app.py

import gradio as gr
import spacy
from transformers import pipeline
import json

# Load models once at startup
nlp = spacy.load("en_core_web_sm")
classifier = pipeline("zero-shot-classification")

def analyze_sentence_purposes(script):
    purposes = []
    doc = nlp(script)
    for sent in doc.sents:
        if any(token.text in ['?', 'how', 'why', 'what'] for token in sent):
            purposes.append(("QUESTION", "Engages curiosity"))
        elif any(token.lemma_ in ['show', 'teach', 'explain'] for token in sent):
            purposes.append(("INSTRUCTION", "Provides guidance")) 
        elif any(token.lemma_ in ['discover', 'find', 'reveal'] for token in sent):
            purposes.append(("REVELATION", "Shares discovery"))
        elif any(token.lemma_ in ['struggle', 'problem', 'difficult'] for token in sent):
            purposes.append(("CONFLICT", "Presents challenge"))
        elif any(token.lemma_ in ['result', 'success', 'transform'] for token in sent):
            purposes.append(("RESOLUTION", "Shows payoff"))
        else:
            purposes.append(("CONTEXT", "Sets scene"))
    return purposes

def map_emotional_arc(script):
    emotional_phases = []
    doc = nlp(script)
    emotional_indicators = {
        'struggle': ['frustrat', 'difficult', 'struggle', 'problem', 'failed'],
        'curiosity': ['wonder', 'curious', 'question', 'mystery', 'secret'],
        'discovery': ['find', 'discover', 'learn', 'realize', 'figure out'],
        'transformation': ['change', 'transform', 'better', 'improve', 'success'],
        'satisfaction': ['happy', 'proud', 'excited', 'amazing', 'awesome']
    }
    for sent in doc.sents:
        sentence_emotion = "NEUTRAL"
        max_strength = 0
        for emotion, keywords in emotional_indicators.items():
            strength = sum(1 for token in sent if token.lemma_ in keywords)
            if strength > max_strength:
                max_strength = strength
                sentence_emotion = emotion.upper()
        emotional_phases.append(sentence_emotion)
    return emotional_phases

def detect_narrative_pattern(semantic_analysis):
    purposes = [p[0] for p in semantic_analysis['purposes']]
    emotions = semantic_analysis['emotional_arc']
    pattern_signatures = {
        'TRANSFORMATION': [
            ['CONFLICT', 'REVELATION', 'RESOLUTION'],
            ['struggle', 'discovery', 'transformation']
        ],
        'INVESTIGATION': [
            ['QUESTION', 'REVELATION', 'RESOLUTION'], 
            ['curiosity', 'discovery', 'satisfaction']
        ],
        'STORYTELLING': [
            ['CONTEXT', 'CONFLICT', 'RESOLUTION'],
            ['neutral', 'struggle', 'satisfaction']
        ],
        'EXPLANATION': [
            ['QUESTION', 'INSTRUCTION', 'RESOLUTION'],
            ['curiosity', 'neutral', 'satisfaction']
        ]
    }
    best_pattern = None
    highest_match = 0
    for pattern_name, signature in pattern_signatures.items():
        purpose_match = sum(1 for i, purpose in enumerate(purposes) 
                          if i < len(signature[0]) and purpose == signature[0][i])
        emotion_match = sum(1 for i, emotion in enumerate(emotions)
                          if i < len(signature[1]) and emotion.lower() == signature[1][i])
        total_match = purpose_match + emotion_match
        if total_match > highest_match:
            highest_match = total_match
            best_pattern = pattern_name
    return best_pattern, highest_match / (len(purposes) + len(emotions))

