Update app.py

app.py

@@ -4,6 +4,101 @@ import time
 import logging
 from typing import List, Dict, Any, Optional
 import random
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import json
+from pathlib import Path
+from datetime import datetime
+
+MODEL_NAME = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).to("cpu").eval()
+
+CHANGELOG_FILE = Path("changelog.json")
+CHATLOG_FILE = Path("chatlog.json")
+
+def generate_reply(user_input, chat_history, temperature, max_tokens):
+    prompt = "You are a helpful AI assistant.\n"
+    for user_msg, bot_msg in chat_history:
+        prompt += f"User: {user_msg}\nAssistant: {bot_msg}\n"
+    prompt += f"User: {user_input}\nAssistant:"
+
+    inputs = tokenizer(prompt, return_tensors="pt", padding=True)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=max_tokens,
+        temperature=temperature,
+        do_sample=True,
+        top_p=0.9,
+        pad_token_id=tokenizer.eos_token_id or tokenizer.pad_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+    reply = tokenizer.decode(outputs[0][inputs['input_ids'].shape[-1]:], skip_special_tokens=True).strip()
+    chat_history.append((user_input, reply))
+
+    # Save chat log after each exchange
+    save_chat_log(chat_history)
+    return "", chat_history
+
+def save_chat_log(chat_history):
+    data = {
+        "timestamp": datetime.now().isoformat(),
+        "chat": chat_history,
+    }
+    if CHATLOG_FILE.exists():
+        old_data = json.loads(CHATLOG_FILE.read_text())
+    else:
+        old_data = []
+    old_data.append(data)
+    CHATLOG_FILE.write_text(json.dumps(old_data, indent=2))
+
+def generate_suggestions(feedback):
+    # Simple mock suggestions based on feedback input
+    # Replace with real model inference if desired
+    suggestions = [
+        f"Improve clarity on: {feedback[:50]}...",
+        "Add context awareness for better follow-up answers.",
+        "Enhance error handling for invalid inputs.",
+    ]
+    # Save suggestions to changelog file
+    save_changelog(suggestions)
+    return "\n- " + "\n- ".join(suggestions)
+
+def save_changelog(suggestions):
+    data = {
+        "timestamp": datetime.now().isoformat(),
+        "suggestions": suggestions,
+    }
+    if CHANGELOG_FILE.exists():
+        old_data = json.loads(CHANGELOG_FILE.read_text())
+    else:
+        old_data = []
+    old_data.append(data)
+    CHANGELOG_FILE.write_text(json.dumps(old_data, indent=2))
+
+with gr.Blocks(title="Autonomous AI Minimal with Self-Improve") as demo:
+    gr.Markdown("# 🤖 Autonomous AI with Feedback & Logging")
+
+    with gr.Tab("Chat"):
+        chatbot = gr.Chatbot(label="Chat")
+        user_input = gr.Textbox(placeholder="Type your message here...", lines=2, label="Your Message")
+        temperature = gr.Slider(0.1, 1.2, value=0.7, step=0.05, label="Temperature")
+        max_tokens = gr.Slider(50, 300, value=150, step=25, label="Max Tokens")
+        send_btn = gr.Button("Send")
+        reset_btn = gr.Button("Reset")
+
+        state = gr.State([])
+
+        send_btn.click(generate_reply, inputs=[user_input, state, temperature, max_tokens], outputs=[user_input, chatbot])
+        reset_btn.click(lambda: [], None, chatbot)
+        reset_btn.click(lambda: [], None, state)
+
+    with gr.Tab("Self-Improve"):
+        feedback = gr.Textbox(label="Enter feedback or observations", lines=3)
+        suggest_btn = gr.Button("Generate Suggestions")
+        suggestions = gr.Textbox(label="Improvement Suggestions", interactive=False, lines=6)
+
+        suggest_btn.click(generate_suggestions, inputs=feedback, outputs=suggestions)
 
 # Logging config for traceability
 logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s:%(message)s')
@@ -386,4 +481,5 @@ class OmniAIvX:
     def encrypt_and_store(self, data: str):
         encrypted = self.security_privacy.encrypt(data)
         self.memory.store_long_term(encrypted)
-
+if __name__ == "__main__":
+    demo.launch(share=True)