Update app.py

app.py

@@ -20,6 +20,11 @@ def parse_resume(pdf):
     sections = {"Resume Content": text}
     return sections
 
+# Process job description text
+def process_job_description(job_desc):
+    """Encode the job description for analysis."""
+    return embedding_model.encode(job_desc)
+
 # Process resume and generate embeddings
 def process_resume(pdf):
     resume_content = parse_resume(pdf)
@@ -29,13 +34,13 @@ def process_resume(pdf):
     return resume_embeddings
 
 # Generate a conversation response
-def generate_conversation_response(user_input):
-    prompt = f"The user said: {user_input}. Respond appropriately as a recruiter."
+def generate_conversation_response(user_input, job_desc_embedding):
+    prompt = f"The user said: {user_input}. Respond appropriately as a professional hiring manager. Focus on how the response relates to the job description."
     response = conversation_model(prompt, max_length=100, num_return_sequences=1)
     return response[0]["generated_text"]
 
 # Generate question from user response
-def generate_question(user_input, resume_embeddings):
+def generate_question(user_input, resume_embeddings, job_desc_embedding):
     """Find the most relevant section in the resume and generate a question."""
     user_embedding = embedding_model.encode(user_input)
     similarities = {
@@ -46,19 +51,63 @@ def generate_question(user_input, resume_embeddings):
     return f"Based on your experience in {most_relevant_section}, can you elaborate more?"
 
 # Gradio interface
-def mock_interview(audio, pdf):
-    resume_embeddings = process_resume(pdf)
-    transcription = stt_model(audio["name"])  # Using Gradio's audio file object
-    question = generate_question(transcription["text"], resume_embeddings)
-    return transcription["text"], question
-
-interface = gr.Interface(
-    fn=mock_interview,
-    inputs=[gr.Audio(type="filepath"), gr.File(label="Upload Resume (PDF)")],
-    outputs=["text", "text"],
-    title="Mock Interview AI",
-    description="Upload your resume and answer questions in a mock interview."
-)
+class MockInterview:
+    def __init__(self):
+        self.resume_embeddings = None
+        self.job_desc_embedding = None
+        self.interview_active = False
+
+    def upload_inputs(self, resume, job_desc):
+        self.resume_embeddings = process_resume(resume)
+        self.job_desc_embedding = process_job_description(job_desc)
+        self.interview_active = True
+        return "Resume and job description processed. Interview is ready to begin."
+
+    def conduct_interview(self, audio):
+        if not self.interview_active:
+            return "Please upload your resume and job description first.", ""
+
+        transcription = stt_model(audio)["text"]  # Transcribe audio
+        question = generate_question(transcription, self.resume_embeddings, self.job_desc_embedding)
+        return transcription, question
+
+    def end_interview(self):
+        self.interview_active = False
+        return "Interview ended. Thank you for participating."
+
+mock_interview = MockInterview()
+
+def upload_inputs(resume, job_desc):
+    return mock_interview.upload_inputs(resume, job_desc)
+
+def conduct_interview(audio):
+    return mock_interview.conduct_interview(audio)
+
+def end_interview():
+    return mock_interview.end_interview()
+
+interface = gr.Blocks()
+with interface:
+    gr.Markdown("""# Mock Interview AI
+Upload your resume and job description, then engage in a realistic interview simulation.""")
+
+    with gr.Row():
+        resume_input = gr.File(label="Upload Resume (PDF)")
+        job_desc_input = gr.Textbox(label="Paste Job Description")
+        upload_button = gr.Button("Upload")
+
+    with gr.Row():
+        audio_input = gr.Audio(type="filepath", label="Speak Your Answer")
+        submit_button = gr.Button("Submit Response")
+        end_button = gr.Button("End Interview")
+
+    with gr.Row():
+        transcription_output = gr.Textbox(label="Transcription")
+        question_output = gr.Textbox(label="Question")
+
+    upload_button.click(upload_inputs, inputs=[resume_input, job_desc_input], outputs=[transcription_output])
+    submit_button.click(conduct_interview, inputs=[audio_input], outputs=[transcription_output, question_output])
+    end_button.click(end_interview, outputs=[transcription_output])
 
 if __name__ == "__main__":
     interface.launch()