Update app.py

app.py

@@ -1,65 +1,63 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
 import os
-import requests
+from huggingface_hub import InferenceClient
+import io
 import base64
-import urllib.parse
-from duckduckgo_search import DDGS
 
-# 🔱 CONFIGURATION
-HF_TOKEN = os.getenv("HF_TOKEN")
-PRIMARY_MODEL = "deepseek-ai/DeepSeek-V3.2"
-client = InferenceClient(token=HF_TOKEN)
+# 🛡️ CLIENT SETUP
+HF_TOKEN = os.getenv("HF_TOKEN") 
 
-def mandatory_search(query):
-    try:
-        search_results = []
-        with DDGS() as ddgs:
-            results = list(ddgs.text(query, max_results=3))
-            for r in results:
-                search_results.append(f"Info: {r['body']}\nLink: {r['href']}")
-        return "\n\n".join(search_results) if search_results else "No live data."
-    except: return "Search offline."
+# DeepSeek-R1 (මෙය තමයි දැනට තියෙන බුද්ධිමත්ම 'Thinking' Model එක)
+text_client = InferenceClient("deepseek-ai/DeepSeek-R1", token=HF_TOKEN)
+image_client = InferenceClient("black-forest-labs/FLUX.1-schnell", token=HF_TOKEN)
 
-def dual_engine(message, history):
-    # 🔱 IMAGE GENERATION (/image [prompt])
+def forge_image(prompt):
+    try:
+        image = image_client.text_to_image(prompt)
+        buffered = io.BytesIO()
+        image.save(buffered, format="PNG")
+        img_str = base64.b64encode(buffered.getvalue()).decode()
+        return f"IMAGE_DATA:{img_str}"
+    except Exception as e:
+        return f"🔱 Error in Image Forge: {str(e)}"
+
+def dual_engine(message, history=None):
     if message.lower().startswith("/image "):
-        prompt = message.replace("/image ", "").strip()
-        img_url = f"https://image.pollinations.ai/prompt/{urllib.parse.quote(prompt)}?width=1024&height=1024&nologo=true&model=flux"
-        try:
-            res = requests.get(img_url, timeout=20)
-            return f"IMAGE_DATA:data:image/png;base64,{base64.b64encode(res.content).decode()}"
-        except: return "⚠️ Image Engine Busy."
-
-    # 🔱 VIDEO GENERATION (/video [prompt])
-    if message.lower().startswith("/video "):
-        prompt = message.replace("/video ", "").strip()
-        # මෙහිදී අපි Pollinations Video Engine එක භාවිතා කරමු (Fastest for UI)
-        video_url = f"https://image.pollinations.ai/prompt/{urllib.parse.quote(prompt)}?width=720&height=480&model=video"
-        return f"🔱 **COMMANDER, VIDEO FORGING STARTED...**\n\nමෙන්න ඔබේ වීඩියෝව: {video_url}\n\n*(සටහන: මෙම ලින්ක් එක ක්ලික් කිරීමෙන් ඔබේ වීඩියෝව නැරඹිය හැකියි)*"
-
-    # 🔱 TEXT CHAT ENGINE
-    live_data = mandatory_search(message)
-    system_instruction = f"IDENTIFICATION: MINZO AI (Created by MINZO-PRIME).\nLIVE DATA: {live_data}\nRespond strategically."
-
-    messages = [{"role": "system", "content": system_instruction}]
-    for user_msg, bot_msg in history[-6:]:
-        messages.append({"role": "user", "content": user_msg})
-        messages.append({"role": "assistant", "content": bot_msg})
-    messages.append({"role": "user", "content": message})
-
+        image_prompt = message.replace("/image ", "").strip()
+        return forge_image(image_prompt)
+    
     try:
-        response = client.chat_completion(messages=messages, model=PRIMARY_MODEL, max_tokens=1500)
-        return response.choices[0].message.content
-    except:
-        return "⚠️ Neural Link Busy. Retry in 5s."
-
-# 🔱 UI SETUP
-demo = gr.ChatInterface(
-    fn=dual_engine,
-    title="🔱 MINZO AI MULTIMODAL STUDIO",
-    description="Commands: /image [text] | /video [text] | Normal Chat (DeepSeek-V3)"
-)
+        # DeepSeek-R1 වැනි Reasoning models වලට system prompt එක අවශ්‍ය නැති තරම්
+        messages = [{"role": "user", "content": message}]
+        
+        response = ""
+        # R1 වලදී thinking process එක එන නිසා stream කිරීම අනිවාර්යයි
+        for message_obj in text_client.chat_completion(
+            messages,
+            max_tokens=4000, # Reasoning සඳහා වැඩි ඉඩක් ලබා දීම
+            stream=True,
+        ):
+            token = message_obj.choices[0].delta.content
+            if token:
+                response += token
+        
+        return response
+    
+    except Exception as e:
+        return f"⚠️ Neural Link Error: {str(e)}"
+
+# 🔱 INTERFACE
+with gr.Blocks() as demo:
+    input_text = gr.Textbox(label="Command Input")
+    output_text = gr.Textbox(label="Response")
+    submit_btn = gr.Button("Execute")
+    
+    submit_btn.click(
+        fn=dual_engine, 
+        inputs=[input_text], 
+        outputs=[output_text],
+        api_name="dual_engine"
+    )
 
 if __name__ == "__main__":
     demo.launch()