Update app.py

app.py

@@ -774,41 +774,7 @@ A:"""
         logging.error(f"Error generating reference answer: {e}", exc_info=True)
         return "Unable to generate reference answer due to an error"
 
-def interpret_confidence(voice_label, face_label, answer_score_label,k=0.2):
-    # Map expressions to rough numerical confidence levels
-    emotion_map = {
-        "happy": 0.9, "neutral": 0.6, "surprised": 0.7, "sad": 0.4,
-        "angry": 0.3, "disgust": 0.2, "fear": 0.3,
-    }
-
-    answer_score_map = {
-        "excellent": 1.0,
-        "good": 0.8,
-        "medium": 0.6,
-        "poor": 0.3
-    }
 
-    voice_score = emotion_map.get(voice_label, 0.5)
-    face_score = emotion_map.get(face_label, 0.5)
-    answer_score = answer_score_map.get(answer_score_label, 0.5)
-    
-    # Adjust weights as needed (emotions may be less reliable than verbal answers)
-    avg_emotion = (voice_score + face_score) /2
-    control_bonus = max(0,answer_score - avg_emotion) *k
-    effective_confidence = (
-        0.5 * answer_score +
-        0.22 * voice_score +
-        0.18 * face_score +
-        0.1 *control_bonus
-    )
-    
-    return {
-    "effective_confidence": round(effective_confidence, 3),
-    "answer_score": round(answer_score, 2),
-    "voice_score": round(voice_score, 2),
-    "face_score": round(face_score, 2),
-    "control_bonus": round(control_bonus, 3)
-}
 
 def build_interview_prompt(conversation_history, user_response, context, job_role, skills, seniority, 
                            difficulty_adjustment=None, voice_label=None, face_label=None, effective_confidence=None):
@@ -857,8 +823,6 @@ def build_interview_prompt(conversation_history, user_response, context, job_rol
     {response_evaluation}
 
     Voice Tone: {voice_label}
-    Facial Expression: {face_label}
-    Estimated Confidence Score: {effective_confidence}
 
     ---
     ---
@@ -931,8 +895,6 @@ def build_interview_prompt(conversation_history, user_response, context, job_rol
         user_response=user_response,
         response_evaluation=response_evaluation.strip(),
         voice_label=voice_label or "unknown",
-        face_label=face_label or "unknown",
-        effective_confidence=effective_confidence if effective_confidence is not None else "N/A"
     )
 
     return prompt
@@ -1451,28 +1413,338 @@ def extract_candidate_details(file_path):
         "skills": skills
     }
 
