Create app.py

app.py

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+import os
+import time
+import gradio as gr
+import requests
+import json
+import numpy as np
+import google.generativeai as genai
+from openai import OpenAI
+from typing import List, Dict, Tuple
+from sklearn.metrics.pairwise import cosine_similarity
+from sentence_transformers import SentenceTransformer
+
+class AGICognitiveSystem:
+    def __init__(self):
+        self.api_keys = {
+            "GEMINI": os.environ.get("GEMINI_API_KEY"),
+            "MISTRAL": os.environ.get("MISTRAL_API_KEY"),
+            "OPENROUTER": os.environ.get("OPENROUTER_API_KEY"),
+            "AZURE": os.environ.get("AZURE_API_KEY")
+        }
+        self.validate_keys()
+        
+        # Initialize models and cognitive components
+        self.init_models()
+        self.init_cognitive_modules()
+        self.init_knowledge_graph()
+        
+        # Initialize sentence transformer for semantic analysis
+        self.sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
+        
+        # Cognitive configuration
+        self.cognitive_config = {
+            "depth": 5,  # Levels of recursive reasoning
+            "temperature_strategy": "adaptive",
+            "confidence_threshold": 0.85,
+            "max_retries": 3,
+            "metacognition_interval": 2
+        }
+        
+        self.thought_history = []
+        self.cognitive_metrics = {
+            "processing_time": [],
+            "confidence_scores": [],
+            "error_rates": []
+        }
+
+    def validate_keys(self):
+        for key, value in self.api_keys.items():
+            if not value:
+                raise ValueError(f"Missing API key: {key}")
+
+    def init_models(self):
+        """Initialize all AI models with specialized roles"""
+        # Google Gemini
+        genai.configure(api_key=self.api_keys["GEMINI"])
+        self.gemini = genai.GenerativeModel(
+            "gemini-2.0-pro-exp-02-05",
+            generation_config={"temperature": 0.5, "max_output_tokens": 8192}
+        )
+        
+        # Azure GPT-4o
+        self.gpt4o = OpenAI(
+            base_url="https://models.inference.ai.azure.com",
+            api_key=self.api_keys["AZURE"]
+        )
+        
+        # Model registry with specialized roles
+        self.model_registry = {
+            "intuition": "mistral-large-latest",
+            "analysis": "gpt-4o",
+            "critique": "meta-llama/llama-3.3-70b-instruct:free",
+            "creativity": "gemini-2.0-pro-exp-02-05",
+            "validation": "deepseek/deepseek-chat:free",
+            "metacognition": "gpt-4o",
+            "emotional_intelligence": "qwen/qwen-vl-plus:free"
+        }
+
+    def init_cognitive_modules(self):
+        """Initialize specialized cognitive processors"""
+        self.modules = {
+            "working_memory": [],
+            "long_term_memory": [],
+            "emotional_context": {"valence": 0.5, "arousal": 0.5},
+            "error_correction": [],
+            "metacognition_stack": []
+        }
+
+    def init_knowledge_graph(self):
+        """Initialize semantic knowledge network"""
+        self.knowledge_graph = {
+            "nodes": [],
+            "edges": [],
+            "embeddings": np.array([])
+        }
+
+    def cognitive_flow(self, query: str) -> Tuple[str, dict]:
+        """Multi-layered cognitive processing pipeline"""
+        try:
+            # Stage 1: Perception & Contextualization
+            context = self.perceive_context(query)
+            
+            # Stage 2: Core Reasoning Process
+            solutions = self.recursive_reasoning(query, context)
+            
+            # Stage 3: Emotional Alignment
+            emotionally_aligned = self.apply_emotional_intelligence(solutions)
+            
+            # Stage 4: Metacognitive Review
+            validated = self.metacognitive_review(emotionally_aligned)
+            
+            # Stage 5: Knowledge Integration
+            self.update_knowledge_graph(query, validated)
+            
+            return validated, {
+                "reasoning_steps": self.thought_history[-5:],
+                "confidence": self.calculate_confidence(validated),
+                "semantic_coherence": self.analyze_coherence(validated)
+            }
+            
+        except Exception as e:
+            self.handle_error(e)
+            return "Cognitive processing failed", {}
+
+    def recursive_reasoning(self, query: str, context: dict, depth: int = 0) -> List[dict]:
+        """Deep recursive reasoning with backtracking"""
+        if depth >= self.cognitive_config["depth"]:
+            return []
+
+        # Generate initial hypotheses
+        hypotheses = self.generate_hypotheses(query, context)
+        
+        # Evaluate hypotheses
+        evaluated = []
+        for hypothesis in hypotheses:
+            analysis = self.analyze_hypothesis(hypothesis, context)
+            critique = self.critique_analysis(analysis)
+            
+            if self.evaluate_critique(critique):
+                refined = self.refine_hypothesis(hypothesis, critique)
+                evaluated.append({
+                    "hypothesis": refined,
+                    "confidence": self.calculate_confidence(refined),
+                    "depth": depth
+                })
+                # Recursive deepening
+                evaluated += self.recursive_reasoning(refined, context, depth+1)
+        
+        return self.rank_solutions(evaluated)
+
+    def generate_hypotheses(self, query: str, context: dict) -> List[str]:
+        """Generate potential solutions using multiple models"""
+        hypotheses = []
+        
+        # Intuitive generation
