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import os |
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os.environ["TRANSFORMERS_CACHE"] = "/tmp/transformers" |
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os.environ["HF_HOME"] = "/tmp/huggingface" |
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import json |
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import re |
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import logging |
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import math |
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import docx |
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import fitz |
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from dotenv import load_dotenv |
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import google.generativeai as genai |
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from flask import Flask, render_template, request, jsonify |
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from sentence_transformers import SentenceTransformer, util |
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from rapidfuzz import fuzz, process |
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from urllib.parse import quote_plus |
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load_dotenv() |
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GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") |
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genai.configure(api_key=GEMINI_API_KEY) |
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app = Flask(__name__) |
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S') |
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model = SentenceTransformer("sentence-transformers/paraphrase-albert-small-v2") |
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def load_json(filename): |
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try: |
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with open(filename, "r", encoding="utf-8") as f: |
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return json.load(f) |
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except Exception as e: |
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logging.error(f"Error loading {filename}: {e}") |
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return [] if filename != "pincodes.json" else {} |
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COURSE_DATA = load_json("careers.json") |
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COLLEGE_DATA = load_json("colleges.json") |
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PINCODE_DATA = load_json("pincodes.json") |
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ALL_TAGS = set() |
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for course in COURSE_DATA: |
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tags_obj = course.get("tags", {}) |
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if isinstance(tags_obj, dict): |
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for category_tags in tags_obj.values(): ALL_TAGS.update(category_tags) |
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ALL_TAGS.update(["theory", "research", "practical"]) |
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QUESTIONS = { "stream": "What was your academic stream after 10th?", "subject_strengths": "Which subjects do you feel strongest in?", "subject_weaknesses": "Which subjects do you find most difficult?", "learning_style": "Do you learn better through practical work or theory/research?", "work_environment": "What kind of work environment do you prefer? (e.g., an office, a lab, outdoors, a workshop)", "team_preference": "Do you prefer working alone or collaboratively?", "interest_activities": "Outside academics, what hobbies do you enjoy?", "general_interests": "What topics or fields are you generally curious about?", "primary_driver": "What motivates your future most? (e.g., money, creativity, helping people, innovation, stability)" } |
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STOP_WORDS = {"a", "an", "and", "the", "in", "on", "for", "with", "i", "my", "is", "are", "like", "to", "of"} |
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TRAIT_KEYWORDS = { "analytical": ["math", "physics", "science", "data", "logic", "puzzles", "engineering", "theory", "research"], "creative": ["art", "design", "music", "writing", "media", "film", "painting"], "social": ["helping", "teaching", "volunteering", "communication", "people", "society", "healthcare", "environment"], "structured": ["commerce", "law", "management", "finance", "corporate", "office"], "hands_on": ["practical", "projects", "sports", "repair", "construction", "biology"], "collaborative": ["team", "teamwork", "collaboration", "people", "social"], "independent": ["alone", "independent", "self-directed", "focus", "quiet"], "field_work": ["outdoors", "on-the-move", "travel", "construction", "farming"], "lab_work": ["lab", "research", "science", "biotech", "forensic"] } |
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def preprocess_text(text): |
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text = text.lower() |
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text = re.sub(r'[.&,]', '', text) |
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text = re.sub(r'\b(in|and)\b', '', text) |
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return re.sub(r'\s+', ' ', text).strip() |
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def normalize_word(word): |
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if not ALL_TAGS: return word |
