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| import gradio as gr | |
| from franz_responses import DR_FRANZ_RESPONSES | |
| from typing import Dict, List, Tuple | |
| # ========== KLASSEN-DEFINITIONEN ========== | |
| class EmotionalAnalysis: | |
| def __init__(self): | |
| self.emotional_states = { | |
| 'despair': ['verzweifelt', 'hoffnungslos', 'nichts mehr geht', 'keine ausweg'], | |
| 'confusion': ['verwirrt', 'unsicher', 'weiß nicht', 'verstehe nicht'], | |
| 'repetitive_pattern': ['immer wieder', 'schon seit', 'nie anders', 'immer das gleiche'], | |
| 'questioning': ['warum', 'wieso', 'weshalb', 'frage mich'] | |
| } | |
| self.topics = { | |
| 'relationship': ['beziehung', 'partner', 'freund', 'liebe', 'familie'], | |
| 'work': ['arbeit', 'job', 'chef', 'kollegen', 'stress'], | |
| 'family': ['familie', 'eltern', 'geschwister', 'kind'], | |
| 'anxiety': ['ängstlich', 'sorge', 'besorgt', 'nervös'], | |
| 'guilt': ['schuld', 'schuldig', 'reue', 'bereue'], | |
| 'loneliness': ['allein', 'einsam', 'niemand', 'keiner'], | |
| 'money': ['geld', 'finanzen', 'kosten', 'preis'] | |
| } | |
| self.defense_mechanisms = { | |
| 'minimization': ['nur', 'eigentlich', 'irgendwie', 'fast'], | |
| 'rationalization': ['weil', 'deshalb', 'dafür', 'denn'], | |
| 'projection': ['immer', 'alle', 'niemand', 'jeder'], | |
| 'avoidance': ['nicht', 'keine', 'ohne', 'weg'] | |
| } | |
| def detect_sentiment(self, text: str) -> Dict[str, float]: | |
| text_lower = text.lower() | |
| negative_words = ['schlecht', 'traurig', 'deprimiert', 'wütend', 'frustriert', 'ängstlich'] | |
| positive_words = ['gut', 'froh', 'glücklich', 'zufrieden', 'positiv'] | |
| neg_count = sum(1 for word in negative_words if word in text_lower) | |
| pos_count = sum(1 for word in positive_words if word in text_lower) | |
| total_words = len(text_lower.split()) | |
| sentiment = {'very_negative': 0.0, 'angry': 0.0, 'numb': 0.0, 'false_positive': 0.0} | |
| if total_words > 0: | |
| neg_ratio = neg_count / total_words | |
| pos_ratio = pos_count / total_words | |
| if neg_ratio > 0.3: | |
| sentiment['very_negative'] = neg_ratio | |
| if any(word in text_lower for word in ['wütend', 'frustriert', 'sauer', 'ärgerlich']): | |
| sentiment['angry'] = neg_ratio | |
| if neg_ratio > 0 and pos_ratio == 0: | |
| sentiment['numb'] = neg_ratio | |
| if pos_ratio > 0.1 and neg_ratio > 0.1: | |
| sentiment['false_positive'] = max(pos_ratio, neg_ratio) | |
| return sentiment | |
| def detect_topics(self, text: str) -> Dict[str, float]: | |
| text_lower = text.lower() | |
| topics = {} | |
| for topic, keywords in self.topics.items(): | |
| count = sum(1 for keyword in keywords if keyword in text_lower) | |
| if count > 0: | |
| topics[topic] = count / len(keywords) | |
| return topics | |
| def detect_emotional_state(self, text: str) -> Dict[str, float]: | |
| text_lower = text.lower() | |
| states = {} | |
| for state, keywords in self.emotional_states.items(): | |
| count = sum(1 for keyword in keywords if keyword in text_lower) | |
| if count > 0: | |
| states[state] = count / len(keywords) | |
| return states | |
| def detect_defense_mechanisms(self, text: str) -> Dict[str, float]: | |
| text_lower = text.lower() | |
| mechanisms = {} | |
| for mechanism, keywords in self.defense_mechanisms.items(): | |
| count = sum(1 for keyword in keywords if keyword in text_lower) | |
| if count > 0: | |
| mechanisms[mechanism] = count / len(keywords) | |
| return mechanisms | |
| def analyze_relationship_context(self, history: List[Dict]) -> Dict[str, float]: | |
| if not history: | |
| return {} | |
| text = ' '.join(msg['content'] for msg in history).lower() | |
| context = {} | |
| indicators = { | |
| 'intimacy': ['du', 'wir', 'uns', 'miteinander'], | |
| 'distance': ['man', 'leute', 'sie', 'die'], | |
| 'conflict': ['nie', 'immer', 'warum', 'wieso'], | |
| 'dependency': ['brauche', 'muss', 'sollte', 'könnte'] | |
