Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline # Placeholder for your chosen LLM integration strategy
+import os # For potential API keys
+
+# --- KAIZEN'S CORE CONFIGURATION ---
+# Esteemed colleagues, this is where you'd integrate the Cognitive Core (LLM).
+# For this demonstration, we use illustrative placeholders.
+# The true EvoForge Prime would connect to a distributed network of specialized AI agents.
+
+# Option 1: Local Pipeline (Requires powerful hardware or smaller models in HF Space)
+# COGNITIVE_CORE_PIPELINE = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.2", device_map="auto")
+
+# Option 2: Hugging Face Inference API (More scalable for public Spaces)
+# HF_TOKEN = os.getenv("HF_TOKEN") # Ensure HF_TOKEN is in Space Secrets
+# from huggingface_hub import InferenceClient
+# COGNITIVE_CORE_CLIENT = InferenceClient(token=HF_TOKEN)
+# DEFAULT_MODEL_ID = "mistralai/Mistral-7B-Instruct-v0.2" # Or your preferred model
+
+def invoke_cognitive_core(prompt_text, task_description="generation", num_sequences=1, model_id=None):
+    """
+    A centralized function to interact with our LLM.
+    This is where the magic of actual LLM interaction would happen.
+    Replace this with your chosen LLM interaction method.
+    """
+    print(f"\n[KAIZEN LOG] Task: {task_description}")
+    print(f"[KAIZEN LOG] Invoking Cognitive Core with Prompt:\n{prompt_text[:300]}...\n") # Log a snippet
+
+    # --- !!! ACTUAL LLM INTEGRATION POINT !!! ---
+    # This section needs to be replaced with real LLM calls.
+    # Example using a placeholder for now:
+    if task_description == "initial_solution_generation":
+        responses = [f"// EvoForge Candidate Solution {i+1} (Simulated)\n// Problem: {prompt_text[:50]}...\n// Approach: Employing principle of {['recursion', 'iteration', 'divide and conquer', 'dynamic programming'][i%4]}." for i in range(num_sequences)]
+        return responses
+    elif task_description == "solution_evaluation":
+        # In reality, you'd parse score and detailed critique
+        score = len(prompt_text) % 10 + 1 # Dummy score
+        critique = f"EvoForge Analysis: Candidate exhibits potential. Clarity: {score-1}/10. Perceived Efficiency: {score}/10. Novelty: {(score+1)%10}/10. Overall Score: {score}/10."
+        return critique, score
+    elif task_description == "solution_refinement":
+        return f"// EvoForge Synthesized Advancement (Simulated)\n// Original concept: {prompt_text[:60]}...\n// Refinement: Optimized data structures and streamlined control flow for enhanced performance and elegance."
+    else:
+        return [f"Cognitive Core Response to: {prompt_text[:70]}... (Simulation)"]
+    # --- END LLM INTEGRATION POINT ---
+
+
+# --- EVOFORGE PRIME - CORE LOGIC MODULE ---
+def orchestrate_evolutionary_cycle(problem_domain, problem_statement, cognitive_catalysts, divergence_factor):
+    if not problem_statement:
+        return "A problem statement is the seed of innovation. Please provide one.", "", "", "", ""
+
+    master_log = f"## EvoForge Prime - Cycle Report ##\n\n"
+    master_log += f"**Domain:** {problem_domain}\n"
+    master_log += f"**Problem Statement:** {problem_statement}\n"
+    master_log += f"**Cognitive Catalysts (Hints):** {cognitive_catalysts if cognitive_catalysts else 'None provided'}\n"
+    master_log += f"**Initial Solution Divergence Factor:** {divergence_factor}\n\n"
+
+    # STAGE 1: Algorithmic Genesis - Generating Diverse Solution Candidates
+    master_log += "### Stage 1: Algorithmic Genesis ###\n"
+    genesis_prompt = f"""
+    As an advanced AI algorithm designer, address the following problem:
+    Problem Domain: {problem_domain}
+    Problem Statement: "{problem_statement}"
+    Consider these initial thoughts/constraints: "{cognitive_catalysts if cognitive_catalysts else 'N/A'}"
+
+    Generate {divergence_factor} distinct and innovative algorithmic solutions or high-level approaches.
+    For each, provide a conceptual outline or pseudo-code.
+    """
+    initial_candidates_raw = invoke_cognitive_core(genesis_prompt, "initial_solution_generation", num_sequences=divergence_factor)
+    master_log += f"Invoked Cognitive Core for {divergence_factor} initial candidates.\n"
+    
+    if not initial_candidates_raw:
+        return master_log + "Error: Cognitive Core failed to generate initial candidates.", "", "", ""
+
+    # STAGE 2: Heuristic Evaluation Matrix - Assessing Candidate Viability
+    master_log += "\n### Stage 2: Heuristic Evaluation Matrix ###\n"
+    evaluated_candidates_display = []
+    evaluated_candidates_data = []
+
+    for idx, candidate_code in enumerate(initial_candidates_raw):
+        evaluation_prompt = f"""
+        Critically evaluate the following algorithmic solution candidate for the problem: "{problem_statement}".
