main.py

# main.py — AI Interview Confidence Analyzer (Gradio / HuggingFace Spaces)

import cv2
import sys
import os
import time
import numpy as np
from collections import deque
import gradio as gr
import tempfile

sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "modules"))

from modules.face_landmarks       import FaceLandmarkExtractor
from modules.expression_detection import ExpressionDetector
from modules.eye_contact          import EyeContactDetector
from modules.head_pose            import HeadPoseEstimator
from modules.llm                  import (
    generate_questions,
    evaluate_answer,
    generate_final_summary,
)
from modules.resume_parser import resume_to_profile, get_resume_context_for_llm

# ── Weights ──────────────────────────────────────────────────────────────────
WEIGHTS = {
    "eye_contact":    30,
    "expression":     25,
    "head_stability": 25,
    "nervousness":    20,
}
SCORE_HISTORY_LEN = 45


# ── Helpers ───────────────────────────────────────────────────────────────────
def compute_confidence_score(expr_result, eye_result, head_result):
    eye_score  = eye_result.get("score",              0)
    expr_score = expr_result.get("expression_score",  0)
    head_score = head_result.get("stability_score",   0)
    nerv_score = expr_result.get("nervousness_score", 0)
    nerv_contribution = max(0, 100 - nerv_score)
    final = (
        eye_score         * WEIGHTS["eye_contact"]    / 100 +
        expr_score        * WEIGHTS["expression"]     / 100 +
        head_score        * WEIGHTS["head_stability"] / 100 +
        nerv_contribution * WEIGHTS["nervousness"]    / 100
    )
    return min(100, max(0, int(final)))


def confidence_label(score):
    if score >= 80: return "High",     "#00dc00"
    if score >= 60: return "Moderate", "#00c896"
    if score >= 40: return "Low",      "#00a5ff"
    return "Very Low", "#003cff"


def analyze_frame(frame):
    """Run all detectors on a single frame. Returns annotated frame + metrics dict."""
    landmark_extractor = FaceLandmarkExtractor()
    expr_detector      = ExpressionDetector(fps=30)
    eye_detector       = EyeContactDetector()
    head_estimator     = HeadPoseEstimator()

    lm_result = landmark_extractor.extract(frame)

    if not lm_result["face_detected"]:
        landmark_extractor.release()
        return frame, None

    kp        = lm_result["key_points"]
    landmarks = lm_result["landmarks"]

    expr_result = expr_detector.detect(kp, frame.shape)
    eye_result  = eye_detector.detect(kp, frame.shape)
    head_result = head_estimator.detect(landmarks, frame.shape)
    confidence  = compute_confidence_score(expr_result, eye_result, head_result)

    landmark_extractor.release()

    metrics = {
        "confidence":   confidence,
        "expression":   expr_result.get("expression",        "N/A"),
        "nervousness":  expr_result.get("nervousness_score",  0),
        "blink_rate":   expr_result.get("blink_rate",         0),
        "gaze":         eye_result.get("gaze_direction",      "N/A"),
        "eye_pct":      eye_result.get("eye_contact_pct",     0),
        "direction":    head_result.get("direction",          "N/A"),
        "stability":    head_result.get("stability_score",    0),
        "pitch":        head_result.get("pitch",              0.0),
        "yaw":          head_result.get("yaw",                0.0),
    }
    return lm_result["annotated_frame"], metrics


def metrics_to_markdown(metrics, question=""):
    if metrics is None:
        return "## No face detected\nPosition yourself properly in the frame."

    score = metrics["confidence"]
    label, color = confidence_label(score)

    bar_filled = "█" * (score // 5)
    bar_empty  = "░" * (20 - score // 5)

    md = f"""

## Confidence Score: {score}/100 — {label}



`{bar_filled}{bar_empty}` {score}%



| Metric | Value |

|---|---|

| Expression | {metrics['expression']} |

| Nervousness | {metrics['nervousness']}/100 |

| Blink Rate | {metrics['blink_rate']}/min |

| Gaze | {metrics['gaze']} |

| Eye Contact | {metrics['eye_pct']}% |

| Head Direction | {metrics['direction']} |

| Head Stability | {metrics['stability']}/100 |

| Pitch / Yaw | {metrics['pitch']:.1f} / {metrics['yaw']:.1f} |



### Tips

"""
    if metrics["gaze"] != "Center":
        md += "- Look directly at the camera\n"
    if metrics["nervousness"] > 50:
        md += "- Breathe slowly, you got this\n"
    if metrics["direction"] != "Forward":
        md += "- Face forward\n"
    if metrics["gaze"] == "Center" and metrics["nervousness"] <= 50 and metrics["direction"] == "Forward":
        md += "- Great posture! Keep it up\n"

    if question:
        md += f"\n---\n**Current Question:** {question}"

    return md


# ── Tab 1: Image Analysis ─────────────────────────────────────────────────────
def analyze_image(image):
    if image is None:
        return None, "Please upload an image."

