Update app.py

app.py

@@ -5,15 +5,12 @@ from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 # -------------------------
 # CONFIGURATION
 # -------------------------
-# Switched to Qwen open-access model
-MODEL_ID = "Qwen/Qwen2.5-VL-7B-Instruct"
+MODEL_ID = "Qwen/Qwen2.5-7B-Instruct"  # ✅ Text-only version
 
 # -------------------------
 # TOKEN AUTHENTICATION
 # -------------------------
-# This model still requires your Hugging Face token (must be added as secret named "HF_TOKEN")
 HF_TOKEN = os.getenv("HF_TOKEN")
-
 if not HF_TOKEN:
     raise ValueError("🚫 Hugging Face token not found. Please add 'HF_TOKEN' in your Space secrets.")
 
@@ -26,11 +23,11 @@ try:
         trust_remote_code=True,
         token=HF_TOKEN
     )
-    
+
     model = AutoModelForCausalLM.from_pretrained(
         MODEL_ID,
         trust_remote_code=True,
-        device_map="auto",   # Automatically chooses GPU if available
+        device_map="auto",
         token=HF_TOKEN
     )
 except Exception as e:
@@ -39,35 +36,23 @@ except Exception as e:
 # -------------------------
 # CREATE PIPELINE
 # -------------------------
-try:
-    pipe = pipeline(
-        "text-generation",
-        model=model,
-        tokenizer=tokenizer,
-        max_new_tokens=300,
-        do_sample=True,
-        temperature=0.7,
-    )
-except Exception as e:
-    raise RuntimeError(f"🚨 Failed to initialize pipeline: {e}")
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=300,
+    do_sample=True,
+    temperature=0.7,
+)
 
 # -------------------------
 # MAIN ASSISTANT FUNCTION
 # -------------------------
 def ai_assistant(command: str) -> str:
-    """
-    Interprets a user's natural language command and returns a response.
-    Uses instruction-style prompting.
-    """
     prompt = f"User: {command}\nAssistant:"
-    
     try:
         output = pipe(prompt)[0]["generated_text"]
-        # Remove the prompt portion to isolate the assistant's answer
-        if "Assistant:" in output:
-            response = output.split("Assistant:")[-1].strip()
-        else:
-            response = output.strip()
+        response = output.split("Assistant:")[-1].strip()
         return response
     except Exception as e:
         return f"⚠️ Error: {e}"