updated

backend/routes/interview_api.py

@@ -363,4 +363,125 @@ from flask import render_template
 @interview_api.route("/interview/complete", methods=["GET"])
 @login_required
 def interview_complete():
-    return render_template("closing.html")
+    """
+    Final interview completion page.  After the last question has been
+    answered, redirect here to show the candidate a brief summary of
+    their overall performance.  The summary consists of a percentage
+    score and a high‑level label (e.g. "Excellent", "Good").  These
+    values are derived from the candidate's application data and
+    interview evaluations.
+
+    The calculation mirrors the logic used in the PDF report
+    generation: the skills match ratio contributes 40% of the final
+    score while the average of the per‑question evaluation ratings
+    contributes 60%.  If no evaluation data is available, a default
+    average of 0.5 is used.  The resulting number is expressed as a
+    percentage (e.g. "75%") and mapped to a descriptive label.
+    """
+
+    score = None
+    feedback_summary = None
+
+    try:
+        # Attempt to locate the most recent application with interview data
+        # for the current user.  Because the completion route does not
+        # receive a job ID, we fall back to the latest application that
+        # contains an interview_log.  If none exists, the summary will
+        # remain empty and the template will render placeholders.
+        application = (
+            Application.query
+            .filter_by(user_id=current_user.id)
+            .filter(Application.interview_log.isnot(None))
+            .order_by(Application.id.desc())
+            .first()
+        )
+
+        if application:
+            # Parse candidate and job skills from stored JSON.  If either
+            # field is missing or malformed, fall back to empty lists.
+            try:
+                candidate_features = json.loads(application.extracted_features) if application.extracted_features else {}
+            except Exception:
+                candidate_features = {}
+            candidate_skills = candidate_features.get('skills', []) or []
+
+            job_skills = []
+            try:
+                job_skills = json.loads(application.job.skills) if application.job and application.job.skills else []
+            except Exception:
+                job_skills = []
+
+            # Compute the skills match ratio.  Normalise skills to lower
+            # case and strip whitespace for comparison.  Avoid division
+            # by zero if the job has no listed skills.
+            candidate_set = {s.strip().lower() for s in candidate_skills}
+            job_set = {s.strip().lower() for s in job_skills}
+            common = candidate_set & job_set
+            ratio = (len(common) / len(job_set)) if job_set else 0.0
+
+            # Extract per‑question evaluations from the interview log.  The
+            # interview_log stores a list of dictionaries with keys
+            # "question", "answer" and "evaluation".  Each evaluation is
+            # expected to include a "score" field containing text such
+            # as "Poor", "Medium", "Good" or "Excellent".  Convert
+            # these descriptors into numeric values in the range [0.2, 1.0]
+            # similar to the logic used in report generation.
+            qa_scores = []
+            try:
+                if application.interview_log:
+                    try:
+                        log_data = json.loads(application.interview_log)
+                    except Exception:
+                        log_data = []
+                    for entry in log_data:
+                        score_text = str(entry.get('evaluation', {}).get('score', '')).lower()
+                        # Map textual scores to numerical values
+                        if ('excellent' in score_text) or ('5' in score_text) or ('10' in score_text):
+                            qa_scores.append(1.0)
+                        elif ('good' in score_text) or ('4' in score_text) or ('8' in score_text) or ('9' in score_text):
+                            qa_scores.append(0.8)
+                        elif ('satisfactory' in score_text) or ('medium' in score_text) or ('3' in score_text) or ('6' in score_text) or ('7' in score_text):
+                            qa_scores.append(0.6)
+                        elif ('needs improvement' in score_text) or ('poor' in score_text) or ('2' in score_text):
+                            qa_scores.append(0.4)
+                        else:
+                            qa_scores.append(0.2)
+            except Exception:
+                qa_scores = []
+
+            # Average the QA scores.  If no scores were recorded (e.g. if
+            # the interview_log is empty or malformed), assume a neutral
+            # average of 0.5 to avoid penalising the candidate for missing
+            # data.
+            qa_average = (sum(qa_scores) / len(qa_scores)) if qa_scores else 0.5
+
+            # Weight skills match (40%) and QA average (60%) to derive
+            # the final overall score.  Convert to a percentage for
+            # display.
+            overall = (ratio * 0.4) + (qa_average * 0.6)
+            percentage = overall * 100.0
+
+            # Assign a descriptive label based on the overall score.
+            if overall >= 0.8:
+                label = 'Excellent'
+            elif overall >= 0.65:
+                label = 'Good'
+            elif overall >= 0.45:
+                label = 'Satisfactory'
+            else:
+                label = 'Needs Improvement'
+
+            # Format the score as a whole‑number percentage.  For example
+            # 0.753 becomes "75%".  Note that rounding is applied.
+            score = f"{percentage:.0f}%"
+            feedback_summary = label
+
+    except Exception as calc_err:
+        # If any error occurs during calculation, fall back to None values.
+        logging.error(f"Error computing overall interview score: {calc_err}")
+        
+    return render_template(
+        "closing.html",
+        score=score,
+        feedback_summary=feedback_summary
+    )