Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
+
+# 1. Load the Brain
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+def analyze_trinity(text_a, label_a, text_b, label_b, text_c, label_c):
+    # FALLBACK LABELS (If user leaves them blank)
+    if not label_a.strip(): label_a = "Input A"
+    if not label_b.strip(): label_b = "Input B"
+    if not label_c.strip(): label_c = "Input C"
+    
+    clean_labels = [label_a, label_b, label_c]
+    texts = [text_a.strip(), text_b.strip(), text_c.strip()]
+    
+    # Validation
+    if not any(texts):
+        return pd.DataFrame(), "Waiting for signals...", "Waiting for signals..."
+
+    # --- PART 1: THE ALIGNMENT MATRIX (With Custom Names) ---
+    # We use embeddings to calculate how 'close' each text is to the others
+    embeddings = model.encode(texts)
+    matrix = cosine_similarity(embeddings)
+    
+    # Create the DataFrame with YOUR custom names
+    df_matrix = pd.DataFrame(matrix, columns=clean_labels, index=clean_labels)
+    df_matrix = df_matrix.round(3)
+
+    # --- PART 2: FINGERPRINTS (Unique Vibe) ---
+    # TF-IDF to find words unique to each specific input
+    try:
+        tfidf = TfidfVectorizer(stop_words='english')
+        tfidf_matrix = tfidf.fit_transform(texts)
+        feature_names = np.array(tfidf.get_feature_names_out())
+        
+        signatures = ""
+        for i, label in enumerate(clean_labels):
+            if not texts[i]: continue # Skip empty boxes
+            
+            row = tfidf_matrix[i].toarray().flatten()
+            top_indices = row.argsort()[-5:][::-1]
+            top_words = feature_names[top_indices]
+            
+            # Only keep words with actual weight
+            valid_words = [w for w, idx in zip(top_words, top_indices) if row[idx] > 0]
+            
+            signatures += f"🔹 {label.upper()}: {', '.join(valid_words)}\n"
+            
+    except:
+        signatures = "Not enough data for fingerprinting."
+
+    # --- PART 3: THE CONSENSUS (Universal vs Majority) ---
+    vectorizer = CountVectorizer(stop_words='english')
+    try:
+        dtm = vectorizer.fit_transform(texts)
+        vocab = vectorizer.get_feature_names_out()
+        presence = (dtm.toarray() > 0).astype(int)
+        
+        # UNIVERSAL: Present in all 3 (Sum = 3)
+        univ_indices = np.where(presence.sum(axis=0) == 3)[0]
+        univ_words = vocab[univ_indices]
+        
+        # MAJORITY: Present in 2 (Sum = 2)
+        maj_indices = np.where(presence.sum(axis=0) == 2)[0]
+        maj_words = vocab[maj_indices]
+        
+        consensus_text = ""
+        
+        if len(univ_words) > 0:
+            consensus_text += f"🔥 UNIVERSAL TRUTH (3/3): {', '.join(univ_words)}\n\n"
+        else:
+            consensus_text += "❌ NO UNIVERSAL TRUTH FOUND.\n\n"
+            
+        if len(maj_words) > 0:
+            consensus_text += f"⚠️ MAJORITY REPORT (2/3): {', '.join(maj_words)}"
+        else:
+            consensus_text += "⚠️ NO PARTIAL ALIGNMENT FOUND."
+            
+    except:
+        consensus_text = "Waiting for more data..."
+
+    return df_matrix, signatures, consensus_text
+
+# --- UI BUILD (Flexible Inputs) ---
+with gr.Blocks(theme=gr.themes.Glass()) as app:
+    gr.Markdown("# 🕸️ THE ARRAY v0.2")
+    gr.Markdown("### FlameTeam Multi-Model Alignment System")
+    
+    with gr.Row():
+        # COLUMN 1
+        with gr.Column():
+            lbl_a = gr.Textbox(label="Label 1", value="Me (Prompt)", placeholder="Name this input...")
+            box_a = gr.TextArea(show_label=False, placeholder="Paste text here...", lines=4)
+        # COLUMN 2
+        with gr.Column():
+            lbl_b = gr.Textbox(label="Label 2", value="Model A", placeholder="Name this input...")
+            box_b = gr.TextArea(show_label=False, placeholder="Paste text here...", lines=4)
+        # COLUMN 3
+        with gr.Column():
+            lbl_c = gr.Textbox(label="Label 3", value="Model B", placeholder="Name this input...")
+            box_c = gr.TextArea(show_label=False, placeholder="Paste text here...", lines=4)
+        
+    btn = gr.Button("RUN THE ARRAY", variant="primary")
+    
+    # RESULTS
+    gr.Markdown("### 1. The Alignment Matrix")
+    out_matrix = gr.Dataframe(label="Cosine Similarity Grid")
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### 2. Unique Fingerprints")
+            out_signatures = gr.Textbox(label="Distinctive Terms (TF-IDF)", lines=5)
+        with gr.Column():
+            gr.Markdown("### 3. The Consensus")
+            out_consensus = gr.Textbox(label="Shared Concepts", lines=5)
+
+    btn.click(analyze_trinity, inputs=[box_a, lbl_a, box_b, lbl_b, box_c, lbl_c], outputs=[out_matrix, out_signatures, out_consensus])
+
+app.launch()