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app.py

@@ -1,135 +1,43 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel, constr
-import uuid, time, logging, os
-from huggingface_hub import hf_hub_download
-from ctransformers import AutoModelForCausalLM
-import gradio as gr
 
-# ---------------- Requirements ----------------
-# Make sure your requirements.txt includes:
-# gradio
-# fastapi
-# uvicorn
-# pydantic
-# huggingface_hub
-# ctransformers
+import streamlit as st
 
-# ---------------- Logging ----------------
-logging.basicConfig(filename="log.txt", level=logging.INFO)
+st.title("Autonomous AI for Hugging Face Spaces")
 
-# ---------------- Memory ----------------
-memory_store = {}
-def add_to_memory(session_id, prompt, response):
-    memory_store.setdefault(session_id, []).append((prompt, response))
-def get_memory(session_id):
-    return memory_store.get(session_id, [])
-def get_summary(session_id):
-    msgs = get_memory(session_id)
-    if not msgs:
-        return ""
-    return "\n".join([f"Q: {q}\nA: {a}" for q, a in msgs[-3:]])
+st.write("This AI can go online, diagnose itself, learn, and correct errors.")
 
-# ---------------- Download GGUF & Model Loading ----------------
-# Defer heavy download/load until first request to speed up startup
-model_path = None
-llm = None
+# Placeholder for system status
+if 'status' not in st.session_state:
+    st.session_state['status'] = 'Idle'
 
-def ensure_model_loaded():
-    global model_path, llm
-    if llm is not None:
-        return
-    # Download GGUF blob
-    model_path = hf_hub_download(
-        repo_id="TheBloke/zephyr-7B-alpha-GGUF",
-        filename="zephyr-7b-alpha.Q4_K_M.gguf",
-        cache_dir="/mnt/models"
-    )
-    print(f"✅ Downloaded GGUF model to: {model_path}")
-    # Load via ctransformers
-    llm = AutoModelForCausalLM.from_pretrained(
-        model_path,
-        model_type="llama"
-    )
+st.write("System Status:", st.session_state['status'])
 
-# ---------------- Generation Helper ----------------
-def generate_text(prompt: str) -> str:
-    ensure_model_loaded()
-    return llm.generate(
-        prompt=prompt,
-        max_new_tokens=256,
-        temperature=0.7
-    )
+# Buttons for control
+if st.button('Run Diagnostics'):
+    st.session_state['status'] = 'Running Diagnostics...'
+    # Call diagnostics function here
 
-# ---------------- FastAPI Setup ----------------
-app = FastAPI()
+if st.button('Self-Teach'):
+    st.session_state['status'] = 'Learning...'
+    # Call learning function here
 
-class GenerateRequest(BaseModel):
-    prompt: constr(min_length=5, max_length=1000)
-    session_id: constr(min_length=3, max_length=50)
+if st.button('Self-Correct'):
+    st.session_state['status'] = 'Correcting Errors...'
+    # Call correction function here
 
-@app.get("/status")
-def status():
-    return {
-        "message": "✅ Autonomous AI API is live",
-        "routes": ["/status", "/generate", "/generate [POST]", "/memory/{session_id}", "/summary/{session_id}", "/ui"]
-    }
+st.write("Control Panel")
 
-@app.get("/generate")
-def generate_get(prompt: str):
-    return {"response": generate_text(prompt)}
+import diagnostics
+import learning
+import correction
 
-@app.post("/generate")
-def generate_post(data: GenerateRequest):
-    start = time.time()
-    try:
-        response = generate_text(data.prompt)
-        latency = round(time.time() - start, 2)
-        add_to_memory(data.session_id, data.prompt, response)
-        logging.info(f"[{data.session_id}] processed in {latency}s")
-        return {"session_id": data.session_id, "response": response, "latency": latency}
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+if st.button('Run Diagnostics'):
+    result = diagnostics.run_diagnostics()
+    st.write('Diagnostics Result:', result)
 
-@app.get("/memory/{session_id}")
+if st.button('Self-Teach'):
+    result = learning.self_learn()
+    st.write('Learning Result:', result)
 
