Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 import re
 
-# ---- Load and parse questions ----
+# ---- Load and parse questions from knowledge.txt ----
 def load_questions(file_path):
     with open(file_path, 'r') as f:
         data = f.read()
@@ -20,109 +20,116 @@ def load_questions(file_path):
 
 all_questions = load_questions('knowledge.txt')
 
-# ---- Categorize questions ----
+# ---- Simple way to assign questions to interview types ----
+# You can replace this later with better tagging
 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',
-        'stack', 'queue', 'recursion', 'reverse', 'bfs', 'dfs', 'time complexity', 'binary search tree',
-        'web application', 'chat system', 'load balancing', 'caching', 'normalization', 'acid', 'indexing',
-        'sql injection', 'https', 'xss', 'hash', 'vulnerabilities'])],
-
+    'function', 'linked list', 'url', 'rest', 'graphql', 'garbage', 'cap theorem', 'sql', 'hash table',
+    'stack', 'queue', 'recursion', 'reverse', 'bfs', 'dfs', 'time complexity', 'binary search tree',
+    'web application', 'chat system', 'load balancing', 'caching', 'normalization', 'acid', 'indexing',
+    'sql injection', 'https', 'xss', 'hash', 'vulnerabilities'])],
+    
     'Competency-Based Interview': [q for q in all_questions if any(keyword in q['question'].lower() for keyword in [
-        "Debugging","Learning Fast","Deadlines","Teamwork","Leadership","Mistake Recovery","Conflict Management","Decision Making"])],
-
+    "Debugging",
+    "Learning Fast",
+    "Deadlines",
+    "Teamwork",
+    "Leadership",
+    "Mistake Recovery",
+    "Conflict Management",
+    "Decision Making"])],
+    
     'Case': [q for q in all_questions if any(keyword in q['question'].lower() for keyword in [
-        "A/B Testing","Financial Modeling","Automation","Data Analysis","Regression","Business Opportunity","Stakeholder Alignment"])]
+    "A/B Testing",
+    "Financial Modeling",
+    "Automation",
+    "Data Analysis",
+    "Regression",
+    "Business Opportunity",
+    "Stakeholder Alignment"])] 
 }
 
-# ---- HF Client ----
+
+# ---- Hugging Face Client ----
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 # ---- Interview type selection ----
 def set_type(choice, user_profile):
-    user_profile.update({
-        "interview_type": choice,
-        "state": "idle",
-        "questions": [],
-        "current_q": 0,
-        "user_answers": [],
-        "field": ""
-    })
+    user_profile["interview_type"] = choice
     return "Great! What’s your background and what field/role are you aiming for?", user_profile
 
-# ---- Background ----
+# ---- Save background ----
 def save_background(info, user_profile):
     user_profile["field"] = info
-    return "Awesome! Type 'start' below when you're ready to begin your interview.", user_profile
+    return "Awesome! Type 'start' below to begin your interview.", user_profile
 
-# ---- Main Respond Logic ----
+# ---- Main respond logic ----
 def respond(message, chat_history, user_profile):
-    msg = message.strip().lower()
-    state = user_profile.get("state", "idle")
-
-    # Safety: ensure setup
     if not user_profile.get("interview_type") or not user_profile.get("field"):
-        chat_history.append((message, "Please finish steps 1 and 2 first."))
+        bot_msg = "Please finish steps 1 and 2 before starting the interview."
+        chat_history.append((message, bot_msg))
         return chat_history
 
-    # START command
-    if msg == "start" and state == "idle":
-        selected_questions = questions_by_type.get(user_profile['interview_type'], [])
-
+    # Start interview logic
+    if message.strip().lower() == 'start':
+        interview_type = user_profile['interview_type']
+        selected_questions = questions_by_type.get(interview_type, [])
+        user_profile['questions'] = selected_questions
+        user_profile['current_q'] = 0
+        user_profile['user_answers'] = []
         if not selected_questions:
-            chat_history.append((message, "No questions found for this interview type."))
+            bot_msg = "No questions available for this interview type."
+        else:
+            bot_msg = f"First question: {selected_questions[0]['question']}"
+        chat_history.append((message, bot_msg))
+        return chat_history
+
+    # If interview is ongoing
+    # if user_profile.get("questions"):
+    #     q_index = user_profile['current_q']
+    #     user_profile['user_answers'].append(message)
+
+    #     q_index += 1
+    #     user_profile['current_q'] = q_index
+
+    #     if q_index < len(user_profile['questions']):
+    #         bot_msg = f"Next question: {user_profile['questions'][q_index]['question']}"
+    #     else:
+    #         bot_msg = "Interview complete! Type 'feedback' if you'd like me to analyze your answers."
+    #     chat_history.append((message, bot_msg))
+    #     return chat_history
+    if user_profile.get("questions"):
+
+        # --- NEW STOP LOGIC ---
+        if message.strip().lower() == 'stop':
+            bot_msg = "Thank you for chatting with Intervu! The interview has been stopped. Type 'feedback' if you'd like me to analyze your answers."
+            chat_history.append((message, bot_msg))
+            user_profile['questions'] = []  # clear questions list to stop
             return chat_history
 