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best, score, _ = process.extractOne(word, ALL_TAGS, scorer=fuzz.ratio) |
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return best if score >= 85 else word |
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def parse_input(text): |
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words = re.split(r"[,\s]+", text.lower()) |
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return {normalize_word(word) for word in words if word and word not in STOP_WORDS} |
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def build_user_profile(answers): |
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profile = {key: parse_input(value) for key, value in answers.items() if value} |
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all_keywords = set().union(*profile.values()) if profile else set() |
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persona = {trait for trait, kws in TRAIT_KEYWORDS.items() if not all_keywords.isdisjoint(kws)} |
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return profile, persona |
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def calculate_distance(pin1, pin2, pincode_data): |
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if pin1 not in pincode_data or pin2 not in pincode_data: return float('inf') |
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lat1, lon1 = pincode_data[pin1]['lat'], pincode_data[pin1]['lon'] |
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lat2, lon2 = pincode_data[pin2]['lat'], pincode_data[pin2]['lon'] |
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R, dLat, dLon = 6371, math.radians(lat2 - lat1), math.radians(lon2 - lon1) |
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a = math.sin(dLat / 2)**2 + math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) * math.sin(dLon / 2)**2 |
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c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a)) |
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return R * c |
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def find_nearby_colleges(course_name, user_pincode): |
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if user_pincode not in PINCODE_DATA: |
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return "<div class='college-card error-card'>Sorry, I don't have location data for that PIN code.</div>", [] |
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colleges_with_course = [] |
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for college in COLLEGE_DATA: |
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for offered_course in college.get('courses_offered', []): |
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if course_name.lower() == offered_course.lower(): |
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colleges_with_course.append(college) |
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break |
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if not colleges_with_course: |
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return f"<div class='college-card error-card'>I couldn't find any colleges in my database offering <b>{course_name}</b>.</div>", [] |
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nearby_colleges = sorted([(c, calculate_distance(user_pincode, c['pincode'], PINCODE_DATA)) for c in colleges_with_course], key=lambda x: x[1]) |
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response_html = "<div class='college-card primary-card'>" |
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response_html += f"<h4>🎯 Top Matches for {course_name} near {user_pincode}</h4><ul>" |
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colleges_found = 0 |
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for college, dist in nearby_colleges: |
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if dist <= 150: |
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colleges_found += 1 |
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encoded_name = quote_plus(college['name']) |
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response_html += f"<li><a href='https://www.google.com/search?q={encoded_name}' target='_blank'><b>{college['name']}</b></a> ({college['pincode']})<br><small>Approx. {dist:.0f} km away</small></li>" |
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if colleges_found == 0: |
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return f"<div class='college-card error-card'>I couldn't find any colleges offering <b>{course_name}</b> within a 150km radius of your PIN code.</div>", [] |
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response_html += "</ul></div>" |
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return response_html, nearby_colleges |
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def format_course_details(course): |
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details = "<div class='details-card'>" |
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details += f"<h3>🎓 {course.get('course', 'N/A')}</h3><p>{course.get('description', '')}</p>" |
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careers = course.get('possible_careers', []) |
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if careers: details += "<b>💼 Potential Career Paths:</b><ul>" + "".join(f"<li>{c}</li>" for c in careers) + "</ul>" |
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education = course.get('required_education', '') |
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if education: details += f"<p>✅ <b>Entry Requirements:</b> {education}</p>" |