    frame = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
    annotated, metrics = analyze_frame(frame)

    if metrics is None:
        out_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        return out_img, "No face detected in image."

    out_img = cv2.cvtColor(annotated, cv2.COLOR_BGR2RGB)
    return out_img, metrics_to_markdown(metrics)


# ── Tab 2: Video Analysis ─────────────────────────────────────────────────────
def analyze_video(video_path):
    if video_path is None:
        return None, "Please upload a video."

    cap = cv2.VideoCapture(video_path)
    if not cap.isOpened():
        return None, "Could not open video."

    all_scores   = []
    expr_counts  = {}
    frame_count  = 0
    sample_every = 10   # analyze every 10th frame for speed
    last_annotated = None

    while True:
        ret, frame = cap.read()
        if not ret:
            break
        frame_count += 1
        if frame_count % sample_every != 0:
            continue

        annotated, metrics = analyze_frame(frame)
        if metrics:
            all_scores.append(metrics["confidence"])
            expr = metrics["expression"]
            expr_counts[expr] = expr_counts.get(expr, 0) + 1
            last_annotated = annotated

    cap.release()

    if not all_scores:
        return None, "No face detected in video."

    avg  = int(np.mean(all_scores))
    peak = int(np.max(all_scores))
    low  = int(np.min(all_scores))
    label, _ = confidence_label(avg)

    expr_breakdown = "\n".join(
        f"- {e}: {int(c / sum(expr_counts.values()) * 100)}%"
        for e, c in sorted(expr_counts.items(), key=lambda x: -x[1])
    )

    summary_md = f"""

## Video Analysis Summary



| Metric | Value |

|---|---|

| Avg Confidence | {avg}/100 [{label}] |

| Peak Score | {peak}/100 |

| Lowest Score | {low}/100 |

| Frames Analyzed | {len(all_scores)} |



### Expression Breakdown

{expr_breakdown}

"""
    out_img = cv2.cvtColor(last_annotated, cv2.COLOR_BGR2RGB) if last_annotated is not None else None
    return out_img, summary_md


# ── Tab 3: AI Mock Interview ──────────────────────────────────────────────────
def setup_interview(name, job_role, experience, skills, resume_file):
    """Generate questions from profile or resume."""
    if not name or not job_role:
        return "Please fill Name and Job Role.", gr.update(visible=False), []

    profile = {
        'name': name, 'job_role': job_role,
        'experience': experience, 'skills': skills,
        'projects': [], 'education': '', 'summary': '', 'resume_text': ''
    }

    if resume_file is not None:
        try:
            parsed = resume_to_profile(resume_file.name)
            profile.update({k: v for k, v in parsed.items() if v})
        except Exception as e:
            print(f"[WARN] Resume parse failed: {e}")

    resume_ctx = get_resume_context_for_llm(profile)

    try:
        questions = generate_questions(
            profile['name'], profile['job_role'],
            profile['experience'], profile['skills'],
            resume_text=resume_ctx,
            num_questions=3,
        )
    except Exception as e:
        return f"Question generation failed: {e}", gr.update(visible=False), []

    q_display = "\n\n".join([f"**Q{i+1}:** {q}" for i, q in enumerate(questions)])
    return (
        f"Interview ready! {len(questions)} questions generated.\n\n{q_display}",
        gr.update(visible=True),
        questions
    )


def evaluate_single_answer(question, answer, job_role):
    """Evaluate one Q&A pair with LLM."""
    if not question or not answer:
        return "Please provide both question and answer."
    try:
        feedback = evaluate_answer(
            question=question,
            answer=answer,
            job_role=job_role,
            followup='',
            followup_answer='',
        )
        score_str  = feedback.get('score_str',    '?')
        strength   = feedback.get('strength',     '')
        improve    = feedback.get('improvement',  '')
        detail     = feedback.get('detail',       '')
        return f"""

