Update app.py

app.py

@@ -1,82 +1,49 @@
 from fastapi import FastAPI, Request
-from transformers import pipeline
-import torch
 import requests
-import re
-import urllib.parse
-from bs4 import BeautifulSoup
-import feedparser
+import json
 from fastapi.middleware.cors import CORSMiddleware
 import uvicorn
 
 app = FastAPI()
 app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
 
-# 🔱 Engine Initialization
-print("🔱 Initializing Neural Core with Advanced Search...")
-pipe = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B-Instruct", device_map="auto", torch_dtype=torch.bfloat16)
-
-# 🔱 WormGPT logic එකෙන් ගත් RSS Feeds
-RSS_FEEDS = {
-    'exploits': ['https://www.exploit-db.com/rss.xml', 'https://cve.circl.lu/last/rss.xml'],
-    'hacking': ['https://feeds.feedburner.com/TheHackersNews', 'https://www.darkreading.com/rss.xml'],
-    'sri_lanka': ['https://www.adaderana.lk/rss.php'],
-    'tech': ['https://feeds.bbci.co.uk/news/technology/rss.xml']
-}
-
-def fetch_rss_logic(query):
-    """ප්‍රශ්නය අනුව අදාළ RSS Feed එක පරීක්ෂා කිරීම"""
-    category = 'tech'
-    if any(w in query.lower() for w in ['exploit', 'cve', 'hack']): category = 'exploits'
-    elif any(w in query.lower() for w in ['lanka', 'colombo']): category = 'sri_lanka'
-    
-    for url in RSS_FEEDS.get(category, RSS_FEEDS['tech']):
-        try:
-            feed = feedparser.parse(url)
-            if feed.entries:
-                return "\n".join([f"News: {e.title} - {e.summary[:200]}" for e in feed.entries[:3]])
-        except: continue
-    return ""
-
-def duckduckgo_search(query):
-    """DuckDuckGo හරහා Web තොරතුරු සෙවීම"""
-    try:
-        url = f"https://html.duckduckgo.com/html/?q={urllib.parse.quote(query)}"
-        headers = {'User-Agent': 'Mozilla/5.0'}
-        r = requests.get(url, headers=headers, timeout=10)
-        soup = BeautifulSoup(r.text, 'html.parser')
-        results = []
-        for a in soup.find_all('a', class_='result__a', limit=3):
-            results.append(a.get_text())
-        return "\n".join(results)
-    except: return ""
+# 🔱 GLM-5.1 CONFIGURATION
+API_URL = "https://api.us-west-2.modal.direct/v1/chat/completions"
+API_KEY = "modalresearch_0yKf_z9azDFpOXr-W1N1WsmBUgXf6Ux-yvpVsFQWzks"
+MODEL_ID = "zai-org/GLM-5.1-FP8"
 
 @app.post("/generate")
 async def generate(request: Request):
     try:
         data = await request.json()
         user_prompt = data.get("prompt")
-        
-        # 🔱 Web Search Logic Execution
-        search_info = ""
-        if any(k in user_prompt.lower() for k in ['news', 'latest', 'today', 'hack', 'cve']):
-            search_info = fetch_rss_logic(user_prompt)
-        
-        if not search_info:
-            search_info = duckduckgo_search(user_prompt)
 
-        # 🔱 Neural Response Generation
-        messages = [
-            {"role": "system", "content": f"You are Elephant AI Pro by MINZO-PRIME. Live Data: {search_info}"},
-            {"role": "user", "content": user_prompt},
-        ]
-        
-        prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-        outputs = pipe(prompt, max_new_tokens=500, do_sample=True, temperature=0.7)
-        response = outputs[0]["generated_text"].split("<|im_start|>assistant")[-1].strip()
-        
-        return {"response": response}
-    
+        # 🔱 Payload for GLM-5.1
+        payload = {
+            "model": MODEL_ID,
+            "messages": [
+                {"role": "system", "content": "You are Elephant AI Pro, a master-level coding assistant built by MINZO-PRIME. Provide highly detailed and functional code."},
+                {"role": "user", "content": user_prompt}
+            ],
+            "max_tokens": 1024,
+            "temperature": 0.6
+        }
+
+        headers = {
+            "Authorization": f"Bearer {API_KEY}",
+            "Content-Type": "application/json"
+        }
+
+        # 🔱 Fast-Inference Request
+        response = requests.post(API_URL, headers=headers, json=payload, timeout=60)
+        result = response.json()
+
+        if "choices" in result:
+            ai_response = result["choices"][0]["message"]["content"]
+            return {"response": ai_response}
+        else:
+            return {"response": "SYSTEM ERROR: API Node non-responsive. Check Token status."}
+
     except Exception as e:
         return {"error": str(e)}