Update app.py

app.py

@@ -1,5 +1,4 @@
-# Updated full code with safe feedback handling
-
+# imports
 import gradio as gr
 from huggingface_hub import InferenceClient
 import re
@@ -7,7 +6,7 @@ import random
 import whisper
 from pydub import AudioSegment
 
-# Load and parse the knowledge file
+# uploading and cleaning the knowledge txt file
 def load_questions(file_path):
     with open(file_path, 'r') as f:
         data = f.read()
@@ -24,6 +23,7 @@ def load_questions(file_path):
 
 all_questions = load_questions('knowledge.txt')
 
+# creating the questions based on each interview
 questions_by_type = {
     'Technical': [q for q in all_questions if any(keyword in q['question'].lower() for keyword in [
         'function', 'linked list', 'url', 'rest', 'graphql', 'garbage', 'cap theorem', 'sql', 'hash table',
@@ -36,28 +36,33 @@ questions_by_type = {
         "testing", "financial", "automation", "analysis", "regression", "business", "stakeholder"])] 
 }
 
+# models
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 whisper_model = whisper.load_model("base")
 
+# whisper audio-to-text function
 def transcribe_audio(file_path):
     try:
-        print(f"\U0001F4C2 Processing audio: {file_path}")
+        print(f"📂 Processing audio: {file_path}")
         audio = AudioSegment.from_file(file_path)
         converted_path = "converted.wav"
         audio.export(converted_path, format="wav")
         result = whisper_model.transcribe(converted_path, fp16=False)
         return result["text"]
     except Exception as e:
-        return f"\u274C ERROR: {str(e)}"
+        return f"❌ ERROR: {str(e)}"
 
+# setting up the users profile (step 1)
 def set_type(choice, user_profile):
     user_profile["interview_type"] = choice
     return "Great! What’s your background and what field/role are you aiming for?", user_profile
 
+# step 2
 def save_background(info, user_profile):
     user_profile["field"] = info
     return "Awesome! Type 'start' below to begin your interview.", user_profile
 
+# step 3
 def respond(message, chat_history, user_profile):
     message_lower = message.strip().lower()
 
@@ -66,6 +71,7 @@ def respond(message, chat_history, user_profile):
         chat_history.append((message, bot_msg))
         return chat_history
 
+    # interview process
     if message_lower == 'start':
         interview_type = user_profile['interview_type']
         selected_questions = questions_by_type.get(interview_type, [])
@@ -109,6 +115,7 @@ def respond(message, chat_history, user_profile):
         chat_history.append((message, feedback))
         return chat_history
 
+    # starting the chatbot
     messages = [{"role": "system", "content": f"You are a professional interviewer conducting a {user_profile['interview_type']} interview for a candidate in {user_profile['field']}."}]
     for q, a in chat_history:
         messages.append({"role": "user", "content": q})
@@ -124,29 +131,17 @@ def generate_feedback(user_profile):
     feedback = []
     questions = user_profile.get('questions', [])
     answers = user_profile.get('user_answers', [])
-
-    if not questions or not answers:
-        return "⚠️ Feedback unavailable: Make sure you've completed the interview first."
-
     for i, user_ans in enumerate(answers):
-        if i >= len(questions):
-            feedback.append(f"⚠️ No matching question for answer {i+1}.")
-            continue
-
-        correct_answers = questions[i].get('answers', [])
-        if not correct_answers:
-            feedback.append(f"❌ No expected answers listed for question {i+1}.")
-            continue
-
+        correct_answers = questions[i]['answers']
         match = any(ans.lower() in user_ans.lower() for ans in correct_answers)
         if match:
             fb = f"Question {i+1}: ✅ Good job!"
         else:
             fb = f"Question {i+1}: ❌ Missed some key points: {correct_answers[0]}"
         feedback.append(fb)
-
     return "\n".join(feedback)
 
+# handle audio input
 def handle_audio(audio_file, chat_history, user_profile):
     transcribed = transcribe_audio(audio_file)
     if transcribed.startswith("❌"):
@@ -154,6 +149,7 @@ def handle_audio(audio_file, chat_history, user_profile):
         return chat_history
     return respond(transcribed, chat_history, user_profile)
 
+# creating the visual elements
 with gr.Blocks() as demo:
     user_profile = gr.State({"interview_type": "", "field": "", "interview_in_progress": False})
     chat_history = gr.State([])
@@ -191,8 +187,10 @@ with gr.Blocks() as demo:
         send_btn = gr.Button("Send Text")
         audio_btn = gr.Button("Send Audio")
 
+
     send_btn.click(respond, inputs=[msg, chat_history, user_profile], outputs=[chatbot], queue=False)
     send_btn.click(lambda: "", None, msg, queue=False)
     audio_btn.click(handle_audio, inputs=[audio_input, chat_history, user_profile], outputs=[chatbot], queue=False)
 
-    demo.launch()
+
+demo.launch()