Update app.py

app.py

@@ -1,9 +1,10 @@
-# ✅ FINAL MERGED & ENHANCED CODE: AI Interview Bot with Gemini, Jason/Erica, One-by-One Flow
+# ✅ FINAL MERGED & ENHANCED CODE: AI Interview Bot (Jason Voice, One-by-One, Stylish UI)
 
 import os
 import gradio as gr
 import whisper
 import tempfile
+import re
 from dotenv import load_dotenv
 from TTS.api import TTS
 import google.generativeai as genai
@@ -18,50 +19,42 @@ model = genai.GenerativeModel("gemini-1.5-flash-latest")
 # ✅ LOAD MODELS
 # -----------------------------
 asr_model = whisper.load_model("base")
-tts_model = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=False, gpu=False)
+jason_tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=False, gpu=False)
 
 # -----------------------------
 # ✅ GLOBAL STATE
 # -----------------------------
 candidate_name = ""
 field = ""
-selected_voice = "Erica"
 interview_questions = []
 current_question_index = 0
 feedback_summary = []
 
 # -----------------------------
-# ✅ STEP 1: Collect Candidate Info + Interviewer Selection
+# ✅ STEP 1: Collect Candidate Info
 # -----------------------------
-def save_user_info(name, user_field, voice):
-    global candidate_name, field, selected_voice
+def save_user_info(name, user_field):
+    global candidate_name, field
     candidate_name = name
     field = user_field
-    selected_voice = voice
-    greeting = f"Hi {name}! Your interviewer will be {voice}. Preparing questions for a {field} internship."
+    greeting = f"Welcome {name}! Jason will conduct your mock interview for the {field} internship."
     return greeting
 
 # -----------------------------
-# ✅ STEP 2: Generate Questions from Gemini
+# ✅ STEP 2: Generate Questions with Strict Format
 # -----------------------------
 def generate_questions():
     global field, interview_questions, current_question_index, feedback_summary
-    import re
     current_question_index = 0
     feedback_summary = []
 
     prompt = (
-        f"You are a professional interview panel. Generate exactly 12 interview questions "
-        f"(7 technical and 5 behavioral) for an internship interview in the field of {field}.\n"
-        f"Return the output in the following strict format:\n"
-        f"1. <question one>\n2. <question two>\n... up to 12. <question twelve>\n"
-        f"No explanations or headings. Just the 12 numbered questions."
+        f"You are a professional interviewer. Generate exactly 12 internship questions (7 technical + 5 behavioral) for the field of {field}.\n"
+        f"Output only the questions, each starting with a number (e.g., 1. ..., 2. ..., ..., 12.). Do not include headings or explanations."
     )
     try:
         response = model.generate_content(prompt)
         raw_output = response.text.strip()
-
-        # ✅ Extract lines that start with "1." to "12."
         lines = re.findall(r"^\d+\.\s+.*", raw_output, re.MULTILINE)
         interview_questions = [line.strip() for line in lines]
 
@@ -72,9 +65,8 @@ def generate_questions():
     except Exception as e:
         return f"Error: {str(e)}", ""
 
-
 # -----------------------------
-# ✅ STEP 3: Speak Current Question
+# ✅ STEP 3: Jason Speaks Current Question
 # -----------------------------
 def speak_current_question():
     if not interview_questions or current_question_index >= len(interview_questions):
@@ -82,18 +74,16 @@ def speak_current_question():
 
     question = interview_questions[current_question_index]
     temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
-    tts_model.tts_to_file(text=question, file_path=temp_file.name)
+    jason_tts.tts_to_file(text=question, file_path=temp_file.name)
     return question, temp_file.name
 
 # -----------------------------
-# ✅ STEP 4: Record Answer → Transcribe → Store (Analysis Later)
+# ✅ STEP 4: Record Answer & Store for Analysis
 # -----------------------------
 def record_answer(audio_file):
     global current_question_index
-
     if not interview_questions:
-        return "❌ Please generate questions first.", ""
-
+        return "❌ Generate questions first.", ""
     if audio_file is None:
         return "❌ Please record your answer.", ""
 
@@ -101,22 +91,16 @@ def record_answer(audio_file):
         result = asr_model.transcribe(audio_file)
         transcript = result["text"]
         question = interview_questions[current_question_index]
-
-        feedback_summary.append({
-            "question": question,
-            "answer": transcript,
-            "feedback": None  # Will be filled later
-        })
+        feedback_summary.append({"question": question, "answer": transcript, "feedback": None})
 
         current_question_index += 1
-        next_q = interview_questions[current_question_index] if current_question_index < len(interview_questions) else "✅ End of questions. Please click SUBMIT for feedback."
-
+        next_q = interview_questions[current_question_index] if current_question_index < len(interview_questions) else "✅ Interview complete. Click SUBMIT for feedback."
         return f"✅ Answer recorded for Q{current_question_index}.", next_q
     except Exception as e:
         return f"Error: {str(e)}", ""
 