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related = course.get('related_courses', []) |
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if related: details += "<b>📚 Key Subjects You'll Study:</b><ul>" + "".join(f"<li>{s}</li>" for s in related) + "</ul>" |
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details += "</div>" |
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return details |
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def format_comparison(courses): |
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if not courses: return "<div class='details-card'><p>I couldn't find any valid courses to compare. Please check the names and try again.</p></div>" |
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table_style = "width:100%;border-collapse:collapse;text-align:left;" |
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th_style = "border-bottom:2px solid #dee2e6;padding:12px;font-size:1rem;" |
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td_style = "border-bottom:1px solid #dee2e6;padding:12px;vertical-align:top;" |
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html = f"<div class='details-card'><table style='{table_style}'><thead><tr><th style='{th_style}'>Feature</th>" |
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for course in courses: |
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course_name = course.get('course') |
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html += f"<th style='{th_style}'><div class='clickable-card' data-action='quick_reply' data-value='{course_name}' style='padding:0; margin:0; text-align:left;'>{course_name}</div></th>" |
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html += "</tr></thead><tbody>" |
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html += f"<tr><td style='{td_style}'><b>💼 Careers</b></td>" |
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for course in courses: html += f"<td style='{td_style}'>{', '.join(course.get('possible_careers', ['N/A']))}</td>" |
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html += "</tr><tr><td style='{td_style}'><b>✅ Requirements</b></td>" |
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for course in courses: html += f"<td style='{td_style}'>{course.get('required_education', 'N/A')}</td>" |
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html += "</tr><tr><td style='{td_style}'><b>📚 Key Subjects</b></td>" |
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for course in courses: html += f"<td style='{td_style}'>{', '.join(course.get('related_courses', ['N/A']))}</td>" |
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html += "</tr><tr><td style='{td_style}'><b>🛠️ Core Skills</b></td>" |
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for course in courses: |
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skills = course.get('tags', {}).get('skills', ['N/A']) |
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html += f"<td style='{td_style}'>{', '.join(s.capitalize() for s in skills)}</td>" |
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html += "</tr></tbody></table>" |
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html += "<p style='font-size:0.85rem; text-align:center; margin-top:1rem; opacity:0.8;'>You can click on a course title in the table above for a detailed view.</p></div>" |
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return html |
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def get_recommendations(answers, courses): |
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profile, persona = build_user_profile(answers) |
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if not any(profile.values()): return "", [] |
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user_profile_text = " ".join(set().union(*profile.values())) |
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user_emb = model.encode(user_profile_text, convert_to_tensor=True) if user_profile_text.strip() else None |
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if user_emb is None: return "", [] |
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scored_courses = [] |
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for course in courses: |
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tags_obj = course.get("tags", {}) |
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course_tags = set().union(*tags_obj.values()) if isinstance(tags_obj, dict) else set() |
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rich_course_text = f"{course.get('course', '')} {course.get('description', '')} {' '.join(course.get('possible_careers', []))} {' '.join(course_tags)}" |
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course_emb = model.encode(rich_course_text, convert_to_tensor=True) |
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semantic_score = float(util.cos_sim(user_emb, course_emb)[0][0]) |
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heuristic_score = sum([0.3 if profile.get("general_interests", set()).intersection(course_tags) else 0, 0.15 if profile.get("subject_strengths", set()).intersection(course_tags) else 0, 0.1 if profile.get("stream", set()).intersection(course_tags) else 0, 0.15 if persona.intersection(course_tags) else 0]) |
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penalty_score = sum([len(profile.get("subject_weaknesses", set()).intersection(course_tags)) * 0.2, 0.15 if persona.intersection(course.get("anti_tags", [])) else 0]) |