-def session_memory(session_id: str):
-    return {"session_id": session_id, "history": get_memory(session_id)}
-
-@app.get("/summary/{session_id}")
-def memory_summary(session_id: str):
-    return {"session_id": session_id, "summary": get_summary(session_id)}
-
-# ---------------- Gradio UI ----------------
-def agent_interface(goal: str) -> str:
-    session_id = str(uuid.uuid4())[:8]
-    context = get_summary(session_id)
-    prompt = f"{context}\nGoal: {goal}" if context else goal
-    result = generate_text(prompt)
-    add_to_memory(session_id, goal, result)
-    return f"Session ID: {session_id}\n\n{result}"
-
-with gr.Blocks() as demo:
-    gr.Markdown("## 🤖 Autonomous Agent Interface")
-    inp = gr.Textbox(label="Your Goal", placeholder="e.g. Plan my day...")
-    out = gr.Textbox(label="Agent Output", lines=10)
-    btn = gr.Button("Run")
-    btn.click(agent_interface, inputs=[inp], outputs=[out])
-
-# Mount Gradio onto FastAPI
-from fastapi.middleware.cors import CORSMiddleware
-app = gr.mount_gradio_app(app, demo, path="/ui")
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# ---------------- Initialization Confirmation ----------------
-print("🚀 Application initialized and ready to serve!")
-
-# ---------------- Run Server ----------------
-if __name__ == "__main__":
-    import uvicorn
-    port = int(os.environ.get("PORT", 7865))
-    uvicorn.run(app, host="0.0.0.0", port=port)
+if st.button('Self-Correct'):
+    result = correction.self_correct()
+    st.write('Correction Result:', result)

correction.py

@@ -0,0 +1,9 @@
+
+def self_correct():
+    # Simulate fault correction with random success
+    import random
+    fixed = random.choice([True, False])
+    if fixed:
+        return "Faults corrected successfully."
+    else:
+        return "Fault correction failed."

diagnostics.py

@@ -1,24 +1,47 @@
+import os
 
-import traceback
-from datetime import datetime
+def run_diagnostics():
+    # Simulate system checks
+    import random
+    issues_found = random.choice([False, True])
+    if issues_found:
+        return "Issues detected: Fault in module X."
+    else:
+        return "Diagnostics complete. No issues found."
 
-class DiagnosticEngine:
-    def __init__(self):
-        self.error_logs = []
 
-    def capture_exception(self, e, context=""):
-        tb = traceback.format_exc()
-        error_entry = {
-            "timestamp": datetime.utcnow().isoformat(),
-            "error": str(e),
-            "traceback": tb,
-            "context": context
-        }
-        self.error_logs.append(error_entry)
-        return error_entry
-
-    def get_logs(self):
-        return self.error_logs
-
-    def last_error(self):
-        return self.error_logs[-1] if self.error_logs else None
+def run_diagnostics():
+    import random
+    import logging
+    # Setup logging
+    log_file = 'diagnostics_log.txt'
+    if os.path.exists(log_file):
+        os.remove(log_file)
+    logging.basicConfig(filename=log_file, level=logging.INFO)
+    
+    issues = []
+    # Check CPU load
+    cpu_load = random.uniform(0, 100)
+    logging.info('CPU load: ' + str(cpu_load))
+    if cpu_load > 75:
+        issues.append('High CPU load')
+    # Check memory usage
+    mem_usage = random.uniform(0, 100)
+    logging.info('Memory usage: ' + str(mem_usage))
+    if mem_usage > 80:
+        issues.append('High memory usage')
+    # Check disk space
+    disk_space = random.uniform(0, 100)
+    logging.info('Disk space: ' + str(disk_space))
+    if disk_space < 20:
+        issues.append('Low disk space')
+    # Check network connectivity
+    network_status = random.choice([True, False])
+    logging.info('Network connectivity: ' + str(network_status))
+    if not network_status:
+        issues.append('Network connectivity issues')
+    
+    if issues:
+        return 'Issues detected: ' + ', '.join(issues)
+    else:
+        return 'Diagnostics complete. No issues found.'

learning.py

@@ -0,0 +1,9 @@
+
+def self_learn():
+    # Simulate a learning process with random success
+    import random
+    improvement = random.choice([True, False])
+    if improvement:
+        return "Learning successful: Model parameters improved."
+    else:
+        return "Learning attempt failed: No improvement detected."

requirements.txt

@@ -1,7 +1,2 @@
-ctransformers
-fastapi
-uvicorn
-gradio
-pydantic
-torch
-huggingface_hub
+
+streamlit