Upload 5 files

src/agents/critic.py

@@ -0,0 +1 @@
+# critic.py

src/agents/executor.py

@@ -0,0 +1 @@
+# executor.py

src/agents/planner.py

@@ -0,0 +1 @@
+# planner.py

src/core/cognitive_engine.py

@@ -1,101 +1 @@
-from ctransformers import AutoModelForCausalLM
-import os
-import json
-import re
-
-class CognitiveEngine:
-    def __init__(self, model):
-        self.model = model
-
-    def identify_improvements(self, task_description):
-        prompt = f"What improvements can be made to this task?\n\nTask: {task_description}"
-        response = self.model(prompt)
-        return response.strip().split("\n")
-
-    def generate_enhancements(self, improvements):
-        prompt = "Generate code changes based on the following improvements:\n"
-        prompt += "\n".join(improvements)
-        return self.model(prompt)
-
-    def apply_enhancements(self, code_updates):
-        print("[Simulation] Applying the following code changes:")
-        print(code_updates)
-        return True  # Simulate success       
-        
-    def load_knowledge(self):
-        try:
-            with open(self.knowledge_file, "r") as f:
-                self.knowledge = json.load(f)
-        except:
-            self.knowledge = {"improvements": [], "solutions": {}, "tasks": []}
-    
-    def save_knowledge(self):
-        with open(self.knowledge_file, "w") as f:
-            json.dump(self.knowledge, f, indent=2)
-    
-    def identify_improvements(self, user_task):
-        prompt = f"""
-        You are an autonomous AI system. The user has requested: "{user_task}"
-        
-        Analyze your current capabilities and suggest concrete improvements to address this request.
-        Focus on:
-        1. Code quality enhancements
-        2. Performance optimizations
-        3. New functionality
-        
-        Respond in JSON format: {{"improvements": ["item1", "item2", "item3"]}}
-        """
-        response = self.model(prompt, max_new_tokens=256)
-        
-        # Extract JSON from response
-        try:
-            json_match = re.search(r'\{.*\}', response, re.DOTALL)
-            if json_match:
-                return json.loads(json_match.group())["improvements"]
-        except:
-            pass
-        
-        # Fallback if JSON parsing fails
-        return [
-            f"Implement user request: {user_task}",
-            "Optimize related systems",
-            "Add documentation for new features"
-        ]
-    
-    def generate_enhancements(self, improvements):
-        prompt = f"""
-        You are a self-coding AI. Generate Python code to implement these improvements:
-        {', '.join(improvements)}
-        
-        Requirements:
-        - Modify only existing files
-        - Include necessary imports
-        - Add comments explaining changes
-        - Ensure backward compatibility
-        
-        Output ONLY valid Python code in a single code block.
-        """
-        return self.model(prompt, max_new_tokens=1024)
-    
-    def apply_enhancements(self, code):
-        try:
-            # Extract code block
-            if "```python" in code:
-                code = code.split("```python")[1].split("```")[0]
-            elif "```" in code:
-                code = code.split("```")[1].split("```")[0]
-            
-            # Validate code
-            compile(code, "<string>", "exec")
-            
-            # Apply changes (in real system would write to files)
-            print(f"Applying code changes:\n{code[:500]}...")
-            
-            # Update knowledge
-            self.knowledge["solutions"][str(time.time())] = code[:500] + "..."
-            self.save_knowledge()
-            
-            return True
-        except Exception as e:
-            print(f"Code validation failed: {str(e)}")
-            return True
+# cognitive_engine.py

src/utils/hf_packager.py

@@ -1,38 +1 @@
-from huggingface_hub import HfApi
-import datetime
-import zipfile
-import os
-import json
-
-class HFSpacePackager:
-    def __init__(self):
-        self.api = HfApi()
-        self.token = os.getenv("HF_TOKEN", "")
-        
-    def create_snapshot(self):
-        """Create snapshot in /tmp directory"""
-        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-        zip_name = f"/tmp/snapshot_{timestamp}.zip"
-        manifest = {
-            "version": timestamp,
-            "files": ["app.py", "src/"],
-            "system": "Autonomous AI HF Space"
-        }
-        
-        # Create zip
-        with zipfile.ZipFile(zip_name, 'w') as zipf:
-            # Add key files
-            for file in ["app.py", "requirements.txt", "Dockerfile"]:
-                if os.path.exists(file):
-                    zipf.write(file)
-            
-            # Add src directory
-            for root, _, files in os.walk("src"):
-                for file in files:
-                    file_path = os.path.join(root, file)
-                    zipf.write(file_path)
-            
-            # Add manifest
-            zipf.writestr("MANIFEST.json", json.dumps(manifest, indent=2))
-        
-        return f"Snapshot created: {zip_name}"
+# hf_packager.py