Create app.py

app.py

@@ -0,0 +1,297 @@
+# =====================
+# 🦁 SIMBA AI - BACKEND API ONLY
+# =====================
+# Provides REST API for your custom frontend
+# No Gradio interface - Pure backend
+# =====================
+
+from flask import Flask, request, jsonify, send_from_directory
+from flask_cors import CORS
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+import time
+import os
+
+print("🚀 Initializing Simba AI Backend API...")
+
+app = Flask(__name__)
+CORS(app)  # Enable CORS for your frontend
+
+# =====================
+# LOAD AI MODEL
+# =====================
+
+model_name = "microsoft/DialoGPT-large"
+
+try:
+    print("📥 Loading AI model...")
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    tokenizer.pad_token = tokenizer.eos_token
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.float16,
+        device_map="auto",
+    )
+    print("✅ Simba AI Model Loaded Successfully!")
+except Exception as e:
+    print(f"❌ Model loading error: {e}")
+    model_name = "microsoft/DialoGPT-medium"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    tokenizer.pad_token = tokenizer.eos_token
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    print("✅ Light model loaded!")
+
+# =====================
+# AFRICAN KNOWLEDGE BASE
+# =====================
+
+simba_knowledge_base = [
+    # CODING
+    {"question": "Python add function", "answer": "def add(a, b): return a + b"},
+    {"question": "Factorial function", "answer": "def factorial(n): return 1 if n == 0 else n * factorial(n-1)"},
+    {"question": "Reverse string function", "answer": "def reverse_string(s): return s[::-1]"},
+    {"question": "Check even number", "answer": "def is_even(n): return n % 2 == 0"},
+    {"question": "Multiply function", "answer": "def multiply(x, y): return x * y"},
+    {"question": "Yoruba greeting function", "answer": "def yoruba_greeting(): return 'Báwo ni'"},
+    
+    # MATH
+    {"question": "15 + 27", "answer": "42"},
+    {"question": "8 × 7", "answer": "56"},
+    {"question": "100 - 45", "answer": "55"},
+    {"question": "12 × 12", "answer": "144"},
+    {"question": "25% of 200", "answer": "50"},
+    
+    # YORUBA
+    {"question": "Hello in Yoruba", "answer": "Báwo ni"},
+    {"question": "Thank you in Yoruba", "answer": "Ẹ sé"},
+    {"question": "How are you in Yoruba", "answer": "Ṣe daadaa ni"},
+    {"question": "Good morning in Yoruba", "answer": "Ẹ káàrọ̀"},
+    {"question": "Good night in Yoruba", "answer": "O dàárọ̀"},
+    {"question": "Please in Yoruba", "answer": "Jọ̀wọ́"},
+    
+    # SWAHILI
+    {"question": "Hello in Swahili", "answer": "Hujambo"},
+    {"question": "Thank you in Swahili", "answer": "Asante"},
+    
+    # IGBO
+    {"question": "Hello in Igbo", "answer": "Nnọọ"},
+    {"question": "Thank you in Igbo", "answer": "Daalụ"},
+    
+    # HAUSA
+    {"question": "Hello in Hausa", "answer": "Sannu"},
+    {"question": "Thank you in Hausa", "answer": "Na gode"},
+    
+    # AFRICAN INNOVATION
+    {"question": "M-Pesa", "answer": "Mobile money service launched in Kenya in 2007"},
+    {"question": "Andela", "answer": "Trains African software developers for global companies"},
+]
+
+print(f"✅ African Knowledge Base: {len(simba_knowledge_base)} entries")
+
+# =====================
+# SEARCH SYSTEM
+# =====================
+
+try:
+    embedder = SentenceTransformer('all-MiniLM-L6-v2')
+    questions = [item["question"] for item in simba_knowledge_base]
+    question_embeddings = embedder.encode(questions)
+
+    dimension = question_embeddings.shape[1]
+    index = faiss.IndexFlatIP(dimension)
+    faiss.normalize_L2(question_embeddings)
+    index.add(question_embeddings)
+    print("✅ Search System Ready!")