+        Solution Candidate {idx+1}:
+        ```
+        {candidate_code}
+        ```
+        Assess its potential correctness, efficiency, clarity, and novelty. Provide a structured critique and assign an overall viability score from 1 (low) to 10 (high).
+        Format:
+        Critique: [Your detailed analysis]
+        Score: [Score_Value]
+        """
+        critique, score = invoke_cognitive_core(evaluation_prompt, "solution_evaluation")
+        evaluated_candidates_display.append(f"**Candidate {idx+1} (Score: {score}/10):**\n```\n{candidate_code}\n```\n**EvoForge Analysis:** {critique}\n---\n")
+        evaluated_candidates_data.append({"solution": candidate_code, "score": score, "critique": critique, "id": idx+1})
+        master_log += f"Evaluated Candidate {idx+1}. Score: {score}/10.\n"
+
+    if not evaluated_candidates_data:
+        return master_log + "\n".join(evaluated_candidates_display), "Error: No candidates available for evaluation.", "", ""
+
+    # STAGE 3: Apex Selection - Identifying the Most Promising Evolutionary Path
+    master_log += "\n### Stage 3: Apex Selection ###\n"
+    evaluated_candidates_data.sort(key=lambda x: x["score"], reverse=True)
+    apex_candidate_data = evaluated_candidates_data[0]
+    apex_candidate_solution = apex_candidate_data["solution"]
+    apex_candidate_critique = apex_candidate_data["critique"]
+    apex_candidate_score = apex_candidate_data["score"]
+    master_log += f"Apex Candidate (ID: {apex_candidate_data['id']}) selected with score {apex_candidate_score}/10.\n"
+
+    # STAGE 4: Algorithmic Refinement - Synthesizing an Advanced Iteration
+    master_log += "\n### Stage 4: Algorithmic Refinement ###\n"
+    refinement_prompt = f"""
+    Given the problem: "{problem_statement}"
+    And the current leading solution candidate (Score: {apex_candidate_score}/10):
+    ```
+    {apex_candidate_solution}
+    ```
+    And its evaluation: "{apex_candidate_critique}"
+
+    Refine this solution. Enhance its efficiency, elegance, robustness, or explore a novel optimization.
+    Explain the key improvements made in the refined version.
+    """
+    refined_solution_text = invoke_cognitive_core(refinement_prompt, "solution_refinement")
+    master_log += "Invoked Cognitive Core for solution refinement.\n"
+
+    # Prepare outputs for Gradio Interface
+    initial_solutions_output_md = "## Stage 1 & 2: Generated Candidates & Evaluations\n" + "\n".join(evaluated_candidates_display)
+    apex_solution_output_md = f"## Stage 3: Apex Candidate Selection\n**Selected Candidate (ID: {apex_candidate_data['id']}, Score: {apex_candidate_score}/10):**\n```\n{apex_candidate_solution}\n```\n**Original EvoForge Analysis:** {apex_candidate_critique}"
+    refined_solution_output_md = f"## Stage 4: Synthesized Advancement\n**EvoForge Refined Solution:**\n```\n{refined_solution_text}\n```"
+    
+    # Exposing the "thought process" prompts for transparency
+    developer_prompts_log = f"""
+    ## Architect's Blueprint: Simulated Cognitive Core Dialogues ##
+
+    **Objective:** {problem_statement}
+
+    **1. Genesis Prompt Snippet (sent to Cognitive Core):**
+    ```
+    ... Generate {divergence_factor} distinct and innovative algorithmic solutions ...
+    Problem Statement: "{problem_statement[:100]}..."
+    Consider these initial thoughts/constraints: "{str(cognitive_catalysts)[:100]}..."
+    ```
+    *(Generated {len(initial_candidates_raw)} candidates)*
+
+    **2. Evaluation Prompt Snippet (for each candidate, e.g., Candidate 1):**
+    ```
+    Critically evaluate the following algorithmic solution candidate ...
+    Solution Candidate 1:
+    {initial_candidates_raw[0][:150]}...
+    ... Assign an overall viability score ...
+    ```
+    *(Selected Apex Candidate ID: {apex_candidate_data['id']} with score: {apex_candidate_score})*
+    
+    **3. Refinement Prompt Snippet (sent to Cognitive Core):**
+    ```
+    ... Refine this solution. Enhance its efficiency, elegance, robustness ...
+    Leading solution candidate (Score: {apex_candidate_score}/10):
+    {apex_candidate_solution[:150]}...
+    And its evaluation: "{apex_candidate_critique[:100]}..."
