "Please upload your resume and job description first.", ""
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if not transcription.strip():
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return "No audio detected. Please try again.", ""
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question = generate_question(transcription, self.resume_embeddings)
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return transcription, question
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def end_interview(self):
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self.interview_active = False
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return "Interview ended. Thank you for participating."
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mock_interview = MockInterview()
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@@ -77,6 +98,9 @@ mock_interview = MockInterview()
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def upload_inputs(resume, job_desc):
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return mock_interview.upload_inputs(resume, job_desc)
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def conduct_interview(audio_file):
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return mock_interview.conduct_interview(audio_file)
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@@ -86,7 +110,7 @@ def end_interview():
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interface = gr.Blocks()
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with interface:
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gr.Markdown("""# Mock Interview AI
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Upload your resume and job description, then engage in a realistic interview simulation.""")
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with gr.Row():
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resume_input = gr.File(label="Upload Resume (PDF)")
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@@ -100,9 +124,9 @@ Upload your resume and job description, then engage in a realistic interview sim
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submit_button = gr.Button("Submit Response")
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end_button = gr.Button("End Interview")
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upload_button.click(upload_inputs, inputs=[resume_input, job_desc_input], outputs=[
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submit_button.click(conduct_interview, inputs=[audio_input], outputs=[transcription_output, question_output])
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end_button.click(end_interview, outputs=[
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if __name__ == "__main__":
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interface.launch()
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from sentence_transformers import SentenceTransformer
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from sklearn.metrics.pairwise import cosine_similarity
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import PyPDF2
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import pyttsx3
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import time
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# Load local models for inference
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stt_model = pipeline("automatic-speech-recognition", model="openai/whisper-base")
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# Load a pre-trained model for vector embeddings
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embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
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# Text-to-Speech engine setup
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tts_engine = pyttsx3.init()
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def speak_text(text):
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"""Speak the given text using TTS engine."""
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tts_engine.say(text)
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tts_engine.runAndWait()
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# Parse PDF and create resume content
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def parse_resume(pdf):
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"""Extract text from an uploaded PDF file."""
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self.resume_embeddings = process_resume(resume)
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self.job_desc_embedding = process_job_description(job_desc)
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self.interview_active = True
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return "Resume and job description processed. Starting the interview."
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def start_interview(self):
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if not self.interview_active:
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return "Please upload your resume and job description first."
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question = "Tell me about yourself."
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speak_text(question)
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return question
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def conduct_interview(self, audio_file):
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if not self.interview_active:
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return "Please upload your resume and job description first.", ""
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# Transcribe audio
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transcription = stt_model(audio_file)["text"]
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if not transcription.strip():
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return "No audio detected. Please try again.", ""
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# Generate next question
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question = generate_question(transcription, self.resume_embeddings)
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speak_text(question)
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return transcription, question
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def end_interview(self):
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self.interview_active = False
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speak_text("Thank you for participating in the interview. Goodbye!")
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return "Interview ended. Thank you for participating."
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mock_interview = MockInterview()
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def upload_inputs(resume, job_desc):
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return mock_interview.upload_inputs(resume, job_desc)
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def start_interview():
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return mock_interview.start_interview()
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def conduct_interview(audio_file):
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return mock_interview.conduct_interview(audio_file)
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interface = gr.Blocks()
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with interface:
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gr.Markdown("""# Mock Interview AI
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Upload your resume and job description, then engage in a realistic audio-based interview simulation.""")
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with gr.Row():
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resume_input = gr.File(label="Upload Resume (PDF)")
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submit_button = gr.Button("Submit Response")
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end_button = gr.Button("End Interview")
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upload_button.click(upload_inputs, inputs=[resume_input, job_desc_input], outputs=[question_output])
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submit_button.click(conduct_interview, inputs=[audio_input], outputs=[transcription_output, question_output])
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end_button.click(end_interview, outputs=[question_output])
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if __name__ == "__main__":
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interface.launch()
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