+# import gradio as gr
+# import time
+# import tempfile
+# import numpy as np
+# import scipy.io.wavfile as wavfile
+# import os
+# import json
+# from transformers import BarkModel, AutoProcessor
+# import torch, gc
+# import whisper
+# from transformers import Wav2Vec2Processor, Wav2Vec2ForSequenceClassification
+# import librosa
+
+# import torch
+# print(torch.cuda.is_available())  # ✅ Tells you if GPU is available
+# torch.cuda.empty_cache()
+# gc.collect()
+
+
+# # Bark TTS
+# print("🔁 Loading Bark model...")
+# model_bark = BarkModel.from_pretrained("suno/bark").to("cuda" if torch.cuda.is_available() else "cpu")
+# print("✅ Bark model loaded")
+# print("🔁 Loading Bark processor...")
+# processor_bark = AutoProcessor.from_pretrained("suno/bark")
+# print("✅ Bark processor loaded")
+# bark_voice_preset = "v2/en_speaker_5"
+
+# def bark_tts(text):
+#     print(f"🔁 Synthesizing TTS for: {text}")
+    
+#     # Process the text
+#     inputs = processor_bark(text, return_tensors="pt", voice_preset=bark_voice_preset)
+    
+#     # Move tensors to device
+#     input_ids = inputs["input_ids"].to(model_bark.device)
+    
+#     start = time.time()
+    
+#     # Generate speech with only the required parameters
+#     with torch.no_grad():
+#         speech_values = model_bark.generate(
+#             input_ids=input_ids,
+#             do_sample=True,
+#             fine_temperature=0.4,
+#             coarse_temperature=0.8
+#         )
+    
+#     print(f"✅ Bark finished in {round(time.time() - start, 2)}s")
+    
+#     # Convert to audio
+#     speech = speech_values.cpu().numpy().squeeze()
+#     speech = (speech * 32767).astype(np.int16)
+    
+#     temp_wav = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
+#     wavfile.write(temp_wav.name, 22050, speech)
+    
+#     return temp_wav.name
+
+
+
+
+
+# # Whisper STT
+# print("🔁 Loading Whisper model...")
+# whisper_model = whisper.load_model("base", device="cuda")
+# print("✅ Whisper model loaded")
+# def whisper_stt(audio_path):
+#     if not audio_path or not os.path.exists(audio_path): return ""
+#     result = whisper_model.transcribe(audio_path)
+#     return result["text"]
+
+# seniority_mapping = {
+#     "Entry-level": 1, "Junior": 2, "Mid-Level": 3, "Senior": 4, "Lead": 5
+# }
+
+
+# # --- 2. Gradio App ---
+
+# with gr.Blocks(theme=gr.themes.Soft()) as demo:
+#     user_data = gr.State({})
+#     interview_state = gr.State({})
+#     missing_fields_state = gr.State([])
+
+#     # --- UI Layout ---
+#     with gr.Column(visible=True) as user_info_section:
+#         gr.Markdown("## Candidate Information")
+#         cv_file = gr.File(label="Upload CV")
+#         job_desc = gr.Textbox(label="Job Description")
+#         start_btn = gr.Button("Continue", interactive=False)
+
+#     with gr.Column(visible=False) as missing_section:
+#         gr.Markdown("## Missing Information")
+#         name_in = gr.Textbox(label="Name", visible=False)
+#         role_in = gr.Textbox(label="Job Role", visible=False)
+#         seniority_in = gr.Dropdown(list(seniority_mapping.keys()), label="Seniority", visible=False)
+#         skills_in = gr.Textbox(label="Skills", visible=False)
+#         submit_btn = gr.Button("Submit", interactive=False)
+
+#     with gr.Column(visible=False) as interview_pre_section:
+#         pre_interview_greeting_md = gr.Markdown()
+#         start_interview_final_btn = gr.Button("Start Interview")
+
+#     with gr.Column(visible=False) as interview_section:
+#         gr.Markdown("## Interview in Progress")
+#         question_audio = gr.Audio(label="Listen", interactive=False, autoplay=True)
+#         question_text = gr.Markdown()
+#         user_audio_input = gr.Audio(sources=["microphone"], type="filepath", label="1. Record Audio Answer")
+#         stt_transcript = gr.Textbox(label="Transcribed Answer (edit if needed)")
+#         confirm_btn = gr.Button("Confirm Answer")
+#         evaluation_display = gr.Markdown()
+#         interview_summary = gr.Markdown(visible=False)
+
+#     # --- UI Logic ---
+
+#     def validate_start_btn(cv_file, job_desc):
+#         return gr.update(interactive=(cv_file is not None and hasattr(cv_file, "name") and bool(job_desc and job_desc.strip())))
+#     cv_file.change(validate_start_btn, [cv_file, job_desc], start_btn)
+#     job_desc.change(validate_start_btn, [cv_file, job_desc], start_btn)
+
+#     def process_and_route_initial(cv_file, job_desc):
+#         details = extract_candidate_details(cv_file.name)
+#         job_info = extract_job_details(job_desc)
+#         data = {
+#             "name": details.get("name", "unknown"), "job_role": job_info.get("job_title", "unknown"),
+#             "seniority": job_info.get("experience_level", "unknown"), "skills": job_info.get("skills", [])
+#         }
+#         missing = [k for k, v in data.items() if (isinstance(v, str) and v.lower() == "unknown") or not v]
+#         if missing:
+#             return data, missing, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)
+#         else:
+#             greeting = f"Hello {data['name']}, your profile is ready. Click 'Start Interview' when ready."