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final_score = (semantic_score + heuristic_score - penalty_score) * 100 |
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if final_score > 20: scored_courses.append((final_score, course)) |
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scored_courses.sort(key=lambda x: x[0], reverse=True) |
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top_courses = scored_courses[:3] |
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raw_recs = [course for _, course in top_courses] |
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if not raw_recs: return "🤔 I couldn’t find a strong match. Would you like to try again?", [] |
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response_html = "<div class='recommendation-container'><h4>🚀 Here are my top recommendations for you:</h4>" |
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for i, (_, course_data) in enumerate(top_courses): |
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course_name = course_data.get('course') |
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description = course_data.get('description', '') |
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skills = course_data.get('tags', {}).get('skills', [])[:3] |
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careers = course_data.get('possible_careers', [])[:3] |
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response_html += f"<div class='recommendation-card clickable-card' data-action='details' data-value='{i+1}'>" |
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response_html += f"<p style='margin-bottom: 1rem;'><b>{i+1}. {course_name}</b><br>{description}</p>" |
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response_html += "<div style='font-size: 0.9rem; display: flex; flex-direction: column; gap: 0.5rem;'>" |
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if skills: |
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response_html += f"<div><b>🛠️ Key Skills:</b> {', '.join(s.capitalize() for s in skills)}</div>" |
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if careers: |
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response_html += f"<div><b>💼 Potential Careers:</b> {', '.join(careers)}</div>" |
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response_html += "</div>" |
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response_html += "</div>" |
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response_html += f"<div class='recommendation-card clickable-card compare-card' data-action='compare' data-value='compare'><p><b>⚖️ Compare Courses</b></p></div></div>" |
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return response_html, raw_recs |
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def next_question(answers): |
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for key, q in QUESTIONS.items(): |
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if key not in answers: return key, q |
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return None, None |
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def extract_text_from_file(file): |
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text = "" |
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filename = file.filename.lower() |
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if filename.endswith('.pdf'): |
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pdf_document = fitz.open(stream=file.read(), filetype="pdf") |
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for page in pdf_document: |
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text += page.get_text() |
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pdf_document.close() |
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elif filename.endswith('.docx'): |
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doc = docx.Document(file) |
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for para in doc.paragraphs: |
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text += para.text + "\n" |
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return text |
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def analyze_resume_and_suggest_jobs(resume_text): |
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if not GEMINI_API_KEY: |
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return "<div class='college-card error-card'>Error: Gemini API key is not configured.</div>" |
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prompt = f""" |
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You are an expert career coach. Analyze the following resume text. |
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Your response must be a single JSON object with four keys: |
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1. "person_name": A string containing the full name of the candidate found in the resume. If no name is clear, return an empty string. |
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2. "overall_score": An integer score out of 100 for the resume's quality. |
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3. "summary": A brief, encouraging 1-2 sentence summary of the resume. |
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4. "job_titles": A list of 3-5 specific job titles the candidate is well-suited for based on their skills and experience. |
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Do not add any text before or after the JSON object. |
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Resume Text to analyze: |
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--- |
