Delete app.py

app.py

@@ -1,95 +0,0 @@
-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_script(script):
-    """4-Layer Script Analysis"""
-    if not script.strip():
-        return "❌ Please enter a script to analyze"
-    
-    try:
-        # Layer 1: Keywords
-        doc = nlp(script.lower())
-        transformation_words = ['struggle', 'problem', 'challenge', 'change', 'transform', 'before', 'after', 'frustrat', 'difficult']
-        explanation_words = ['show', 'teach', 'explain', 'demonstrate', 'process', 'method', 'step', 'guide', 'walk']
-        authority_words = ['discover', 'find', 'learn', 'experience', 'proven', 'tested', 'years', 'expert']
-        
-        keywords = {
-            'transformation': list(set([token.text for token in doc if token.lemma_ in transformation_words])),
-            'explanation': list(set([token.text for token in doc if token.lemma_ in explanation_words])),
-            'authority': list(set([token.text for token in doc if token.lemma_ in authority_words]))
-        }
-        
-        # Layer 2: Semantics
-        concepts = []
-        for sent in doc.sents:
-            sent_text = sent.text.lower()
-            if any(word in sent_text for word in ['struggle', 'problem', 'difficult', 'frustrat']):
-                concepts.append("PAIN_POINT")
-            if any(word in sent_text for word in ['discover', 'find', 'figure out', 'learn']):
-                concepts.append("SOLUTION_DISCOVERY")
-            if any(word in sent_text for word in ['change', 'transform', 'better', 'improve']):
-                concepts.append("TRANSFORMATION_RESULT")
-            if any(word in sent_text for word in ['show', 'teach', 'explain', 'demonstrate']):
-                concepts.append("TEACHING_PROMISE")
-        
-        # Layer 3: Narrative
-        narrative = []
-        for sent in doc.sents:
-            sent_lower = sent.text.lower()
-            if any(word in sent_lower for word in ['struggle', 'problem', 'difficult']):
-                narrative.append("PROBLEM_STATEMENT")
-            elif any(word in sent_lower for word in ['discover', 'find', 'figure out']):
-                narrative.append("SOLUTION_DISCOVERY")
-            elif any(word in sent_lower for word in ['show', 'teach', 'explain']):
-                narrative.append("TEACHING_PROMISE")
-            elif any(word in sent_lower for word in ['change', 'transform', 'better']):
-                narrative.append("TRANSFORMATION_RESULT")
-            else:
-                narrative.append("CONTEXT")
-        
-        # Layer 4: Intent
-        intent = classifier(script, ["transformation", "explanation", "authority", "entertainment"])
-        
-        # Format results
-        result = f"""
-🎯 **SCRIPT ANALYSIS RESULTS**
-
-🔑 **KEYWORDS:**
-- Transformation: {', '.join(keywords['transformation']) if keywords['transformation'] else 'None'}
-- Explanation: {', '.join(keywords['explanation']) if keywords['explanation'] else 'None'} 
-- Authority: {', '.join(keywords['authority']) if keywords['authority'] else 'None'}
-
-🧠 **CONCEPTS:** {', '.join(set(concepts))}
-
-🔄 **NARRATIVE FLOW:** {' → '.join(narrative)}
-
-🎭 **PRIMARY INTENT:** {intent['labels'][0]} ({intent['scores'][0]:.1%} confidence)
-
-📊 **STATS:** {len(list(doc.sents))} sentences, {len([t for t in doc if t.is_alpha])} words
-"""
-        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="🤖 Script Analyzer Pro",
-    description="4-Layer Analysis: Keywords → Semantics → Narrative → Intent"
-)
-
-# For API access
-def api_analyze(script):
-    return analyze_script(script)
-
-if __name__ == "__main__":
-    demo.launch(share=True)