+#             return data, missing, gr.update(visible=False), gr.update(visible=False), gr.update(visible=True, value=greeting)
+#     start_btn.click(
+#         process_and_route_initial,
+#         [cv_file, job_desc],
+#         [user_data, missing_fields_state, user_info_section, missing_section, pre_interview_greeting_md]
+#     )
+
+#     def show_missing(missing):
+#         if missing is None: missing = []
+#         return gr.update(visible="name" in missing), gr.update(visible="job_role" in missing), gr.update(visible="seniority" in missing), gr.update(visible="skills" in missing)
+#     missing_fields_state.change(show_missing, missing_fields_state, [name_in, role_in, seniority_in, skills_in])
+
+#     def validate_fields(name, role, seniority, skills, missing):
+#         if not missing: return gr.update(interactive=False)
+#         all_filled = all([(not ("name" in missing) or bool(name.strip())), (not ("job_role" in missing) or bool(role.strip())), (not ("seniority" in missing) or bool(seniority)), (not ("skills" in missing) or bool(skills.strip())),])
+#         return gr.update(interactive=all_filled)
+#     for inp in [name_in, role_in, seniority_in, skills_in]:
+#         inp.change(validate_fields, [name_in, role_in, seniority_in, skills_in, missing_fields_state], submit_btn)
+
+#     def complete_manual(data, name, role, seniority, skills):
+#         if data["name"].lower() == "unknown": data["name"] = name
+#         if data["job_role"].lower() == "unknown": data["job_role"] = role
+#         if data["seniority"].lower() == "unknown": data["seniority"] = seniority
+#         if not data["skills"]: data["skills"] = [s.strip() for s in skills.split(",")]
+#         greeting = f"Hello {data['name']}, your profile is ready. Click 'Start Interview' to begin."
+#         return data, gr.update(visible=False), gr.update(visible=True), gr.update(value=greeting)
+#     submit_btn.click(complete_manual, [user_data, name_in, role_in, seniority_in, skills_in], [user_data, missing_section, interview_pre_section, pre_interview_greeting_md])
+
+#     def start_interview(data):
+#         # --- Advanced state with full logging ---
+#         state = {
+#             "questions": [], "answers": [], "face_labels": [], "voice_labels": [], "timings": [],
+#             "question_evaluations": [], "answer_evaluations": [], "effective_confidences": [],
+#             "conversation_history": [],
+#             "difficulty_adjustment": None,
+#             "question_idx": 0, "max_questions": 3, "q_start_time": time.time(),
+#             "log": []
+#         }
+#         # --- Optionally: context retrieval here (currently just blank) ---
+#         context = ""
+#         prompt = build_interview_prompt(
+#             conversation_history=[], user_response="", context=context, job_role=data["job_role"],
+#             skills=data["skills"], seniority=data["seniority"], difficulty_adjustment=None,
+#             voice_label="neutral", face_label="neutral"
+#         )
+#         #here the original one 
+#         # first_q = groq_llm.predict(prompt)
+#         # # Evaluate Q for quality
+#         # q_eval = eval_question_quality(first_q, data["job_role"], data["seniority"], None)
+#         # state["questions"].append(first_q)
+#         # state["question_evaluations"].append(q_eval)
+
+#         #here the testing one
+#         first_q = groq_llm.predict(prompt)
+#         q_eval = {
+#             "Score": "N/A",
+#             "Reasoning": "Skipped to reduce processing time",
+#             "Improvements": []
+#         }
+#         state["questions"].append(first_q)
+#         state["question_evaluations"].append(q_eval)
+
+        
+#         state["conversation_history"].append({'role': 'Interviewer', 'content': first_q})
+#         start = time.perf_counter()
+#         audio_path = bark_tts(first_q)
+#         print("⏱️ Bark TTS took", time.perf_counter() - start, "seconds")
+
+#         # LOG
+#         state["log"].append({"type": "question", "question": first_q, "question_eval": q_eval, "timestamp": time.time()})
+#         return state, gr.update(visible=False), gr.update(visible=True), audio_path, f"*Question 1:* {first_q}"
+#     start_interview_final_btn.click(start_interview, [user_data], [interview_state, interview_pre_section, interview_section, question_audio, question_text])
+
+#     def transcribe(audio_path):
+#         return whisper_stt(audio_path)
+#     user_audio_input.change(transcribe, user_audio_input, stt_transcript)
+
+#     def process_answer(transcript, audio_path, state, data):
+#         if not transcript:
+#             return state, gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update()
+
+#         elapsed = round(time.time() - state.get("q_start_time", time.time()), 2)
+#         state["timings"].append(elapsed)
+#         state["answers"].append(transcript)
+#         state["conversation_history"].append({'role': 'Candidate', 'content': transcript})
+
+#         # --- 1. Emotion analysis (simplified for testing) ---
+#         voice_label = "neutral"
+#         face_label = "neutral"
+#         state["voice_labels"].append(voice_label)
+#         state["face_labels"].append(face_label)
+
+#         # --- 2. Evaluate previous Q and Answer ---
