Update app.py

app.py

@@ -1,41 +1,118 @@
+# --- 🔱 वेदिका 3.5 फ्लैश: भारत का अपना 2B AI (Super Fast Version) ---
+# रचयिता एवं मार्गदर्शक: दिव्य पटेल जी | भारत 🇮🇳
+
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+from threading import Thread
+from PIL import Image
+import os
+import cv2
+
+print("🔱 भारत का अजेय AI 'वेदिका 3.5 फ्लैश' सुपर-फास्ट मोड में जागृत हो रहा है...")
+
+# CPU Optimization
+os.environ["OMP_NUM_THREADS"] = "2"
+torch.set_num_threads(2)
+
+MODEL_ID = "pateltraders55455/Vedika-3.5-flash"
+
+try:
+    print(f"🔱 '{MODEL_ID}' (2B) लोड किया जा रहा है...")
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_ID,
+        device_map="cpu",
+        torch_dtype=torch.bfloat16,
+        low_cpu_mem_usage=True,
+        trust_remote_code=True
+    )
+    print("🔱 विजय! 'वेदिका 3.5 फ्लैश' तैयार है!")
+except Exception as e:
+    print(f"🔱 मॉडल लोडिंग में त्रुटि: {e}")
+    model, tokenizer = None, None
+
 def generate_vedika_magic(message, history, image=None, video=None):
     if model is None or tokenizer is None:
-        return history + [{"role": "assistant", "content": "🔱 सिस्टम त्रुटि: मॉडल लोड नहीं हो सका।"}]
+        return history + [{"role": "assistant", "content": "🔱 सिस्टम त्रुटि: मोडल लोड नहीं हो सका।"}]
 
-    # हिस्ट्री को सुरक्षित रखें
     recent_history = history[-2:] if len(history) > 2 else history
-
-    # सिस्टम इंस्ट्रक्शन
-    system_instruction = """You are 'Vedika 3.5 Flash'..."""
+    
+    system_instruction = """You are 'Vedika 3.5 Flash'...
+    
+    <think>
+    1. Analyze the user's query carefully.
+    2. Break down the problem into smaller logical steps.
+    3. Consider different solutions or facts.
+    4. Formulate the best response.
+    </think>
+    
+    [Your final, polished answer goes here, OUTSIDE the think tags.]
+    """
 
     messages = [{"role": "system", "content": system_instruction}]
+    
     for user_msg, ai_msg in recent_history:
         messages.append({"role": "user", "content": user_msg})
         messages.append({"role": "assistant", "content": ai_msg})
-
+    
     if image is not None:
         messages.append({"role": "user", "content": "Describe this image."})
     if video is not None:
         messages.append({"role": "user", "content": "Describe this video."})
-
+    
     messages.append({"role": "user", "content": message})
-
+    
     try:
         text_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
         inputs = tokenizer([text_prompt], return_tensors="pt").to(model.device)
-
+        
         streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
-        generate_kwargs = dict(**inputs, streamer=streamer, max_new_tokens=512, temperature=1, top_p=0.9, do_sample=True, use_cache=True)
+        
+        generate_kwargs = dict(
+            **inputs,
+            streamer=streamer,
+            max_new_tokens=512,
+            temperature=1,
+            top_p=0.9,
+            do_sample=True,
+            use_cache=True
+        )
 
         t = Thread(target=model.generate, kwargs=generate_kwargs)
         t.start()
 
+# पिछले स्ट्रिमर लूप को जारी रखते हैं
         response_text = ""
         for new_token in streamer:
             response_text += new_token
 
-        # अब सही फॉर्मेट में लौटाएँ
+        # सही फॉर्मेट में परिणाम को लौटाएँ ताकि Gradio को समस्या न हो
         return history + [{"role": "assistant", "content": response_text}]
 
     except Exception as e:
-        return history + [{"role": "assistant", "content": f"🔱 प्रसंस्करण त्रुटि: {str(e)}"}]
+        return history + [{"role": "assistant", "content": f"🔱 प्रसंस्करण त्रुटि: {str(e)}"}]
+
+# =============================================================================
+# 🔱 वेदिका 3.5 फ्लैश का मल्टीमीडिया UI + Send बटन
+# ============================================================================
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🔱 Vedika 3.5 Flash (Super Fast)")
+
+    with gr.Row():
+        text_input = gr.Textbox(placeholder="वेदिका 3.5 फ्लैश से कुछ भी पूछें...")
+        image_input = gr.Image(type="filepath", label="Upload Photo")
+        video_input = gr.Video(label="Upload Video")
+    
+    send_btn = gr.Button("Send")
+    chat_output = gr.Chatbot()
+
+    def chat_fn(message, history, image, video):
+        return generate_vedika_magic(message, history, image, video)
+
+    send_btn.click(chat_fn, [text_input, chat_output, image_input, video_input], chat_output)
+
+if __name__ == "__main__":
+    demo.launch()
+