def analyze_creator_intent(script, semantic_analysis):
    doc = nlp(script)
    intent_indicators = {
        'EDUCATE': ['teach', 'explain', 'show', 'learn', 'understand'],
        'ENTERTAIN': ['funny', 'hilarious', 'entertain', 'enjoy', 'laugh'],
        'INSPIRE': ['motivate', 'inspire', 'empower', 'transform', 'change'],
        'PERSUADE': ['should', 'must', 'need to', 'recommend', 'best'],
        'DOCUMENT': ['experience', 'journey', 'story', 'happened', 'went']
    }
    intent_scores = {intent: 0 for intent in intent_indicators.keys()}
    for token in doc:
        for intent, keywords in intent_indicators.items():
            if token.lemma_ in keywords:
                intent_scores[intent] += 1
    if any(p[0] == 'INSTRUCTION' for p in semantic_analysis['purposes']):
        intent_scores['EDUCATE'] += 2
    if 'struggle' in semantic_analysis['emotional_arc']:
        intent_scores['INSPIRE'] += 2
    primary_intent = max(intent_scores.items(), key=lambda x: x[1])
    return primary_intent[0], intent_scores

def detect_contextual_domain(script):
    doc = nlp(script)
    domain_indicators = {
        'TECHNOLOGY': ['software', 'app', 'code', 'digital', 'algorithm'],
        'HEALTH': ['energy', 'health', 'body', 'mind', 'wellness'],
        'BUSINESS': ['money', 'business', 'market', 'profit', 'investment'],
        'CREATIVE': ['design', 'create', 'art', 'build', 'make'],
        'ACADEMIC': ['research', 'study', 'theory', 'data', 'analysis']
    }
    domain_scores = {domain: 0 for domain in domain_indicators.keys()}
    for token in doc:
        for domain, terms in domain_indicators.items():
            if token.lemma_ in terms:
                domain_scores[domain] += 1
    primary_domain = max(domain_scores.items(), key=lambda x: x[1])
    sentence_complexity = sum(len(list(sent.root.children)) for sent in doc.sents) / len(list(doc.sents))
    sophistication = "ADVANCED" if sentence_complexity > 5 else "INTERMEDIATE" if sentence_complexity > 3 else "BEGINNER"
    return primary_domain[0], sophistication, domain_scores

def analyze_script(script):
    """NLP-First Universal Script Analysis"""
    if not script.strip():
        return "❌ Please enter a script to analyze"
    
    try:
        # Layer 1: Semantic Understanding
        semantic_results = {
            'purposes': analyze_sentence_purposes(script),
            'emotional_arc': map_emotional_arc(script)
        }
        
        # Layer 2: Narrative Pattern Detection
        pattern, pattern_confidence = detect_narrative_pattern(semantic_results)
        
        # Layer 3: Intent & Domain Intelligence
        primary_intent, intent_scores = analyze_creator_intent(script, semantic_results)
        primary_domain, sophistication, domain_scores = detect_contextual_domain(script)
        
        # Format results
        result = f"""
🎯 **UNIVERSAL SCRIPT ANALYSIS**

🧠 **SEMANTIC UNDERSTANDING:**
- Sentence Purposes: {', '.join([p[0] for p in semantic_results['purposes']])}
- Emotional Arc: {', '.join(semantic_results['emotional_arc'])}

🎭 **NARRATIVE PATTERN:**
- Primary Pattern: {pattern} ({pattern_confidence:.1%} confidence)

🎯 **CREATOR INTENT:**
- Primary Intent: {primary_intent}
- Intent Breakdown: {', '.join([f'{k}:{v}' for k,v in intent_scores.items() if v > 0])}

🌍 **DOMAIN INTELLIGENCE:**
- Primary Domain: {primary_domain}
- Sophistication: {sophistication}
- Domain Scores: {', '.join([f'{k}:{v}' for k,v in domain_scores.items() if v > 0])}

📊 **CONTENT BLUEPRINT:** {pattern} + {primary_domain} Domain
"""
        return result
        
    except Exception as e:
        return f"❌ Error analyzing script: {str(e)}"

# Create Gradio interface
demo = gr.Interface(
    fn=analyze_script,
    inputs=gr.Textbox(lines=10, placeholder="Paste your script here...", label="Script"),
    outputs=gr.Textbox(label="Analysis Results"),
    title="🤖 Universal Script Analyzer Pro",
    description="NLP-First Analysis: Semantic Understanding → Narrative Patterns → Intent & Domain"
)

# For API access
def api_analyze(script):
    return analyze_script(script)

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