 # -----------------------------
-# ✅ STEP 5: Submit & Analyze All Answers via Gemini
+# ✅ STEP 5: Analyze All Answers via Gemini
 # -----------------------------
 def analyze_all():
     if not feedback_summary:
@@ -127,11 +111,10 @@ def analyze_all():
         prompt = (
             f"Interview Question: {item['question']}\n"
             f"Candidate's Answer: {item['answer']}\n"
-            "You are an experienced interview evaluator. Analyze the answer:\n"
-            "- Overall Rating (Weak to Excellent)\n"
-            "- Score out of 100\n"
-            "- Suggestions\n\n"
-            "Format:\nOverall Rating: <...>\nScore: <...>\nSuggestions: <...>"
+            "Evaluate the answer:\n"
+            "- Overall Rating: Weak/Average/Good/Excellent\n"
+            "- Score: out of 100\n"
+            "- Suggestions:"
         )
         try:
             response = model.generate_content(prompt)
@@ -139,46 +122,43 @@ def analyze_all():
             item['feedback'] = feedback
             final_summary += f"Q{i}: {item['question']}\nAnswer: {item['answer']}\n{feedback}\n{'-'*50}\n"
         except Exception as e:
-            final_summary += f"Q{i}: {item['question']}\nAnswer: {item['answer']}\nFeedback Error: {str(e)}\n{'-'*50}\n"
+            final_summary += f"Q{i}: {item['question']}\nAnswer: {item['answer']}\nError: {str(e)}\n{'-'*50}\n"
     return final_summary
 
 # -----------------------------
-# ✅ GRADIO UI (Visually Structured)
+# ✅ GRADIO UI (Modern Look)
 # -----------------------------
-with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="violet")) as demo:
     gr.Markdown("""
-    # 🧠 AI Mock Interview Bot
-    👋 Welcome! Select your field, interviewer, and get ready for your personalized mock interview experience powered by Gemini.
+    <div style='text-align: center; font-size: 28px; font-weight: bold;'>🎙️ AI Mock Interview Bot</div>
+    <div style='text-align: center; font-size: 16px;'>Choose your field and get started. Jason will guide you through your interview.</div>
     """)
 
     with gr.Row():
-        name = gr.Textbox(label="Your Name")
-        field = gr.Textbox(label="Interview Field (e.g. Sales, Software, HR)")
-        voice = gr.Radio(choices=["Jason", "Erica"], label="Select Interviewer Voice", value="Erica")
-        start_btn = gr.Button("✅ Start Interview")
-    greet = gr.Textbox(label="Greeting", interactive=False)
-
-    start_btn.click(save_user_info, inputs=[name, field, voice], outputs=greet)
+        name = gr.Textbox(label="👤 Your Name")
+        field = gr.Textbox(label="🎯 Interview Field (e.g. Software, Marketing, HR)")
+        start_btn = gr.Button("🚀 Begin Interview")
+    greet = gr.Textbox(label="Welcome Message", interactive=False)
+    start_btn.click(save_user_info, inputs=[name, field], outputs=greet)
 
     with gr.Row():
-        gen_btn = gr.Button("🎯 Generate Questions")
+        gen_btn = gr.Button("📄 Generate Questions")
         status = gr.Textbox(label="Status")
-        current_q = gr.Textbox(label="Current Question", lines=2)
-
+        current_q = gr.Textbox(label="📌 Current Question", lines=2)
     gen_btn.click(generate_questions, outputs=[status, current_q])
 
-    speak = gr.Button("🔊 Hear Question")
-    audio = gr.Audio(label="Audio Output")
+    speak = gr.Button("🔊 Hear Question", elem_classes="speak-btn")
+    audio = gr.Audio(label="🔈 Jason's Voice")
     speak.click(fn=speak_current_question, outputs=[current_q, audio])
 
-    record = gr.Audio(sources=["microphone"], type="filepath", label="🎙️ Record Your Answer")
-    submit_ans = gr.Button("✔️ Submit Answer")
-    rec_status = gr.Textbox(label="Recording Status")
+    record = gr.Audio(sources=["microphone"], type="filepath", label="🎤 Record Your Answer")
+    submit_ans = gr.Button("✅ Submit This Answer")
+    rec_status = gr.Textbox(label="Answer Status")
     next_q = gr.Textbox(label="Next Question")
     submit_ans.click(fn=record_answer, inputs=record, outputs=[rec_status, current_q])
 
-    submit_all = gr.Button("📊 Finish Interview & Analyze")
-    analysis = gr.Textbox(label="🧾 Final Feedback", lines=20)
+    submit_all = gr.Button("📊 Submit All & Get Feedback")
+    analysis = gr.Textbox(label="📋 Final Feedback Summary", lines=20)
     submit_all.click(fn=analyze_all, outputs=analysis)
 
 # -----------------------------