+    ```
+    --- End of Blueprint ---
+    """
+
+    return initial_solutions_output_md, apex_solution_output_md, refined_solution_output_md, developer_prompts_log, master_log
+
+# --- EVOFORGE PRIME - GRADIO INTERFACE MANIFEST ---
+interface_header = """
+# EvoForge Prime: Conceptual Algorithmic Evolution Engine
+### *Crafted by Aelius Kaizen, Architect of Tomorrow's Algorithms*
+
+**Inspired by the principles demonstrated by Google DeepMind's AlphaEvolve, EvoForge Prime offers a glimpse into AI-driven algorithmic discovery and refinement.**
+This is a *conceptual demonstration* using simulated or actual LLM interactions (if configured) to mimic an evolutionary process:
+1.  **Genesis:** Diverse solutions are ideated by a Cognitive Core (LLM).
+2.  **Evaluation:** Each candidate is critically analyzed (by an LLM or heuristic).
+3.  **Apex Selection:** The most promising candidate is identified.
+4.  **Refinement:** The Cognitive Core attempts to enhance the apex candidate.
+
+**This is a blueprint, a thought experiment made interactive. The actual AlphaEvolve is a vastly complex, proprietary system.**
+"""
+
+with gr.Blocks(theme=gr.themes.Glass(primary_hue="blue", secondary_hue="cyan"), title="EvoForge Prime") as evo_forge_interface:
+    gr.Markdown(interface_header)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("## I. Define the Algorithmic Frontier")
+            input_problem_domain = gr.Dropdown(
+                ["Python Code Optimization", "Abstract Algorithm Design", "Mathematical Logic Puzzle", "Data Structure Innovation", "General Heuristic Improvement"],
+                label="Select Problem Domain",
+                value="Python Code Optimization"
+            )
+            input_problem_statement = gr.Textbox(
+                lines=6,
+                label="Articulate the Problem or Goal",
+                placeholder="e.g., 'Design a Python function to efficiently find the k-th smallest element in an unsorted list.' or 'Propose a novel heuristic for the Traveling Salesperson Problem.'"
+            )
+            input_cognitive_catalysts = gr.Textbox(
+                lines=3,
+                label="Seed with Cognitive Catalysts (Optional Hints / Constraints)",
+                placeholder="e.g., 'Prioritize space complexity.' or 'Consider a non-comparison-based sort for a sub-problem.'"
+            )
+            input_divergence_factor = gr.Slider(1, 5, value=3, step=1, label="Initial Solution Divergence (Number of Candidates)")
+            
+            launch_evolution_btn = gr.Button("🚀 Initiate EvoForge Cycle", variant="primary")
+
+        with gr.Column(scale=2):
+            gr.Markdown("## II. Evolutionary Trajectory & Artifacts")
+            with gr.Tabs():
+                with gr.TabItem("🧬 Genesis & Evaluation Matrix"):
+                    output_initial_candidates = gr.Markdown(label="Primary Solution Candidates & Heuristic Analysis")
+                with gr.TabItem("🌟 Apex Candidate"):
+                    output_apex_candidate = gr.Markdown(label="Selected Evolutionary Path (Input to Refinement Stage)")
+                with gr.TabItem("💡 Synthesized Advancement"):
+                    output_refined_solution = gr.Markdown(label="Refined Algorithmic Construct")
+                with gr.TabItem("📜 Architect's Blueprint (LLM Dialogues)"):
+                    output_developer_prompts = gr.Markdown(label="Simulated/Actual Prompts to Cognitive Core")
+                with gr.TabItem("📋 Cycle Report"):
+                    output_master_log = gr.Markdown(label="Comprehensive Log of the Evolutionary Cycle")
+
+    launch_evolution_btn.click(
+        orchestrate_evolutionary_cycle,
+        inputs=[input_problem_domain, input_problem_statement, input_cognitive_catalysts, input_divergence_factor],
+        outputs=[output_initial_candidates, output_apex_candidate, output_refined_solution, output_developer_prompts, output_master_log]
+    )
+
+    gr.Markdown("---")
+    gr.Markdown("**Aelius Kaizen's Disclaimer:** EvoForge Prime is a conceptual instrument designed to illuminate the *potential* of AI in algorithmic innovation. The 'Cognitive Core' (LLM) responses are probabilistic and serve as creative springboards, not definitive solutions. True algorithmic breakthroughs require rigorous mathematical proof, empirical validation, and the irreplaceable spark of human ingenuity complementing AI's prowess. This is but a humble step on a grand journey.")
+
+# To launch this marvel locally: python your_script_name.py
+# For Hugging Face Spaces, this file is 'app.py'. Include 'requirements.txt'.
+if __name__ == "__main__":
+    evo_forge_interface.launch(share=False) # Set share=True for a public link if running locally