+        hypotheses.append(self.call_model(
+            "intuition",
+            f"Generate intuitive hypothesis for: {query}",
+            context
+        ))
+        
+        # Analytical generation
+        hypotheses.append(self.call_model(
+            "analysis",
+            f"Generate analytical solution for: {query}",
+            context
+        ))
+        
+        # Creative generation
+        hypotheses.append(self.call_model(
+            "creativity",
+            f"Generate creative approach for: {query}",
+            context
+        ))
+        
+        return [h for h in hypotheses if h]
+
+    def call_model(self, module: str, prompt: str, context: dict) -> str:
+        """Advanced model caller with adaptive temperature and retry"""
+        temperature = self.calculate_temperature(context)
+        retries = 0
+        
+        while retries < self.cognitive_config["max_retries"]:
+            try:
+                if module in ["intuition", "metacognition"]:
+                    return self._call_mistral(prompt, temperature)
+                elif module == "analysis":
+                    return self._call_gpt4o(prompt, temperature)
+                elif module == "creativity":
+                    return self.gemini.generate_content(prompt).text
+                elif module == "emotional_intelligence":
+                    return self._call_qwen(prompt)
+                elif module == "validation":
+                    return self._call_deepseek(prompt)
+                
+            except Exception as e:
+                retries += 1
+                self.handle_error(e)
+        
+        return ""
+
+    def _call_mistral(self, prompt: str, temperature: float) -> str:
+        """Call Mistral API"""
+        headers = {
+            "Authorization": f"Bearer {self.api_keys['MISTRAL']}",
+            "Content-Type": "application/json"
+        }
+        
+        payload = {
+            "model": self.model_registry["intuition"],
+            "messages": [{"role": "user", "content": prompt}],
+            "temperature": temperature,
+            "max_tokens": 2000
+        }
+        
+        response = requests.post(
+            "https://api.mistral.ai/v1/chat/completions",
+            headers=headers,
+            json=payload
+        )
+        
+        return response.json()['choices'][0]['message']['content']
+
+    def _call_gpt4o(self, prompt: str, temperature: float) -> str:
+        """Call GPT-4o via Azure"""
+        try:
+            response = self.gpt4o.chat.completions.create(
+                model=self.model_registry["analysis"],
+                messages=[{"role": "user", "content": prompt}],
+                temperature=temperature,
+                max_tokens=2000
+            )
+            return response.choices[0].message.content
+        except Exception as e:
+            raise RuntimeError(f"GPT-4o Error: {str(e)}")
+
+    def calculate_confidence(self, response: str) -> float:
+        """Calculate semantic confidence score"""
+        query_embed = self.sentence_model.encode(response)
+        knowledge_embeds = self.knowledge_graph["embeddings"]
+        
+        if knowledge_embeds.size == 0:
+            return 0.5  # Neutral confidence
+        
+        similarities = cosine_similarity([query_embed], knowledge_embeds)
+        return np.max(similarities)
+
+    def update_knowledge_graph(self, query: str, response: str):
+        """Dynamic knowledge integration"""
+        embedding = self.sentence_model.encode(response)
+        
+        if self.knowledge_graph["embeddings"].size == 0:
+            self.knowledge_graph["embeddings"] = np.array([embedding])
+        else:
+            self.knowledge_graph["embeddings"] = np.vstack(
+                [self.knowledge_graph["embeddings"], embedding]
+            )
+        
+        self.knowledge_graph["nodes"].append({
+            "id": len(self.knowledge_graph["nodes"]),
+            "content": response,
+            "embedding": embedding.tolist()
+        })
+
+    def handle_error(self, error: Exception):
+        """Error handling and recovery"""
+        self.cognitive_metrics["error_rates"].append(time.time())
+        print(f"System Error: {str(error)}")
+        # Implement error recovery logic here
+
+def create_agi_interface():
+    try:
+        agi = AGICognitiveSystem()
+    except ValueError as e:
+        return gr.Blocks().launch(error_message=str(e))
+    
+    with gr.Blocks(title="Advanced AGI System", theme=gr.themes.Soft(), css="""
+        .cognitive-node { padding: 15px; margin: 10px; border-radius: 8px; background: #f8f9fa; }
+        .confidence-meter { height: 10px; background: #eee; border-radius: 5px; margin: 10px 0; }
+        .confidence-fill { height: 100%; border-radius: 5px; background: #4CAF50; }
+        """) as demo:
+        
+        gr.Markdown("# 🧠 Advanced AGI Cognitive System")
+        
+        with gr.Row():
+            input_panel = gr.Textbox(label="Input Query", lines=3, 
+                                   placeholder="Enter complex query...")
+            with gr.Accordion("Cognitive Controls", open=False):
+                depth = gr.Slider(1, 10, value=5, label="Reasoning Depth")
+                creativity = gr.Slider(0, 1, value=0.7, label="Creativity Level")
+        
+        output_panel = gr.Markdown()
+        visualization = gr.HTML()
+        metrics = gr.DataFrame(headers=["Metric", "Value"])
+        
+        input_panel.submit(
+            fn=agi.cognitive_flow,
+            inputs=input_panel,
+            outputs=[output_panel, metrics]
+        )
+        
+    return demo
+
+if __name__ == "__main__":
+    create_agi_interface().launch(server_port=7860)