Create app.py

app.py

@@ -0,0 +1,58 @@
+from fastapi import FastAPI, Header, HTTPException
+import torch
+import json
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from duckduckgo_search import DDGS
+
+app = FastAPI()
+
+# පද්ධතියේ මතකය (Storage සඳහා)
+LEARNING_FILE = "/data/elephant_learning_data.jsonl" # HF Storage path
+
+# 18GB RAM එකට ගැලපෙන පරිදි Mistral 4-bit වලින් Load කිරීම
+model_id = "mistralai/Mistral-7B-v0.3"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, 
+    torch_dtype=torch.bfloat16, 
+    device_map="auto",
+    load_in_4bit=True
+)
+
+# API Keys 50 (ELE-PRIME-001 to ELE-PRIME-050)
+API_KEYS = {f"ELE-PRIME-{i:03d}": {"credits": 5000} for i in range(1, 51)}
+
+@app.get("/")
+def read_root():
+    return {"message": "Elephant API Node 2026 is Online"}
+
+@app.post("/v1/chat")
+async def chat(message: dict, x_api_key: str = Header(None)):
+    if x_api_key not in API_KEYS:
+        raise HTTPException(status_code=403, detail="Invalid API Key")
+
+    user_query = message.get("query", "")
+    
+    # Web Search for 2026 Live Data
+    context = ""
+    try:
+        with DDGS() as ddgs:
+            results = [r['body'] for r in ddgs.text(user_query, max_results=2)]
+            context = "\n".join(results)
+    except:
+        context = "No live data available."
+
+    # Response Generation
+    input_text = f"Context: {context}\nUser: {user_query}\nAssistant:"
+    inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
+    outputs = model.generate(**inputs, max_new_tokens=256)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True).split("Assistant:")[-1].strip()
+
+    # Learning Loop: දත්ත පසුව Fine-tuning සඳහා Save කිරීම
+    log_data = {"q": user_query, "a": response, "key": x_api_key}
+    with open("learning_log.jsonl", "a") as f:
+        f.write(json.dumps(log_data) + "\n")
+
+    return {"reply": response, "status": "learned"}
+
+main = app