+except Exception as e:
+    print(f"❌ Search system error: {e}")
+    index = None
+
+def simba_search(query, top_k=2):
+    """Search African knowledge base"""
+    if index is None:
+        return simba_knowledge_base[:top_k]
+    
+    try:
+        query_embedding = embedder.encode([query])
+        faiss.normalize_L2(query_embedding)
+        scores, indices = index.search(query_embedding, top_k)
+        
+        results = []
+        for i, idx in enumerate(indices[0]):
+            if idx < len(simba_knowledge_base):
+                results.append({
+                    "question": simba_knowledge_base[idx]["question"],
+                    "answer": simba_knowledge_base[idx]["answer"],
+                    "score": scores[0][i]
+                })
+        return results
+    except:
+        return simba_knowledge_base[:top_k]
+
+# =====================
+# SIMBA AI CORE FUNCTION
+# =====================
+
+def generate_simba_response(message):
+    """Core function to generate Simba AI response"""
+    
+    start_time = time.time()
+    
+    # Quick responses for common greetings
+    quick_responses = {
+        "hello": "🦁 Báwo ni! Hello! I'm Simba AI, the first African LLM.",
+        "hi": "🦁 Báwo ni! Welcome to Simba AI!",
+        "hey": "🦁 Hello! I'm Simba AI, specializing in African languages and coding.",
+    }
+    
+    lower_message = message.lower().strip()
+    if lower_message in quick_responses:
+        return {
+            "response": quick_responses[lower_message],
+            "response_time": round(time.time() - start_time, 2),
+            "knowledge_used": ["quick_response"]
+        }
+    
+    try:
+        # Search for relevant knowledge
+        search_results = simba_search(message, top_k=2)
+        
+        # Build context
+        context = "African Knowledge Reference:\n"
+        for i, result in enumerate(search_results, 1):
+            context += f"{i}. {result['question']}: {result['answer']}\n"
+        
+        # Build prompt
+        prompt = f"User: {message}\nAfrican Knowledge: {context}\nSimba AI:"
+        
+        # Generate response
+        inputs = tokenizer.encode(prompt, return_tensors="pt", max_length=512, truncation=True)
+        
+        with torch.no_grad():
+            outputs = model.generate(
+                inputs,
+                max_new_tokens=100,
+                temperature=0.7,
+                do_sample=True,
+                pad_token_id=tokenizer.eos_token_id,
+            )
+        
+        full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Extract response
+        if "Simba AI:" in full_response:
+            response = full_response.split("Simba AI:")[-1].strip()
+        else:
+            response = full_response[len(prompt):].strip()
+        
+        if not response.startswith("🦁"):
+            response = f"🦁 {response}"
+            
+        response_time = round(time.time() - start_time, 2)
+        
+        return {
+            "response": response,
+            "response_time": response_time,
+            "knowledge_used": [r["question"] for r in search_results],
+            "model": model_name
+        }
+        
+    except Exception as e:
+        return {
+            "response": f"🦁 Simba AI is currently learning. Please try again!",
+            "response_time": round(time.time() - start_time, 2),
+            "error": str(e)
+        }
+
+# =====================
+# API ROUTES
+# =====================
+
+@app.route('/')
+def home():
+    return jsonify({
+        "message": "🦁 Simba AI Backend API - First African LLM",
+        "status": "running",
+        "endpoints": {
+            "/api/chat": "POST - Chat with Simba AI",
+            "/api/health": "GET - Health check",
+            "/api/info": "GET - API information"
+        }
+    })
+
+@app.route('/api/health')
+def health_check():
+    return jsonify({
+        "status": "healthy",
+        "model": model_name,
+        "timestamp": time.time()
+    })
+
+@app.route('/api/info')
+def api_info():
+    return jsonify({
+        "name": "Simba AI - First African LLM",
+        "version": "1.0",
+        "model": model_name,
+        "capabilities": [
+            "African Languages: Yoruba, Swahili, Igbo, Hausa",
+            "Python Coding & Programming",
+            "Mathematics & Problem Solving",
+            "African Innovation Knowledge"
+        ],
+        "knowledge_base_size": len(simba_knowledge_base)
+    })
+
+@app.route('/api/chat', methods=['POST'])
+def chat():
+    try:
+        data = request.get_json()
+        
+        if not data or 'message' not in data:
+            return jsonify({
+                "error": "Missing 'message' in request body"
+            }), 400
+        
+        user_message = data['message']
+        
+        if not user_message.strip():
+            return jsonify({
+                "error": "Message cannot be empty"
+            }), 400
+        
+        # Generate response
+        result = generate_simba_response(user_message)
+        
+        return jsonify({
+            "success": True,
+            "user_message": user_message,
+            "simba_response": result["response"],
+            "response_time": result["response_time"],
+            "knowledge_used": result.get("knowledge_used", []),
+            "model": result.get("model", model_name),
+            "timestamp": time.time()
+        })
+        
+    except Exception as e:
+        return jsonify({
+            "success": False,
+            "error": str(e),
+            "timestamp": time.time()
+        }), 500
+
+# =====================
+# LAUNCH
+# =====================
+
+if __name__ == '__main__':
+    print("🎯 Simba AI Backend API Ready!")
+    print("🌐 Endpoints:")
+    print("   GET  /api/health - Health check")
+    print("   GET  /api/info - API information") 
+    print("   POST /api/chat - Chat with Simba AI")
+    print("\n🚀 Starting server...")
+    app.run(host='0.0.0.0', port=7860, debug=False)