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NORTHERN-OPEN-AI

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ArBaltee commited on Mar 4

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b7a99bc

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1 Parent(s): 9a9fb02

Update app.py

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app.py +21 -124

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@@ -1,129 +1,26 @@
-# NORTHERN_AI
-# Created by AR.BALTEE
-import os
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from flask import Flask, request, jsonify
-app = Flask(__name__)
-# Choose a lightweight open model that can run on limited hardware
-# Options include:
-# - GPT2-small (if you have ~2GB RAM for the model)
-# - Hugging Face's inference endpoints (cloud-based, some free tiers)
-# - Models like DialoGPT-small, BLOOM-560M, or OPT-350M
-# Configuration
-MODEL_NAME = "EleutherAI/gpt-neo-125M"  # A relatively small model, replace with your choice
-USE_CLOUD_INFERENCE = True  # Set to True to use Hugging Face's Inference API instead of local model
-# Hugging Face API Token (sign up for free at huggingface.co)
-HF_API_TOKEN = os.environ.get("HF_API_TOKEN", "")  # Store your token as an environment variable for security
-# System prompt that defines your AI assistant's personality
-SYSTEM_PROMPT = """NORTHERN_AI is a helpful AI assistant created by AR.BALTEE.
-It aims to provide accurate and helpful information to users' questions.
-NORTHERN_AI is friendly, concise, and knowledgeable."""
-class NorthernAI:
-    def __init__(self):
-        self.system_prompt = SYSTEM_PROMPT
-        if not USE_CLOUD_INFERENCE:
-            print("Loading model locally (requires sufficient RAM)...")
-            self.tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-            # Load in 8-bit to reduce memory requirements
-            self.model = AutoModelForCausalLM.from_pretrained(
-                MODEL_NAME,
-                torch_dtype=torch.float16,
-                low_cpu_mem_usage=True,
-                device_map="auto"
-            )
-        else:
-            print("Using cloud inference API (minimal RAM required)...")
-            # For cloud inference, we'll just need the API endpoint
-            from huggingface_hub import InferenceClient
-            self.client = InferenceClient(token=HF_API_TOKEN)
-    def generate_response(self, user_input):
-        prompt = f"{self.system_prompt}\n\nUser: {user_input}\nNORTHERN_AI:"
-        if USE_CLOUD_INFERENCE:
-            # Use Hugging Face's Inference API
-            response = self.client.text_generation(
-                prompt,
-                model=MODEL_NAME,
-                max_new_tokens=150,
-                temperature=0.7,
-                top_p=0.95,
-                repetition_penalty=1.1
-            )
-            return response.strip()
-        else:
-            # Local generation
-            inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
-            with torch.no_grad():
-                output = self.model.generate(
-                    **inputs,
-                    max_new_tokens=150,
-                    temperature=0.7,
-                    top_p=0.95,
-                    repetition_penalty=1.1
-                )
-            return self.tokenizer.decode(output[0], skip_special_tokens=True).split("NORTHERN_AI:")[-1].strip()
-# Initialize the AI assistant
-northern_ai = NorthernAI()
-@app.route('/api/chat', methods=['POST'])
-def chat():
-    data = request.json
-    user_message = data.get('message', '')
-    response = northern_ai.generate_response(user_message)
-    return jsonify({"response": response})
-@app.route('/')
-def home():
-    return """
-    <html>
-        <head><title>NORTHERN_AI by AR.BALTEE</title></head>
-        <body>
-            <h1>Welcome to NORTHERN_AI</h1>
-            <form id="chat-form">
-                <input type="text" id="user-input" placeholder="Ask NORTHERN_AI something...">
-                <button type="submit">Send</button>
-            </form>
-            <div id="chat-history"></div>
-            <script>
-                document.getElementById('chat-form').addEventListener('submit', async (e) => {
-                    e.preventDefault();
-                    const input = document.getElementById('user-input');
-                    const message = input.value;
-                    input.value = '';
-                    // Display user message
-                    const chatHistory = document.getElementById('chat-history');
-                    chatHistory.innerHTML += `<p><strong>You:</strong> ${message}</p>`;
-                    // Get AI response
-                    const response = await fetch('/api/chat', {
-                        method: 'POST',
-                        headers: {'Content-Type': 'application/json'},
-                        body: JSON.stringify({message})
-                    });
-                    const data = await response.json();
-                    chatHistory.innerHTML += `<p><strong>NORTHERN_AI:</strong> ${data.response}</p>`;
-                });
-            </script>
-        </body>
-    </html>
-    """
-if __name__ == '__main__':
-    # Use the PORT environment variable provided by most free hosting services
-    port = int(os.environ.get("PORT", 5000))
-    app.run(host='0.0.0.0', port=port)

+# NOimport os
+import gradio as gr
+def generate_response(message):
+    # Simple response for testing
+    return f"NORTHERN_AI: Thank you for your message: '{message}'"
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# NORTHERN_AI by AR.BALTEE")
+    with gr.Row():
+        with gr.Column():
+            message = gr.Textbox(label="Your message")
+            submit = gr.Button("Send")
+        with gr.Column():
+            output = gr.Textbox(label="NORTHERN_AI Response")
+    submit.click(generate_response, inputs=message, outputs=output)
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()RTHERN_AI
+# Created by AR.BALTEE