Update app.py

app.py

@@ -1,66 +1,103 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-environment variables
-os.environ = "HUGGINGFACEHUB_API_TOKEN"
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+# app.py
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import requests
+import importlib.util
+import os
+import hashlib
+import json
+from datetime import datetime
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Configuration
+CONFIG = {
+    "model_name": "deepseek-ai/deepseek-coder-1.3b-instruct",
+    "update_url": "https://raw.githubusercontent.com/[YOUR_USERNAME]/deepseek-updates/main/",
+    "manifest_url": "https://raw.githubusercontent.com/[YOUR_USERNAME]/deepseek-updates/main/manifest.json",
+    "allowed_modules": ["response_handler", "updater"],
+    "update_frequency": 6  # hours
+}
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(CONFIG["model_name"])
+model = AutoModelForCausalLM.from_pretrained(CONFIG["model_name"])
 
-    messages.append({"role": "user", "content": message})
+# Security verification
+def verify_file_integrity(content, expected_hash):
+    sha256 = hashlib.sha256()
+    sha256.update(content.encode('utf-8'))
+    return sha256.hexdigest() == expected_hash
 
-    response = ""
+# Update mechanism
+def check_for_updates():
+    try:
+        # Get update manifest
+        response = requests.get(CONFIG["manifest_url"])
+        manifest = response.json()
+        
+        # Check last update time
+        last_update_path = "last_update.txt"
+        if os.path.exists(last_update_path):
+            with open(last_update_path, 'r') as f:
+                last_update = datetime.fromisoformat(f.read().strip())
+            time_since_update = (datetime.utcnow() - last_update).total_seconds() / 3600
+            if time_since_update < CONFIG["update_frequency"]:
+                return "Too soon for update check"
+        
+        # Process updates
+        updates_applied = []
+        for module_name in CONFIG["allowed_modules"]:
+            if module_name in manifest["modules"]:
+                module_info = manifest["modules"][module_name]
+                file_path = f"{module_name}.py"
+                
+                # Download and verify update
+                response = requests.get(CONFIG["update_url"] + file_path)
+                if response.status_code == 200:
+                    content = response.text
+                    if verify_file_integrity(content, module_info["sha256"]):
+                        # Save new version
+                        with open(file_path, 'w') as f:
+                            f.write(content)
+                        updates_applied.append(module_name)
+        
+        # Update timestamp
+        with open(last_update_path, 'w') as f:
+            f.write(datetime.utcnow().isoformat())
+            
+        return f"Updates applied to: {', '.join(updates_applied)}" if updates_applied else "No updates available"
+    
+    except Exception as e:
+        return f"Update failed: {str(e)}"
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+# Dynamic module loader
+def load_module(module_name):
+    spec = importlib.util.spec_from_file_location(module_name, f"{module_name}.py")
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
 
-        response += token
-        yield response
+# Load core modules
+response_handler = load_module("response_handler")
+updater = load_module("updater")
 
+# Main processing function
+def process_query(prompt):
+    # Check for update command
+    if "/update" in prompt:
+        return check_for_updates()
+    
+    # Normal processing
+    return response_handler.generate_response(prompt, tokenizer, model)
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+# Create Gradio interface
+interface = gr.Interface(
+    fn=process_query,
+    inputs=gr.Textbox(lines=3, placeholder="Enter your query..."),
+    outputs="text",
+    title="Self-Updating DeepSeek AI",
+    description="This AI can update its own code. Type '/update' to check for improvements."
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch(server_port=7860, share=True)