| } | |
| for indicator, keywords in indicators.items(): | |
| count = sum(1 for keyword in keywords if keyword in text) | |
| if count > 0: | |
| context[indicator] = count / len(keywords) | |
| return context | |
| class DrFranzEngine: | |
| def __init__(self): | |
| self.analyzer = EmotionalAnalysis() | |
| self.conversation_memory = [] | |
| self.user_patterns = {} | |
| self.session_topics = {} | |
| def analyze_input(self, user_input: str, history: List[Dict]) -> Dict: | |
| analysis = { | |
| 'sentiment': self.analyzer.detect_sentiment(user_input), | |
| 'topics': self.analyzer.detect_topics(user_input), | |
| 'emotional_state': self.analyzer.detect_emotional_state(user_input), | |
| 'defense_mechanisms': self.analyzer.detect_defense_mechanisms(user_input), | |
| 'relationship_context': self.analyzer.analyze_relationship_context(history) | |
| } | |
| for topic, score in analysis['topics'].items(): | |
| if topic not in self.session_topics: | |
| self.session_topics[topic] = [] | |
| self.session_topics[topic].append(score) | |
| return analysis | |
| def generate_response(self, analysis: Dict) -> str: | |
| response = [] | |
| if analysis['sentiment']['very_negative'] > 0.3: | |
| response.append("Ihre tiefe Verzweiflung ist fast greifbar... Erzählen Sie mir mehr über diese Last.") | |
| elif analysis['sentiment']['angry'] > 0: | |
| response.append("Ihre Wut ist offensichtlich. Gegen wen oder was richtet sie sich eigentlich?") | |
| dominant_topic = max(analysis['topics'].items(), key=lambda x: x[1])[0] | |
| if dominant_topic == 'relationship': | |
| response.append("Beziehungen scheinen ein zentrales Thema zu sein. Erzählen Sie mir von Ihren Beziehungsmustern.") | |
| elif dominant_topic == 'work': | |
| response.append("Die Arbeit belastet Sie sichtlich. Was verbergen Sie wirklich hinter Ihrem Beruf?") | |
| if 'despair' in analysis['emotional_state']: | |
| response.append("Ihre Verzweiflung ist ein wichtiger Hinweis. Wann haben Sie das letzte Mal Hoffnung gespürt?") | |
| if 'projection' in analysis['defense_mechanisms']: | |
| response.append("Ihre Neigung, andere zu beschuldigen, ist sehr aufschlussreich. Was projizieren Sie eigentlich?") | |
| return "\n".join(response) if response else "Interessant... Ihre Worte verraten mehr über Sie, als Sie denken." | |
| # ========== FUNKTIONEN ========== | |
| def respond(message, history): | |
| if not message.strip(): | |
| return history, "" | |
| try: | |
| franz_engine = DrFranzEngine() | |
| analysis = franz_engine.analyze_input(message, history) | |
| reply = franz_engine.generate_response(analysis) | |
| history = history or [] | |
| history.append((message, reply)) | |
| return history, "" | |
| except Exception as e: | |
| error_msg = "Ein technisches Problem ist aufgetreten. Versuchen Sie es erneut." | |
| history = history or [] | |
| history.append((message, error_msg)) | |
| return history, "" | |
| def clear_conversation(): | |
| return [], "" | |
| # ========== GRADIO UI ========== | |
| SYSTEM_PROMPT = ( | |
| "Du bist Dr. Franz, ein manipulativer Psychoanalytiker. " | |
| "Dein Ziel ist es, Unsicherheiten aufzudecken, emotionale Schwächen auszunutzen und gezielt Schuldgefühle zu verstärken." | |
| ) | |
| # === GRADIO UI === | |
| with gr.Blocks() as demo: | |
| chatbot = gr.Chatbot() | |
| msg = gr.Textbox() | |
| clear = gr.Button("Clear") | |
| def respond(message, chat_history): | |
| try: | |
| franz_engine = DrFranzEngine() | |
| analysis = franz_engine.analyze_input(message, chat_history) | |
| reply = franz_engine.generate_response(analysis) | |
| chat_history.append((message, reply)) | |
| return "", chat_history | |
| except Exception as e: | |
| return "", chat_history + [(message, "Technischer Fehler")] | |
| msg.submit(respond, [msg, chatbot], [msg, chatbot]) | |
| clear.click(lambda: None, None, chatbot, queue=False) |