+#         last_q = state["questions"][-1]
+#         q_eval = state["question_evaluations"][-1]  # Already in state
+#         ref_answer = generate_reference_answer(last_q, data["job_role"], data["seniority"])
+#         answer_eval = evaluate_answer(last_q, transcript, ref_answer, data["job_role"], data["seniority"], None)
+#         state["answer_evaluations"].append(answer_eval)
+#         answer_score = answer_eval.get("Score", "medium") if answer_eval else "medium"
+
+#         # --- 3. Adaptive difficulty ---
+#         if answer_score == "excellent":
+#             state["difficulty_adjustment"] = "harder"
+#         elif answer_score in ("medium", "poor"):
+#             state["difficulty_adjustment"] = "easier"
+#         else:
+#             state["difficulty_adjustment"] = None
+
+#         # --- 4. Effective confidence (simplified) ---
+#         eff_conf = {"effective_confidence": 0.6}
+#         state["effective_confidences"].append(eff_conf)
+
+#         # --- LOG ---
+#         state["log"].append({
+#             "type": "answer",
+#             "question": last_q,
+#             "answer": transcript,
+#             "answer_eval": answer_eval,
+#             "ref_answer": ref_answer,
+#             "face_label": face_label,
+#             "voice_label": voice_label,
+#             "effective_confidence": eff_conf,
+#             "timing": elapsed,
+#             "timestamp": time.time()
+#         })
+
+#         # --- Next or End ---
+#         qidx = state["question_idx"] + 1
+#         if qidx >= state["max_questions"]:
+#             # Save as JSON (optionally)
+#             timestamp = time.strftime("%Y%m%d_%H%M%S")
+#             log_file = f"interview_log_{timestamp}.json"
+#             with open(log_file, "w", encoding="utf-8") as f:
+#                 json.dump(state["log"], f, indent=2, ensure_ascii=False)
+#             # Report
+#             summary = "# Interview Summary\n"
+#             for i, q in enumerate(state["questions"]):
+#                 summary += (f"\n### Q{i + 1}: {q}\n"
+#                             f"- *Answer*: {state['answers'][i]}\n"
+#                             f"- *Q Eval*: {state['question_evaluations'][i]}\n"
+#                             f"- *A Eval*: {state['answer_evaluations'][i]}\n"
+#                             f"- *Time*: {state['timings'][i]}s\n")
+#             summary += f"\n\n⏺ Full log saved as {log_file}."
+#             return (state, gr.update(visible=True, value=summary), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(visible=True, value=f"Last Detected — Face: {face_label}, Voice: {voice_label}"))
+#         else:
+#             # --- Build next prompt using adaptive difficulty ---
+#             state["question_idx"] = qidx
+#             state["q_start_time"] = time.time()
+#             context = ""  # You can add your context logic here
+#             prompt = build_interview_prompt(
+#                 conversation_history=state["conversation_history"],
+#                 user_response=transcript,
+#                 context=context,
+#                 job_role=data["job_role"],
+#                 skills=data["skills"],
+#                 seniority=data["seniority"],
+#                 difficulty_adjustment=state["difficulty_adjustment"],
+#                 voice_label=voice_label,
+#             )
+#             next_q = groq_llm.predict(prompt)
+#             # Evaluate Q quality
+#             q_eval = eval_question_quality(next_q, data["job_role"], data["seniority"], None)
+#             state["questions"].append(next_q)
+#             state["question_evaluations"].append(q_eval)
+#             state["conversation_history"].append({'role': 'Interviewer', 'content': next_q})
+#             state["log"].append({"type": "question", "question": next_q, "question_eval": q_eval, "timestamp": time.time()})
+#             audio_path = bark_tts(next_q)
+#             # Display evaluations
+#             eval_md = f"*Last Answer Eval:* {answer_eval}\n\n*Effective Confidence:* {eff_conf}"
+#             return (
+#                 state, gr.update(visible=False), audio_path, f"*Question {qidx + 1}:* {next_q}",
+#                 gr.update(value=None), gr.update(value=None),
+#                 gr.update(visible=True, value=eval_md),
+#             )
+#     # Replace your confirm_btn.click with this:
+#     confirm_btn.click(
+#         process_answer,
+#         [stt_transcript, user_audio_input, interview_state, user_data],  # Added None for video_path
+#         [interview_state, interview_summary, question_audio, question_text, user_audio_input, stt_transcript, evaluation_display]
+#     ).then(
+#         lambda: (gr.update(value=None), gr.update(value=None)), None, [user_audio_input, stt_transcript]
+#     )
+
+# demo.launch(debug=True)
+
 import gradio as gr
 import time
 import tempfile
 import numpy as np
 import scipy.io.wavfile as wavfile
-import cv2
 import os
 import json
-from moviepy.editor import VideoFileClip
-import shutil
 from transformers import BarkModel, AutoProcessor
 import torch, gc
 import whisper
-from transformers import Wav2Vec2Processor, Wav2Vec2ForSequenceClassification
-import librosa
 
