Update app.py

app.py

@@ -1,30 +1,33 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 from transformers.utils import get_json_schema
+import torch
 
 # -----------------------
-# Load model (CPU friendly)
+# Load model
 # -----------------------
 model_name = "bhaiyahnsingh45/functiongemma-multiagent-router"
 
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-
 model = AutoModelForCausalLM.from_pretrained(
-    model_name
+    model_name,
+    device_map="auto",
+    torch_dtype="auto"
 )
 
 # -----------------------
 # Agents
 # -----------------------
 def technical_support_agent(issue_type: str, priority: str) -> str:
-    return f"🛠️ Technical Support → {issue_type} ({priority})"
+    return f"🛠️ Routing to Technical Support: {issue_type} ({priority})"
 
 def billing_agent(request_type: str, urgency: str) -> str:
-    return f"💰 Billing → {request_type} ({urgency})"
+    return f"💰 Routing to Billing: {request_type} ({urgency})"
 
 def product_info_agent(query_type: str, category: str) -> str:
-    return f"📦 Product Info → {query_type} ({category})"
+    return f"📦 Routing to Product Info: {query_type} ({category})"
 
+# Tool schemas
 AGENT_TOOLS = [
     get_json_schema(technical_support_agent),
     get_json_schema(billing_agent),
@@ -34,7 +37,7 @@ AGENT_TOOLS = [
 SYSTEM_MSG = "You are an intelligent routing agent that directs customer queries to the appropriate specialized agent."
 
 # -----------------------
-# Inference
+# Core inference
 # -----------------------
 def route_query(user_query: str):
 
@@ -47,24 +50,28 @@ def route_query(user_query: str):
         messages,
         tools=AGENT_TOOLS,
         add_generation_prompt=True,
+        return_dict=True,
         return_tensors="pt"
     )
 
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+
     outputs = model.generate(
-        inputs,
-        max_new_tokens=100
+        **inputs,
+        max_new_tokens=128,
+        pad_token_id=tokenizer.eos_token_id
     )
 
     result = tokenizer.decode(
-        outputs[0],
+        outputs[0][len(inputs["input_ids"][0]):],
         skip_special_tokens=True
     )
 
-    return result.split("assistant")[-1].strip()
+    return result
 
 
 # -----------------------
-# Chat UI
+# Chatbot logic
 # -----------------------
 def chat_fn(message, history):
     response = route_query(message)
@@ -72,21 +79,19 @@ def chat_fn(message, history):
     return history, history
 
 
+# -----------------------
+# UI
+# -----------------------
 with gr.Blocks() as demo:
-    gr.Markdown("## 🤖 Multi-Agent Router (Fast CPU Demo)")
-    
-    chatbot = gr.Chatbot(height=400)
-    msg = gr.Textbox(placeholder="Ask something...")
-    
-    gr.Examples(
-        examples=[
-            "My app crashes when uploading files",
-            "I want a refund",
-            "What features are in premium plan?"
-        ],
-        inputs=msg
-    )
+    gr.Markdown("## 🤖 Multi-Agent Router Chatbot")
+    gr.Markdown("Ask anything about billing, product, or technical issues.")
+
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(placeholder="Type your query here...")
+    clear = gr.Button("Clear")
 
     msg.submit(chat_fn, [msg, chatbot], [chatbot, chatbot])
+    clear.click(lambda: None, None, chatbot, queue=False)
 
+# Launch
 demo.launch()