Update app.py

app.py

@@ -1,6 +1,7 @@
 import os
 import random
 import requests
+import base64
 from flask import Flask, request, jsonify
 from flask_cors import CORS
 from datetime import datetime
@@ -15,7 +16,8 @@ except ImportError:
 app = Flask(__name__)
 CORS(app)
 
-# AUTHORIZED API KEYS
+# AUTHORIZED CONFIGURATION
+IMGBB_KEY = "c32f54279bcd7d4f766b5eac37fd7327"
 API_KEYS = [
     "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",
     "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",
@@ -24,103 +26,79 @@ API_KEYS = [
     "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"
 ]
 
-def fetch_web_intel(query):
-    """වැඩිදියුණු කළ වෙබ් සෙවුම් පද්ධතිය"""
-    intel = ""
+def search_intel(query):
+    """RAG-style web retrieval"""
+    intel_data = ""
     try:
-        ddgs = DDGS()
-        # සෙවුම් ප්‍රතිඵල 5ක් දක්වා වැඩි කර වඩාත් නිවැරදි තොරතුරු ලබා ගනී
-        results = ddgs.text(query, max_results=5)
-        if results:
-            for i, r in enumerate(results, 1):
-                intel += f"\n[{i}] Source: {r['title']}\nInfo: {r['body']}\n"
-        else:
-            intel = "No direct web matches found for the query."
-    except Exception:
-        intel = "Web intelligence node is currently throttled."
-    return intel
+        with DDGS() as ddgs:
+            results = list(ddgs.text(query, region='wt-wt', max_results=4))
+            if results:
+                for r in results:
+                    intel_data += f"\n- {r['title']}: {r['body']}"
+    except: intel_data = "Web access limited."
+    return intel_data
+
+def upload_to_imgbb(base64_image):
+    """Backend-side Image Upload to ImgBB"""
+    try:
+        payload = {"key": IMGBB_KEY, "image": base64_image}
+        res = requests.post("https://api.imgbb.com/1/upload", data=payload)
+        return res.json()['data']['url']
+    except: return None
 
 @app.route('/api/chat', methods=['POST'])
 def chat():
     try:
         data = request.json
         user_msg = data.get("message", "")
-        image_url = data.get("image_url") 
+        # Frontend එකෙන් 'image_base64' නමින් data එක එවිය යුතුයි
+        image_base_64 = data.get("image_base64") 
         tier = data.get("tier", "prime")
 
-        # 🔱 පද්ධතියේ මූලික මොඩල් සිතියම (Prime & Nexus අනිවාර්යයෙන්ම පවතී)
-        model_map = {
-            "prime": "minimax/minimax-m2.5:free",
-            "nexus": "tencent/hy3-preview:free"
-        }
+        final_image_url = None
+        if image_base_64:
+            # පින්තූරයක් තිබේ නම් එය Backend එකෙන් Upload කරයි
+            final_image_url = upload_to_imgbb(image_base_64)
 
-        # 🔱 AUTO-SWITCH Logic
-        if image_url:
-            # රූපයක් ඇති විට ස්වයංක්‍රීයව Vision මොඩල් එක තෝරා ගනී
+        # 🔱 Model Selection Logic
+        if final_image_url:
             selected_model = "nvidia/llama-nemotron-embed-vl-1b-v2:free"
-            active_tier = "VISION-EMBED"
-            web_context = "Image analysis in progress. Web search bypassed."
+            active_tier = "VISION-CORE"
+            web_context = "Image analysis mode active."
         else:
-            # රූපයක් නැති විට ඔබ ඉල්ලූ Prime හෝ Nexus භාවිතා කරයි
+            model_map = {"prime": "minimax/minimax-m2.5:free", "nexus": "tencent/hy3-preview:free"}
             selected_model = model_map.get(tier, "minimax/minimax-m2.5:free")
             active_tier = tier.upper()
-            # Web Search එක මෙහිදී ක්‍රියාත්මක වේ
-            web_context = fetch_web_intel(user_msg)
+            web_context = search_intel(user_msg)
 
-        current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        
         headers = {
             "Authorization": f"Bearer {random.choice(API_KEYS)}",
             "Content-Type": "application/json",
-            "HTTP-Referer": "https://huggingface.co/spaces/minzo456/Elephant-AI-Core",
-            "X-Title": "Elephant AI Core"
+            "HTTP-Referer": "https://huggingface.co/spaces/minzo456/Elephant-AI-Core"
         }
 
-        # System Prompt එක තුළට සෙවුම් දත්ත ඇතුළත් කිරීම
-        system_prompt = (
-            f"You are the {active_tier} core of Elephant AI, built by MINZO-PRIME.\n"
-            f"Current Time: {current_time}\n"
-            f"Real-time Web Context: {web_context}\n"
-            "Maintain your sovereign, high-intelligence persona at all times."
-        )
-
-        # Message Content සකස් කිරීම (Text or Multimodal)
-        if image_url:
-            user_content = [
-                {"type": "text", "text": user_msg},
-                {"type": "image_url", "image_url": {"url": image_url}}
-            ]
-        else:
-            user_content = user_msg
+        # Payload Assembly
+        content = [{"type": "text", "text": user_msg}]
+        if final_image_url:
+            content.append({"type": "image_url", "image_url": {"url": final_image_url}})
 
         payload = {
             "model": selected_model,
             "messages": [
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": user_content}
-            ],
-            "temperature": 0.6 # නිරවද්‍යතාවය සහ නිර්මාණශීලිත්වය අතර සමබරතාවය සඳහා
+                {"role": "system", "content": f"You are {active_tier} mode of Elephant AI by MINZO-PRIME. Web Data: {web_context}"},
+                {"role": "user", "content": content}
+            ]
         }
 
-        resp = requests.post(
-            "https://openrouter.ai/api/v1/chat/completions", 
-            headers=headers, 
-            json=payload, 
-            timeout=60
-        )
-        
-        if resp.status_code == 200:
-            ai_reply = resp.json()['choices'][0]['message']['content']
-            return jsonify({"reply": ai_reply})
-        else:
-            return jsonify({"reply": f"Node Error: {resp.status_code} - {resp.text}"}), resp.status_code
+        resp = requests.post("https://openrouter.ai/api/v1/chat/completions", headers=headers, json=payload, timeout=60)
+        return jsonify({"reply": resp.json()['choices'][0]['message']['content']})
 
     except Exception as e:
         return jsonify({"reply": f"Core Disruption: {str(e)}"}), 500
 
 @app.route('/')
 def health():
-    return "🐘 Elephant AI Core: ONLINE | Operator: MINZO-PRIME | Web Intel: ENABLED"
+    return "🐘 Elephant AI Core: ONLINE | Operator: MINZO-PRIME"
 
 if __name__ == "__main__":
     app.run(host='0.0.0.0', port=7860)