-        user_profile["questions"] = selected_questions
-        user_profile["current_q"] = 0
-        user_profile["user_answers"] = []
-        user_profile["state"] = "intro"
+        # Existing interview logic continues here:
+        q_index = user_profile['current_q']
+        user_profile['user_answers'].append(message)
 
-        intro_msg = f"Great, let's begin your {user_profile['interview_type']} interview for the {user_profile['field']} role.\nI'll be asking you several questions to assess your knowledge. Feel free to take your time answering."
-        chat_history.append((message, intro_msg))
-        chat_history.append(("", "Type 'next' when you're ready to begin."))
-        return chat_history
+        q_index += 1
+        user_profile['current_q'] = q_index
 
-    # INTRO → waiting for user to say "next"
-    if msg == "next" and state == "intro":
-        user_profile["state"] = "interviewing"
-        first_question = user_profile['questions'][0]['question']
-        chat_history.append((message, f"First question: {first_question}"))
-        return chat_history
+        if q_index < len(user_profile['questions']):
+            bot_msg = f"Next question: {user_profile['questions'][q_index]['question']}"
+        else:
+            bot_msg = "Interview complete! Type 'feedback' if you'd like me to analyze your answers."
 
-    # STOP anytime during interviewing
-    if msg == "stop" and state in ["intro", "interviewing"]:
-        user_profile["state"] = "stopped"
-        chat_history.append((message, "Interview stopped. You may type 'start' to begin again."))
+        chat_history.append((message, bot_msg))
         return chat_history
+    
 
-    # FEEDBACK command
-    if msg == "feedback":
-        if state != "completed":
-            chat_history.append((message, "Feedback is only available after completing the interview."))
-            return chat_history
+    # Handle feedback request
+    if message.strip().lower() == 'feedback':
         feedback = generate_feedback(user_profile)
         chat_history.append((message, feedback))
         return chat_history
 
-    # INTERVIEWING STATE LOGIC
-    if state == "interviewing":
-        q_index = user_profile["current_q"]
-        questions = user_profile["questions"]
-
-        if q_index < len(questions):
-            user_profile["user_answers"].append(message)
-            user_profile["current_q"] += 1
-
-            if user_profile["current_q"] < len(questions):
-                next_q = questions[user_profile["current_q"]]["question"]
-                chat_history.append((message, f"Next question: {next_q}"))
-            else:
-                user_profile["state"] = "completed"
-                chat_history.append((message, "✅ Interview complete! Type 'feedback' if you'd like a performance analysis."))
-            return chat_history
-
-    # ANY OTHER MESSAGE (idle, stopped, etc) → small talk fallback using LLM
+    # Default fallback
     messages = [
         {"role": "system", "content": f"You are a professional interviewer conducting a {user_profile['interview_type']} interview for a candidate in the {user_profile['field']} field."}
     ]
@@ -136,18 +143,13 @@ def respond(message, chat_history, user_profile):
     chat_history.append((message, bot_msg))
     return chat_history
 
-# ---- Feedback ----
+# ---- Simple feedback function (keyword based for now) ----
 def generate_feedback(user_profile):
     feedback = []
     questions = user_profile.get('questions', [])
     answers = user_profile.get('user_answers', [])
 
-    num_questions = min(len(questions), len(answers))
-    if num_questions == 0:
-        return "No completed interview found."
-
-    for i in range(num_questions):
-        user_ans = answers[i]
+    for i, user_ans in enumerate(answers):
         correct_answers = questions[i]['answers']
         match = any(ans.lower() in user_ans.lower() for ans in correct_answers)
         if match:
@@ -157,21 +159,19 @@ def generate_feedback(user_profile):
         feedback.append(fb)
     return "\n".join(feedback)
 
-# ---- Gradio UI ----
+# ---- Gradio Interface ----
 with gr.Blocks() as demo:
-    user_profile = gr.State({
-        "interview_type": "", "field": "", "state": "idle",
-        "questions": [], "current_q": 0, "user_answers": []
-    })
+    user_profile = gr.State({"interview_type": "", "field": ""})
     chat_history = gr.State([])
 
     gr.Markdown("# 🎤 Welcome to Intervu")
 
     gr.Markdown("### Step 1: Choose Interview Type")
     with gr.Row():
-        btn1 = gr.Button("Technical")
-        btn2 = gr.Button("Competency-Based Interview")
-        btn3 = gr.Button("Case")
+        with gr.Column():
+            btn1 = gr.Button("Technical")
+            btn2 = gr.Button("Competency-Based Interview")
+            btn3 = gr.Button("Case")
         type_output = gr.Textbox(label="Bot response", interactive=False)
 
     btn1.click(set_type, inputs=[gr.Textbox(value="Technical", visible=False), user_profile], outputs=[type_output, user_profile])