Update app.py

app.py

@@ -1,34 +1,27 @@
-import os
-import time
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-# Retrieve API token from Hugging Face Secrets
-HF_TOKEN = os.getenv("HF_TOKEN")
+client = InferenceClient("google/gemma-1.1-2b-it")
+client = InferenceClient("mistralai/Mistral-Nemo-Instruct-2407")
 
-# Ensure the token is available
-if not HF_TOKEN:
-    raise ValueError("Missing Hugging Face API Token! Set 'HF_TOKEN' in Hugging Face Secrets.")
+def models(Query): 
+    
+    messages = []
+    
+    messages.append({"role": "user", "content": f"[SYSTEM] You are ASSISTANT who answer question asked by user in short and concise manner. [USER] {Query}"})
 
-# Initialize clients with authentication
-client_gemma = InferenceClient("google/gemma-1.1-2b-it", token=HF_TOKEN)
-client_mistral = InferenceClient("mistralai/Mistral-Nemo-Instruct-2407", token=HF_TOKEN)
+    Response = ""
 
-# Function for normal fast responses
-def models(query): 
-    messages = [{"role": "user", "content": f"[SYSTEM] You are an AI assistant. Answer concisely. [USER] {query}"}]
-    
-    response = ""
-    try:
-        for message in client_gemma.chat_completion(messages, max_tokens=1024, stream=True):
-            token = message.choices[0].delta.content
-            response += token
-            yield response
-            time.sleep(0.3)  # Prevent rate limiting
-    except Exception as e:
-        yield f"Error: {str(e)}"
+    for message in client.chat_completion(
+        messages,
+        max_tokens=2048,
+        stream=True
+    ):
+        token = message.choices[0].delta.content
+
+        Response += token
+        yield Response
 
-# Function for detailed critical thinking responses
 def nemo(query):
     budget = 3
     message = f"""[INST] [SYSTEM] You are a helpful assistant in normal conversation.
@@ -61,30 +54,22 @@ Example format:
 <reflection> [Evaluation of the solution] </reflection>
 <reward> [Float between 0.0 and 1.0] </reward> [/INST] [INST] [QUERY] {query} [/INST] [ASSISTANT] """
 
+    stream = client.text_generation(message, max_new_tokens=4096, stream=True, details=True, return_full_text=False)
     output = ""
-    try:
-        stream = client_mistral.text_generation(message, max_new_tokens=2048, stream=True, details=True, return_full_text=False)
-        for response in stream:
-            output += response.token.text
-            yield output
-            time.sleep(0.3)  # Prevent rate limiting
-    except Exception as e:
-        yield f"Error: {str(e)}"
 
-# Description for Gradio UI
-description = "# Chat GO\n### Enter your query and get lightning-fast responses"
+    for response in stream:
+        output += response.token.text
+    return output
 
-# Gradio Interfaces
-with gr.Blocks() as demo1:
-    gr.Interface(fn=models, inputs=["text"], outputs="text", description=description)
+description="# Chat GO\n### Enter your query and Press enter and get lightning fast response"
 
+with gr.Blocks() as demo1:
+    gr.Interface(description=description,fn=models, inputs=["text"], outputs="text")
 with gr.Blocks() as demo2:
-    gr.Interface(fn=nemo, inputs=["text"], outputs="text", description="Critical Thinking Mode", api_name="critical_thinker", concurrency_limit=5)
+    gr.Interface(description="Very low but critical thinker",fn=nemo, inputs=["text"], outputs="text", api_name="critical_thinker", concurrency_limit=10)
 
-# Create a tabbed interface
 with gr.Blocks() as demo:
-    gr.TabbedInterface([demo1, demo2], ["Fast", "Critical"])
+    gr.TabbedInterface([demo1, demo2] , ["Fast", "Critical"])
 
-# Launch the application
-demo.queue(max_size=100000)  # Adjust max queue size
+demo.queue(max_size=300000)
 demo.launch()