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README.md

@@ -1 +1,32 @@
-# README.md
+# 🧠 Autonomous AI (FastAPI + Multi-Agent)
+
+This is a multi-agent autonomous AI application using FastAPI, `ctransformers`, and Gradio. It supports:
+
+- 🧠 Planner / Executor / Critic agents
+- ✅ Session-based memory
+- 📤 POST + GET endpoints
+- 🎛 Web interface via Gradio
+- 📦 Ready for Hugging Face Spaces
+
+## 🚀 Run locally
+
+```bash
+pip install -r requirements.txt
+python app.py
+```
+
+## 🧪 Endpoints
+
+| Method | Endpoint                 | Description             |
+|--------|--------------------------|-------------------------|
+| GET    | `/status`                | System check            |
+| GET    | `/generate?prompt=...`   | Quick model query       |
+| POST   | `/generate`              | Send prompt + session   |
+| GET    | `/memory/{session_id}`   | View memory             |
+| GET    | `/summary/{session_id}`  | Get recent summary      |
+
+## 🧠 Agents
+
+- `planner`: Plans steps
+- `executor`: Executes simulated code
+- `critic`: Reviews execution

knowledge/state.json

@@ -1 +1,19 @@
-# state.json
+{
+  "knowledge_base": {
+    "name": "AutonomousAI_KnowledgeBase",
+    "description": "Used by the autonomous AI system for context and reasoning.",
+    "created_at": "2025-06-26T12:00:00Z",
+    "last_updated": "2025-06-26T12:00:00Z",
+    "total_documents": 0,
+    "total_tokens": 0,
+    "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
+    "vector_store": {
+      "type": "FAISS",
+      "index_file": "vector_index/faiss.index",
+      "dimension": 384,
+      "metric": "cosine"
+    },
+    "memory_enabled": true,
+    "status": "initialized"
+  }
+}

memory.py

@@ -1,5 +1,4 @@
-import json
-import os
+import json, os
 
 MEMORY_FILE = "memory.json"
 
@@ -10,25 +9,19 @@ def init_memory():
     with open(MEMORY_FILE, "r") as f:
         return json.load(f)
 
-def save_memory(data):
+def save_memory(memory):
     with open(MEMORY_FILE, "w") as f:
-        json.dump(data, f, indent=2)
+        json.dump(memory, f, indent=2)
 
 def add_to_memory(session_id, prompt, response):
     memory = init_memory()
-    if session_id not in memory:
-        memory[session_id] = []
+    memory.setdefault(session_id, [])
     memory[session_id].append({"prompt": prompt, "response": response})
     memory[session_id] = memory[session_id][-50:]
     save_memory(memory)
 
 def get_memory(session_id):
-    memory = init_memory()
-    return memory.get(session_id, [])
-
-def search_memory(query, memory):
-    # Placeholder: Return last few memory entries related to query (simple all for now)
-    return memory["texts"][-5:]
+    return init_memory().get(session_id, [])
 
 def get_summary(session_id, max_items=3):
     history = get_memory(session_id)

requirements.txt

@@ -1,10 +1,7 @@
 fastapi
-gradio
 uvicorn
+ctransformers
+gradio
+psutil
 sentence-transformers
-numpy
 faiss-cpu
-psutil
-torch
-transformers
-ctransformers

src/agents/critic.py

@@ -1 +1,2 @@
-# critic.py
+def review_result(step, result):
+    return f"Review passed for step: {step}"

src/agents/executor.py

@@ -1 +1,2 @@
-# executor.py
+def execute_step(step):
+    return f"Executed: {step} (simulated)"

src/agents/planner.py

@@ -1 +1,2 @@
-# planner.py
+def plan_task(goal, memory):
+    return [f"Analyze: {goal}", "Generate code", "Review & validate"]

src/core/cognitive_engine.py

@@ -1 +1,16 @@
-# cognitive_engine.py
+class CognitiveEngine:
+    def __init__(self, model):
+        self.model = model
+
+    def identify_improvements(self, task_description):
+        prompt = f"Suggest improvements:\n\nTask: {task_description}"
+        return self.model(prompt).split("\n")
+
+    def generate_enhancements(self, improvements):
+        prompt = "Generate code for improvements:\n" + "\n".join(improvements)
+        return self.model(prompt)
+
+    def apply_enhancements(self, code):
+        print("Applying enhancements...")
+        print(code)
+        return True

src/utils/hf_packager.py

@@ -1 +1,2 @@
-# hf_packager.py
+def create_snapshot():
+    return "https://huggingface.co/spaces/your-snapshot-url"

static/style.css

@@ -1,108 +1 @@
-:root {
-    --primary: #4f46e5;
-    --secondary: #6366f1;
-    --success: #10b981;
-    --danger: #ef4444;
-    --warning: #f59e0b;
-    --card-bg: #ffffff;
-    --bg: #f8fafc;
-    --text: #1e293b;
-}
-
-body {
-    background-color: var(--bg);
-    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-    margin: 0;
-    padding: 20px;
-    color: var(--text);
-}
-
-h1, h2, h3 {
-    color: var(--primary);
-}
-
-.tab {
-    background: var(--card-bg);
-    border-radius: 10px;
-    padding: 20px;
-    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.05);
-    margin-bottom: 20px;
-    border: 1px solid #e2e8f0;
-}
-
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-    background: var(--primary);
-    color: white;
-    border: none;
-    padding: 12px 24px;
-    border-radius: 8px;
-    cursor: pointer;
-    font-weight: 600;
-    transition: all 0.3s;
-    margin: 5px 0;
-}
-
-button:hover {
-    background: var(--secondary);
-    transform: translateY(-2px);
-    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
-}
-
-button.primary {
-    background: var(--success);
-}
-
-button.danger {
-    background: var(--danger);
-}
-
-textarea, input, .json-container, .code-container {
-    background: #f1f5f9;
-    border: 1px solid #cbd5e1;
-    border-radius: 8px;
-    padding: 15px;
-    margin: 10px 0;
-    width: 100%;
-}
-
-.code-container {
-    font-family: 'Fira Code', monospace;
-    white-space: pre-wrap;
-}
-
-.tabs {
-    display: flex;
-    margin-bottom: 20px;
-    border-bottom: 2px solid #e2e8f0;
-}
-
-.tab-button {
-    background: none;
-    color: var(--text);
-    border: none;
-    padding: 12px 20px;
-    border-radius: 0;
-    margin: 0;
-}
-
-.tab-button.active {
-    border-bottom: 3px solid var(--primary);
-    color: var(--primary);
-    font-weight: bold;
-}
-
-.row {
-    display: flex;
-    gap: 20px;
-    margin-bottom: 20px;
-}
-
-.column {
-    flex: 1;
-}
-
-@media (max-width: 768px) {
-    .row {
-        flex-direction: column;
-    }
-}
+body { font-family: sans-serif; }