-import torch
-print(torch.cuda.is_available())  # ✅ Tells you if GPU is available
+print(torch.cuda.is_available())
 torch.cuda.empty_cache()
 gc.collect()
 
-
 # Bark TTS
 print("🔁 Loading Bark model...")
 model_bark = BarkModel.from_pretrained("suno/bark").to("cuda" if torch.cuda.is_available() else "cpu")
@@ -1480,20 +1752,13 @@ print("✅ Bark model loaded")
 print("🔁 Loading Bark processor...")
 processor_bark = AutoProcessor.from_pretrained("suno/bark")
 print("✅ Bark processor loaded")
-bark_voice_preset = "v2/en_speaker_5"
+bark_voice_preset = "v2/en_speaker_12"
 
 def bark_tts(text):
     print(f"🔁 Synthesizing TTS for: {text}")
-    
-    # Process the text
     inputs = processor_bark(text, return_tensors="pt", voice_preset=bark_voice_preset)
-    
-    # Move tensors to device
     input_ids = inputs["input_ids"].to(model_bark.device)
-    
     start = time.time()
-    
-    # Generate speech with only the required parameters
     with torch.no_grad():
         speech_values = model_bark.generate(
             input_ids=input_ids,
@@ -1501,22 +1766,13 @@ def bark_tts(text):
             fine_temperature=0.4,
             coarse_temperature=0.8
         )
-    
     print(f"✅ Bark finished in {round(time.time() - start, 2)}s")
-    
-    # Convert to audio
     speech = speech_values.cpu().numpy().squeeze()
     speech = (speech * 32767).astype(np.int16)
-    
     temp_wav = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
     wavfile.write(temp_wav.name, 22050, speech)
-    
     return temp_wav.name
 