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{resume_text} |
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--- |
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""" |
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try: |
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model_gen = genai.GenerativeModel('gemini-1.5-pro-latest') |
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response = model_gen.generate_content(prompt) |
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json_match = re.search(r'\{.*\}', response.text, re.DOTALL) |
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if not json_match: |
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raise ValueError("Invalid JSON response from API") |
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feedback = json.loads(json_match.group(0)) |
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name = feedback.get('person_name', '').strip() |
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html_response = "<div class='details-card'>" |
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if name: |
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html_response += f"<h3>📝 Resume Analysis for {name}</h3>" |
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else: |
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html_response += f"<h3>📝 Resume Analysis</h3>" |
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html_response += f"<p><b>Overall Score:</b> {feedback.get('overall_score', 'N/A')}/100</p>" |
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html_response += f"<p><b>Summary:</b> <i>{feedback.get('summary', '')}</i></p>" |
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html_response += "<b>🚀 Potential Job Roles:</b><ul>" + "".join(f"<li>{title}</li>" for title in feedback.get('job_titles', [])) + "</ul>" |
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html_response += "</div>" |
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return html_response |
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except Exception as e: |
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print(f"\n---!!! GEMINI API ERROR !!!---\n{e}\n-----------------------------\n") |
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logging.error(f"Gemini API Error: {e}") |
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error_message = ("Sorry, the analysis failed. This is often an API key issue. " |
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"Please check the terminal where you ran `python app.py` for the specific error message.") |
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return f"<div class='college-card error-card'>{error_message}</div>" |
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@app.route("/") |
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def index(): |
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return render_template("index.html") |
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@app.route("/upload_resume", methods=["POST"]) |
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def upload_resume(): |
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if 'resume_file' not in request.files: return jsonify({"error": "No file part"}), 400 |
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file = request.files['resume_file'] |
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if file.filename == '': return jsonify({"error": "No selected file"}), 400 |
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if file and (file.filename.lower().endswith('.pdf') or file.filename.lower().endswith('.docx')): |
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try: |
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resume_text = extract_text_from_file(file) |
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if not resume_text.strip(): return jsonify({"response": "<div class='college-card error-card'>The uploaded file seems to be empty.</div>"}) |
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feedback_html = analyze_resume_and_suggest_jobs(resume_text) |
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return jsonify({"response": feedback_html}) |
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except Exception as e: |
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logging.error(f"Resume Upload Error: {e}") |
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return jsonify({"response": "<div class='college-card error-card'>Sorry, an error occurred while processing your file.</div>"}) |
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return jsonify({"error": "Invalid file type. Please upload a PDF or DOCX file."}), 400 |
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@app.route("/chat", methods=["POST"]) |
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def chat(): |
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data = request.get_json() |
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msg = data.get("message", "").strip() |
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convo = data.get("conversation", {}) |
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bot_response = "" |
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if not convo: |
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convo = {"state": "awaiting_initial_action", "answers": {}} |
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bot_response = "Welcome to CareerPal! You can type `start` to begin a personalized guidance session, or select a specific tool from the panel on the left." |