## AI Evaluation



**Score:** {score_str}



**Strength:** {strength}



**Improvement:** {improve}



**Details:** {detail}

"""
    except Exception as e:
        return f"Evaluation failed: {e}"


# ── Build Gradio UI ───────────────────────────────────────────────────────────
with gr.Blocks(
    title="AI Interview Confidence Analyzer",
    theme=gr.themes.Base(primary_hue="purple"),
    css="""

    .score-box { font-size: 1.4em; font-weight: bold; }

    footer { display: none !important; }

    """
) as demo:

    gr.Markdown("""

    # 🎙️ AI Interview Confidence Analyzer

    Multimodal confidence analysis — Face | Expression | Eye Contact | Head Pose | LLM Evaluation

    """)

    with gr.Tabs():

        # ── Tab 1: Image ──────────────────────────────────────────────────────
        with gr.Tab("📸 Analyze Image"):
            gr.Markdown("Upload a photo from your interview or practice session.")
            with gr.Row():
                img_input  = gr.Image(label="Upload Image", type="pil")
                img_output = gr.Image(label="Annotated Result")
            img_metrics = gr.Markdown(label="Metrics")
            img_btn     = gr.Button("Analyze", variant="primary")
            img_btn.click(
                fn=analyze_image,
                inputs=img_input,
                outputs=[img_output, img_metrics]
            )

        # ── Tab 2: Video ──────────────────────────────────────────────────────
        with gr.Tab("🎬 Analyze Video"):
            gr.Markdown("Upload a recorded interview video for frame-by-frame analysis.")
            with gr.Row():
                vid_input  = gr.Video(label="Upload Video")
                vid_output = gr.Image(label="Last Analyzed Frame")
            vid_metrics = gr.Markdown(label="Summary")
            vid_btn     = gr.Button("Analyze Video", variant="primary")
            vid_btn.click(
                fn=analyze_video,
                inputs=vid_input,
                outputs=[vid_output, vid_metrics]
            )

        # ── Tab 3: AI Mock Interview ──────────────────────────────────────────
        with gr.Tab("🤖 AI Mock Interview"):
            gr.Markdown("""

            Enter your profile, generate personalized interview questions,

            then type your answers to get AI feedback.

            """)

            with gr.Row():
                with gr.Column():
                    name_input  = gr.Textbox(label="Your Name", placeholder="Sunaina")
                    role_input  = gr.Textbox(label="Job Role",  placeholder="AI/ML Engineer")
                    exp_input   = gr.Textbox(label="Experience", placeholder="1 year intern, built RAG systems")
                    skill_input = gr.Textbox(label="Skills",    placeholder="Python, LangChain, FastAPI, FAISS")
                    resume_file = gr.File(label="Resume (PDF/DOCX/TXT) — optional", file_types=[".pdf", ".docx", ".txt"])
                    setup_btn   = gr.Button("Generate Questions", variant="primary")

                with gr.Column():
                    setup_output = gr.Markdown(label="Questions")

            questions_state = gr.State([])

            setup_btn.click(
                fn=setup_interview,
                inputs=[name_input, role_input, exp_input, skill_input, resume_file],
                outputs=[setup_output, gr.Column(visible=False), questions_state]
            )

            gr.Markdown("---")
            gr.Markdown("### Evaluate Your Answer")
            gr.Markdown("Copy a question from above, paste it, write your answer, and get AI feedback.")

            with gr.Row():
                eval_question = gr.Textbox(label="Question", lines=2, placeholder="Paste the question here")
                eval_role     = gr.Textbox(label="Job Role", placeholder="AI/ML Engineer")
            eval_answer   = gr.Textbox(label="Your Answer", lines=5, placeholder="Type your answer here...")
            eval_btn      = gr.Button("Get AI Feedback", variant="primary")
            eval_output   = gr.Markdown(label="AI Feedback")

            eval_btn.click(
                fn=evaluate_single_answer,
                inputs=[eval_question, eval_answer, eval_role],
                outputs=eval_output
            )

    gr.Markdown("""

    ---

    Built by **Sunaina** | AI/ML Engineer Intern @ Indux Technology

    | [GitHub](https://github.com/Sunaina792/AI-Interview-system)

    """)

if __name__ == "__main__":
    demo.launch()