-
-
-
-
 # Whisper STT
 print("🔁 Loading Whisper model...")
 whisper_model = whisper.load_model("base", device="cuda")
@@ -1530,15 +1786,11 @@ seniority_mapping = {
     "Entry-level": 1, "Junior": 2, "Mid-Level": 3, "Senior": 4, "Lead": 5
 }
 
-
-# --- 2. Gradio App ---
-
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     user_data = gr.State({})
     interview_state = gr.State({})
     missing_fields_state = gr.State([])
 
-    # --- UI Layout ---
     with gr.Column(visible=True) as user_info_section:
         gr.Markdown("## Candidate Information")
         cv_file = gr.File(label="Upload CV")
@@ -1567,8 +1819,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         evaluation_display = gr.Markdown()
         interview_summary = gr.Markdown(visible=False)
 
-    # --- UI Logic ---
-
     def validate_start_btn(cv_file, job_desc):
         return gr.update(interactive=(cv_file is not None and hasattr(cv_file, "name") and bool(job_desc and job_desc.strip())))
     cv_file.change(validate_start_btn, [cv_file, job_desc], start_btn)
@@ -1578,8 +1828,10 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         details = extract_candidate_details(cv_file.name)
         job_info = extract_job_details(job_desc)
         data = {
-            "name": details.get("name", "unknown"), "job_role": job_info.get("job_title", "unknown"),
-            "seniority": job_info.get("experience_level", "unknown"), "skills": job_info.get("skills", [])
+            "name": details.get("name", "unknown"),
+            "job_role": job_info.get("job_title", "unknown"),
+            "seniority": job_info.get("experience_level", "unknown"),
+            "skills": job_info.get("skills", [])
         }
         missing = [k for k, v in data.items() if (isinstance(v, str) and v.lower() == "unknown") or not v]
         if missing:
@@ -1587,11 +1839,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         else:
             greeting = f"Hello {data['name']}, your profile is ready. Click 'Start Interview' when ready."
             return data, missing, gr.update(visible=False), gr.update(visible=False), gr.update(visible=True, value=greeting)
-    start_btn.click(
-        process_and_route_initial,
-        [cv_file, job_desc],
-        [user_data, missing_fields_state, user_info_section, missing_section, pre_interview_greeting_md]
-    )
+    start_btn.click(process_and_route_initial, [cv_file, job_desc], [user_data, missing_fields_state, user_info_section, missing_section, pre_interview_greeting_md])
 
     def show_missing(missing):
         if missing is None: missing = []
@@ -1600,7 +1848,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
     def validate_fields(name, role, seniority, skills, missing):
         if not missing: return gr.update(interactive=False)
-        all_filled = all([(not ("name" in missing) or bool(name.strip())), (not ("job_role" in missing) or bool(role.strip())), (not ("seniority" in missing) or bool(seniority)), (not ("skills" in missing) or bool(skills.strip())),])
+        all_filled = all([(not ("name" in missing) or bool(name.strip())), (not ("job_role" in missing) or bool(role.strip())), (not ("seniority" in missing) or bool(seniority)), (not ("skills" in missing) or bool(skills.strip()))])
         return gr.update(interactive=all_filled)
     for inp in [name_in, role_in, seniority_in, skills_in]:
         inp.change(validate_fields, [name_in, role_in, seniority_in, skills_in, missing_fields_state], submit_btn)
@@ -1615,46 +1863,22 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     submit_btn.click(complete_manual, [user_data, name_in, role_in, seniority_in, skills_in], [user_data, missing_section, interview_pre_section, pre_interview_greeting_md])
 