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logging.info("--- NEW SESSION INITIALIZED ---") |
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return jsonify({"response": bot_response, "conversation": convo}) |
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current_state = convo.get("state", "awaiting_initial_action") |
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msg_lower = msg.lower() |
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logging.info(f"STATE: {current_state} | USER: {msg}") |
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feature_commands = ["personalized guidance", "compare courses", "college location finder", "resume analyser"] |
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if msg_lower in feature_commands: |
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current_state = "awaiting_initial_action" |
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if current_state == "awaiting_initial_action": |
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if msg_lower == "start" or msg_lower == "personalized guidance": |
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convo["state"] = "asking_questions" |
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convo["answers"] = {} |
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key, question = next_question(convo["answers"]) |
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bot_response = f"Great, let's find your perfect career path! I'll ask a few questions to get started.<br><br>{question}" |
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elif msg_lower == "compare courses": |
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convo["state"] = "awaiting_compare_confirmation" |
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bot_response = "Do you have specific courses in mind to compare?<div class='quick-reply-container'><div class='quick-reply-button clickable-card' data-action='quick_reply' data-value='Yes'>Yes</div><div class='quick-reply-button clickable-card' data-action='quick_reply' data-value='No'>No</div></div>" |
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elif msg_lower == "college location finder": |
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convo["state"] = "awaiting_course_for_college_search" |
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bot_response = "Happy to help you find colleges! What is the name of the course you're interested in?" |
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elif msg_lower == "resume analyser": |
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convo["state"] = "awaiting_resume_upload" |
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bot_response = "Great! Please upload your resume (PDF or DOCX format) using the upload button below." |
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elif msg_lower == 'end chat': |
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bot_response = "Sure. Would you like to leave some feedback about your experience?<div class='quick-reply-container'><div class='quick-reply-button clickable-card' data-action='quick_reply' data-value='Yes'>👍 Yes</div><div class='quick-reply-button clickable-card' data-action='quick_reply' data-value='No'>👎 No</div></div>" |
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convo['state'] = 'awaiting_end_confirmation' |
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else: |
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bot_response = "Sorry, I didn't understand. You can type `start` or select a feature from the panel." |
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elif current_state == "asking_questions": |
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last_key, _ = next_question(convo["answers"]) |
|
|
if last_key: |
|
|
convo["answers"][last_key] = msg |
|
|
parsed_tags = parse_input(msg) |
|
|
cleaned = ", ".join(tag.capitalize() for tag in parsed_tags) or msg |
|
|
if last_key == "subject_weaknesses": bot_response = f"👌 Got it — I’ll stay away from careers heavy in {cleaned}. " |
|
|
elif last_key == "subject_strengths": bot_response = f"🔥 Nice! Being strong in {cleaned} is a great asset. " |
|
|
elif last_key == "interest_activities": bot_response = f"😎 Cool! Enjoying {cleaned} gives me clues about your personality. " |
|
|
elif last_key == "general_interests": bot_response = f"👍 That's insightful! An interest in {cleaned} helps narrow down the options. " |
|
|
else: bot_response = "✅ Okay, noted. " |
|
|
next_key, next_q = next_question(convo["answers"]) |
|
|
if next_q: |
|
|
bot_response += next_q |
|
|
else: |
|
|
bot_response, recs = get_recommendations(convo["answers"], COURSE_DATA) |
|
|
if recs: |
|
|
convo["last_recommendations"] = recs |
|
|
convo["state"] = "awaiting_more_details" |
|
|
bot_response += "<br>Click a course for more details, compare, or end the session." |
|
|
bot_response += "<div class='quick-reply-container'><div class='quick-reply-button clickable-card' data-action='quick_reply' data-value='End Chat'>🚪 End Chat</div></div>" |
|
|
else: |
|
|
convo["state"] = "awaiting_initial_action" |
|
|
|
|
|
elif current_state == "awaiting_compare_confirmation": |
|
|
if msg_lower == 'yes': |
|
|
convo["state"] = "awaiting_course_names_for_compare" |
|
|
bot_response = "Please enter up to 3 course names, separated by commas." |
|
|
else: |
|
|
convo["state"] = "asking_questions" |
|
|
convo["answers"] = {} |
|
|
key, question = next_question(convo["answers"]) |
|
|
bot_response = f"No problem! Let's find some courses for you first.<br><br>{question}" |
|
|
|
|
|
elif current_state == "awaiting_course_names_for_compare": |
