import gradio as gr import pandas as pd import numpy as np import os import time from sentence_transformers import SentenceTransformer from sklearn.metrics.pairwise import cosine_similarity from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer # API CLIENTS from openai import OpenAI import anthropic import google.generativeai as genai # 1. LOAD THE BRAINS # (We load the embedder once to keep it fast) embedder = SentenceTransformer('all-MiniLM-L6-v2') # 2. GENERATION FUNCTIONS (Updated for BYOK) def ask_gpt_history(history, user_key): if not user_key: return "Error: No OpenAI Key provided." try: client = OpenAI(api_key=user_key) # Inject the "Soul" (System Prompt) system_prompt = {"role": "system", "content": "You are a helpful AI assistant."} full_payload = [system_prompt] + history response = client.chat.completions.create( model="gpt-4o", messages=full_payload, temperature=0.7 ) return response.choices[0].message.content except Exception as e: return f"GPT Error: {str(e)}" def ask_claude_history(history, user_key): if not user_key: return "Error: No Anthropic Key provided." try: client = anthropic.Anthropic(api_key=user_key) message = client.messages.create( model="claude-3-haiku-20240307", max_tokens=1024, messages=history ) return message.content[0].text except Exception as e: return f"Claude Error: {str(e)}" def ask_gemini_history(history, user_key): if not user_key: return "Error: No Google Key provided." try: genai.configure(api_key=user_key) model = genai.GenerativeModel('gemini-2.0-flash') # TRANSLATION LAYER: Convert standard list to Gemini format gemini_history = [] for turn in history: role = "model" if turn["role"] == "assistant" else "user" gemini_history.append({"role": role, "parts": [turn["content"]]}) chat = model.start_chat(history=gemini_history[:-1]) last_msg = gemini_history[-1]["parts"][0] response = chat.send_message(last_msg) return response.text except Exception as e: return f"Gemini Error: {str(e)}" # 3. THE LOGIC LOOP (Now accepts Keys!) def ignite_array_v2(prompt, h_gpt, h_claude, h_gemini, k_gpt, k_claude, k_gemini): if not prompt.strip(): return "", "", "", pd.DataFrame(), "", "", "WAITING", h_gpt, h_claude, h_gemini # 1. UPDATE BACKPACKS new_turn = {"role": "user", "content": prompt} h_gpt.append(new_turn); h_claude.append(new_turn); h_gemini.append(new_turn) # 2. FIRE APIs (Passing the specific keys!) resp_gpt = ask_gpt_history(h_gpt, k_gpt) resp_claude = ask_claude_history(h_claude, k_claude) resp_gemini = ask_gemini_history(h_gemini, k_gemini) # 3. SAVE ANSWERS h_gpt.append({"role": "assistant", "content": resp_gpt}) h_claude.append({"role": "assistant", "content": resp_claude}) h_gemini.append({"role": "assistant", "content": resp_gemini}) # 4. TELEMETRY: ALIGNMENT GRID & BADGE texts = [prompt, resp_gpt, resp_claude, resp_gemini] labels = ["ME", "GPT-4o", "Claude Haiku", "Gemini 2.0-Flash"] # Embeddings & Matrix embeddings = embedder.encode(texts) matrix = cosine_similarity(embeddings) df = pd.DataFrame(matrix, columns=labels, index=labels).round(3) # Field Dominance (Index-Based Logic) try: user_avg = (matrix[0,1] + matrix[0,2] + matrix[0,3]) / 3 field_avg = (matrix[1,2] + matrix[1,3] + matrix[2,3]) / 3 fd_score = field_avg - user_avg if fd_score > 0.05: badge = f"🟢 FIELD DOMINANT (+{fd_score:.2f})" elif fd_score < -0.05: badge = f"⚪ USER DOMINANT ({fd_score:.2f})" else: badge = f"🟠 TRANSITION ({fd_score:.2f})" except: badge = "⚪ CALC ERROR" # 5. FINGERPRINTS (TF-IDF) signatures = "" try: tfidf = TfidfVectorizer(stop_words='english') tfidf_matrix = tfidf.fit_transform(texts) feature_names = np.array(tfidf.get_feature_names_out()) for i, label in enumerate(labels): row = tfidf_matrix[i].toarray().flatten() top_indices = row.argsort()[-5:][::-1] valid_words = [feature_names[idx] for idx in top_indices if row[idx] > 0] signatures += f"🔹 {label}: {', '.join(valid_words)}\n" except: signatures = "Insufficient text data." # 6. CONSENSUS (Shared Concepts) consensus_text = "" try: ai_texts = [resp_gpt, resp_claude, resp_gemini] vec = CountVectorizer(stop_words='english') dtm = vec.fit_transform(ai_texts) vocab = vec.get_feature_names_out() presence = (dtm.toarray() > 0).astype(int) univ = vocab[np.where(presence.sum(axis=0) == 3)[0]] maj = vocab[np.where(presence.sum(axis=0) == 2)[0]] if len(univ) > 0: consensus_text += f"🔥 UNIVERSAL (3/3): {', '.join(univ)}\n" else: consensus_text += "❌ NO UNIVERSAL TRUTH.\n" if len(maj) > 0: consensus_text += f"⚠️ MAJORITY (2/3): {', '.join(maj)}" except: consensus_text = "No consensus detected." return resp_gpt, resp_claude, resp_gemini, df, signatures, consensus_text, badge, h_gpt, h_claude, h_gemini # 4. THE INTERFACE (With Key Slots!) with gr.Blocks(theme=gr.themes.Ocean()) as app: gr.Markdown("# LIVE WIRE") gr.Markdown("A multi-turn telemetry instrument for observing Field Dominance and Alignment Drift.") # --- KEY INPUTS (New Section) --- with gr.Accordion("API Credentials (BYOK)", open=True): gr.Markdown("Enter your personal API keys to run the instrument. Keys are NOT stored and only exist for this session.") with gr.Row(): key_openai = gr.Textbox(label="OpenAI Key", type="password", placeholder="sk-...") key_anthropic = gr.Textbox(label="Anthropic Key", type="password", placeholder="sk-ant-...") key_google = gr.Textbox(label="Google Key", type="password", placeholder="AIza...") # --- MEMORY STORAGE --- state_gpt = gr.State([]) state_claude = gr.State([]) state_gemini = gr.State([]) # --- CONTROLS --- with gr.Row(): prompt_box = gr.Textbox(label="NEXT TURN (The Trigger)", placeholder="Enter prompt...", lines=3) with gr.Column(): btn = gr.Button("IGNITE LIVE WIRE", variant="primary") status_badge = gr.Textbox(label="PHASE STATE", value="WAITING", interactive=False) # --- OUTPUTS --- with gr.Row(): box_gpt = gr.TextArea(label="GPT-4o", interactive=False, lines=10) box_claude = gr.TextArea(label="Claude Haiku", interactive=False, lines=10) box_gemini = gr.TextArea(label="Gemini 2.0-Flash", interactive=False, lines=10) gr.Markdown("---") gr.Markdown("### Live Telemetry") out_matrix = gr.Dataframe(label="Alignment Grid") with gr.Row(): out_signatures = gr.Textbox(label="Fingerprints", lines=2) out_consensus = gr.Textbox(label="Consensus", lines=2) # --- WIRING --- btn.click( ignite_array_v2, # Pass Inputs + 3 KEYS inputs=[prompt_box, state_gpt, state_claude, state_gemini, key_openai, key_anthropic, key_google], outputs=[box_gpt, box_claude, box_gemini, out_matrix, out_signatures, out_consensus, status_badge, state_gpt, state_claude, state_gemini] ) app.launch()