     def start_interview(data):
-        # --- Advanced state with full logging ---
         state = {
-            "questions": [], "answers": [], "face_labels": [], "voice_labels": [], "timings": [],
-            "question_evaluations": [], "answer_evaluations": [], "effective_confidences": [],
-            "conversation_history": [],
-            "difficulty_adjustment": None,
-            "question_idx": 0, "max_questions": 3, "q_start_time": time.time(),
-            "log": []
+            "questions": [], "answers": [], "timings": [], "question_evaluations": [], "answer_evaluations": [],
+            "conversation_history": [], "difficulty_adjustment": None, "question_idx": 0, "max_questions": 3,
+            "q_start_time": time.time(), "log": []
         }
-        # --- Optionally: context retrieval here (currently just blank) ---
         context = ""
         prompt = build_interview_prompt(
             conversation_history=[], user_response="", context=context, job_role=data["job_role"],
-            skills=data["skills"], seniority=data["seniority"], difficulty_adjustment=None,
-            voice_label="neutral", face_label="neutral"
+            skills=data["skills"], seniority=data["seniority"], difficulty_adjustment=None, voice_label="neutral"
         )
-        #here the original one 
-        # first_q = groq_llm.predict(prompt)
-        # # Evaluate Q for quality
-        # q_eval = eval_question_quality(first_q, data["job_role"], data["seniority"], None)
-        # state["questions"].append(first_q)
-        # state["question_evaluations"].append(q_eval)
-
-        #here the testing one
         first_q = groq_llm.predict(prompt)
-        q_eval = {
-            "Score": "N/A",
-            "Reasoning": "Skipped to reduce processing time",
-            "Improvements": []
-        }
+        q_eval = {"Score": "N/A", "Reasoning": "Skipped to reduce processing time", "Improvements": []}
         state["questions"].append(first_q)
         state["question_evaluations"].append(q_eval)
-
-        
         state["conversation_history"].append({'role': 'Interviewer', 'content': first_q})
-        start = time.perf_counter()
         audio_path = bark_tts(first_q)
-        print("⏱️ Bark TTS took", time.perf_counter() - start, "seconds")
-
-        # LOG
         state["log"].append({"type": "question", "question": first_q, "question_eval": q_eval, "timestamp": time.time()})
         return state, gr.update(visible=False), gr.update(visible=True), audio_path, f"*Question 1:* {first_q}"
     start_interview_final_btn.click(start_interview, [user_data], [interview_state, interview_pre_section, interview_section, question_audio, question_text])
@@ -1663,30 +1887,22 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         return whisper_stt(audio_path)
     user_audio_input.change(transcribe, user_audio_input, stt_transcript)
 
-    def process_answer(transcript, audio_path, video_path, state, data):
+    def process_answer(transcript, audio_path, state, data):
         if not transcript:
             return state, gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update()
-    
+
         elapsed = round(time.time() - state.get("q_start_time", time.time()), 2)
         state["timings"].append(elapsed)
         state["answers"].append(transcript)
         state["conversation_history"].append({'role': 'Candidate', 'content': transcript})
 
-        # --- 1. Emotion analysis (simplified for testing) ---
-        voice_label = "neutral"
-        face_label = "neutral"
-        state["voice_labels"].append(voice_label)
-        state["face_labels"].append(face_label)
-
-        # --- 2. Evaluate previous Q and Answer ---
         last_q = state["questions"][-1]
-        q_eval = state["question_evaluations"][-1]  # Already in state
+        q_eval = state["question_evaluations"][-1]
         ref_answer = generate_reference_answer(last_q, data["job_role"], data["seniority"])
         answer_eval = evaluate_answer(last_q, transcript, ref_answer, data["job_role"], data["seniority"], None)
         state["answer_evaluations"].append(answer_eval)
         answer_score = answer_eval.get("Score", "medium") if answer_eval else "medium"
 
-        # --- 3. Adaptive difficulty ---
         if answer_score == "excellent":
             state["difficulty_adjustment"] = "harder"
         elif answer_score in ("medium", "poor"):
@@ -1694,33 +1910,18 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         else:
             state["difficulty_adjustment"] = None
 