|
|
user_courses = [name.strip() for name in msg_lower.split(',')[:3]] |
|
|
matched_courses = [] |
|
|
course_titles = [c['course'] for c in COURSE_DATA] |
|
|
for user_course in user_courses: |
|
|
best_match, score, _ = process.extractOne(user_course, course_titles, scorer=fuzz.token_set_ratio, processor=preprocess_text) |
|
|
if score > 85: |
|
|
matched_courses.append(next(c for c in COURSE_DATA if c['course'] == best_match)) |
|
|
|
|
|
bot_response = format_comparison(matched_courses) |
|
|
convo["last_recommendations"] = matched_courses |
|
|
convo["state"] = "awaiting_more_details" |
|
|
bot_response += "<div class='quick-reply-container'><div class='quick-reply-button clickable-card' data-action='quick_reply' data-value='End Chat'>🚪 End Chat</div></div>" |
|
|
|
|
|
elif current_state == "awaiting_course_for_college_search": |
|
|
course_titles = [c['course'] for c in COURSE_DATA] |
|
|
best_match, score, _ = process.extractOne(msg_lower, course_titles, scorer=fuzz.token_set_ratio, processor=preprocess_text) |
|
|
|
|
|
if score > 85: |
|
|
convo["course_for_college_search"] = best_match |
|
|
convo["state"] = "awaiting_pincode" |
|
|
bot_response = f"Okay, searching for colleges offering '<b>{best_match}</b>'. Please provide your 6-digit area PIN code." |
|
|
else: |
|
|
bot_response = "I couldn't find a clear match for that course. Could you please try rephrasing or be more specific?" |
|
|
convo["state"] = "awaiting_course_for_college_search" |
|
|
|
|
|
elif current_state == "awaiting_pincode": |
|
|
if re.match(r"^\d{6}$", msg): |
|
|
course_name = convo.get("course_for_college_search", "this course") |
|
|
bot_response, _ = find_nearby_colleges(course_name, msg) |
|
|
bot_response += "<div class='quick-reply-container'><div class='quick-reply-button clickable-card' data-action='quick_reply' data-value='College Location Finder'>🔎 Search Again</div><div class='quick-reply-button clickable-card' data-action='quick_reply' data-value='End Chat'>🚪 End Chat</div></div>" |
|
|
convo["state"] = "awaiting_initial_action" |
|
|
else: |
|
|
bot_response = "That doesn't seem like a valid 6-digit PIN code. Please try again." |
|
|
|
|
|
elif current_state == "awaiting_more_details": |
|
|
recs = convo.get("last_recommendations", []) |
|
|
if msg_lower == 'end chat': |
|
|
bot_response = "Sure. Would you like to leave some feedback about your experience?<div class='quick-reply-container'><div class='quick-reply-button clickable-card' data-action='quick_reply' data-value='Yes'>👍 Yes</div><div class='quick-reply-button clickable-card' data-action='quick_reply' data-value='No'>👎 No</div></div>" |
|
|
convo['state'] = 'awaiting_end_confirmation' |
|
|
elif msg_lower == 'compare': |
|
|
bot_response = format_comparison(recs) |
|
|
bot_response += "<div class='quick-reply-container'><div class='quick-reply-button clickable-card' data-action='quick_reply' data-value='End Chat'>🚪 End Chat</div></div>" |
|
|
else: |
|
|
chosen_course = None |
|
|
if msg.isdigit() and 1 <= int(msg) <= len(recs): |
|
|
chosen_course = recs[int(msg) - 1] |
|
|
else: |
|
|
course_titles = [r.get('course', '') for r in recs] |
|
|
best_match, score, _ = process.extractOne(msg, course_titles, scorer=fuzz.ratio) |
|
|
if score > 70: chosen_course = next((r for r in recs if r.get('course') == best_match), None) |
|
|
|
|
|
if chosen_course: |
|
|
bot_response = format_course_details(chosen_course) |
|
|
convo["course_for_college_search"] = chosen_course.get('course') |
|
|
bot_response += "<br><br>Would you like to find nearby colleges for this course?<div class='quick-reply-container'><div class='quick-reply-button clickable-card' data-action='quick_reply' data-value='Yes'>👍 Yes</div><div class='quick-reply-button clickable-card' data-action='quick_reply' data-value='No'>👎 No</div></div>" |
|
|
convo['state'] = 'awaiting_college_search_confirmation' |
|
|
else: |
|
|
bot_response = "Sorry, I didn't recognize that selection. Please choose an option from your recommendations." |
|
|
|
|
|
elif current_state == 'awaiting_college_search_confirmation': |
|
|
if 'yes' in msg_lower: |
|
|
bot_response = "Great! Please provide your 6-digit PIN code." |
|
|
convo['state'] = 'awaiting_pincode' |
|
|
else: |
|
|
bot_response = "No problem. You can explore other recommendations, compare courses, or select a new feature from the left panel." |
|
|
bot_response += "<div class='quick-reply-container'><div class='quick-reply-button clickable-card' data-action='quick_reply' data-value='End Chat'>🚪 End Chat</div></div>" |
|
|
convo['state'] = 'awaiting_more_details' |
|
|
|
|
|
elif current_state == 'awaiting_end_confirmation': |
|
|
if 'yes' in msg_lower: |
|
|
bot_response = "I'd love to hear your thoughts. How was your experience?" |
|
|
convo['state'] = 'awaiting_feedback' |
|
|
else: |
|
|
bot_response = "No problem! It was great helping you." |
|
|
bot_response += "<div class='quick-reply-container'><div class='quick-reply-button clickable-card' data-action='restart'>🔄 Start Over</div></div>" |
|
|
convo['state'] = 'session_ended' |
|
|
|
|
|
elif current_state == 'awaiting_feedback': |
|
|
logging.info(f"FEEDBACK: {msg}") |
|
|
bot_response = "Thank you for your feedback!" |
|
|
bot_response += "<div class='quick-reply-container'><div class='quick-reply-button clickable-card' data-action='restart'>🔄 Start Over</div></div>" |
|
|
convo['state'] = 'session_ended' |
|
|
|
|
|
logging.info(f"BOT: {re.sub('<[^<]+?>', ' ', bot_response).strip()}") |
|
|
return jsonify({"response": bot_response, "conversation": convo}) |
|
|
|
|
|
if __name__ == "__main__": |
|
|
app.run(debug=True) |