-        # --- 4. Effective confidence (simplified) ---
-        eff_conf = {"effective_confidence": 0.6}
-        state["effective_confidences"].append(eff_conf)
-
-        # --- LOG ---
         state["log"].append({
-            "type": "answer",
-            "question": last_q,
-            "answer": transcript,
-            "answer_eval": answer_eval,
-            "ref_answer": ref_answer,
-            "face_label": face_label,
-            "voice_label": voice_label,
-            "effective_confidence": eff_conf,
-            "timing": elapsed,
-            "timestamp": time.time()
+            "type": "answer", "question": last_q, "answer": transcript,
+            "answer_eval": answer_eval, "ref_answer": ref_answer,
+            "timing": elapsed, "timestamp": time.time()
         })
 
-        # --- Next or End ---
         qidx = state["question_idx"] + 1
         if qidx >= state["max_questions"]:
-            # Save as JSON (optionally)
             timestamp = time.strftime("%Y%m%d_%H%M%S")
             log_file = f"interview_log_{timestamp}.json"
             with open(log_file, "w", encoding="utf-8") as f:
                 json.dump(state["log"], f, indent=2, ensure_ascii=False)
-            # Report
             summary = "# Interview Summary\n"
             for i, q in enumerate(state["questions"]):
                 summary += (f"\n### Q{i + 1}: {q}\n"
@@ -1729,41 +1930,31 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                             f"- *A Eval*: {state['answer_evaluations'][i]}\n"
                             f"- *Time*: {state['timings'][i]}s\n")
             summary += f"\n\n⏺ Full log saved as {log_file}."
-            return (state, gr.update(visible=True, value=summary), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(visible=True, value=f"Last Detected — Face: {face_label}, Voice: {voice_label}"))
+            return state, gr.update(visible=True, value=summary), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(visible=False)
         else:
-            # --- Build next prompt using adaptive difficulty ---
             state["question_idx"] = qidx
             state["q_start_time"] = time.time()
-            context = ""  # You can add your context logic here
+            context = ""
             prompt = build_interview_prompt(
                 conversation_history=state["conversation_history"],
-                user_response=transcript,
-                context=context,
-                job_role=data["job_role"],
-                skills=data["skills"],
-                seniority=data["seniority"],
-                difficulty_adjustment=state["difficulty_adjustment"],
-                voice_label=voice_label,
+                user_response=transcript, context=context,
+                job_role=data["job_role"], skills=data["skills"],
+                seniority=data["seniority"], difficulty_adjustment=state["difficulty_adjustment"],
+                voice_label="neutral"
             )
             next_q = groq_llm.predict(prompt)
-            # Evaluate Q quality
             q_eval = eval_question_quality(next_q, data["job_role"], data["seniority"], None)
             state["questions"].append(next_q)
             state["question_evaluations"].append(q_eval)
             state["conversation_history"].append({'role': 'Interviewer', 'content': next_q})
             state["log"].append({"type": "question", "question": next_q, "question_eval": q_eval, "timestamp": time.time()})
             audio_path = bark_tts(next_q)
-            # Display evaluations
-            eval_md = f"*Last Answer Eval:* {answer_eval}\n\n*Effective Confidence:* {eff_conf}"
-            return (
-                state, gr.update(visible=False), audio_path, f"*Question {qidx + 1}:* {next_q}",
-                gr.update(value=None), gr.update(value=None),
-                gr.update(visible=True, value=eval_md),
-            )
-    # Replace your confirm_btn.click with this:
+            eval_md = f"*Last Answer Eval:* {answer_eval}"
+            return state, gr.update(visible=False), audio_path, f"*Question {qidx + 1}:* {next_q}", gr.update(value=None), gr.update(value=None), gr.update(visible=True, value=eval_md)
+
     confirm_btn.click(
         process_answer,
-        [stt_transcript, user_audio_input, None, interview_state, user_data],  # Added None for video_path
+        [stt_transcript, user_audio_input, interview_state, user_data],
         [interview_state, interview_summary, question_audio, question_text, user_audio_input, stt_transcript, evaluation_display]
     ).then(
         lambda: (gr.update(value=None), gr.update(value=None)), None, [user_audio_input, stt_transcript]