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LICENSE

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app.py

@@ -1,55 +1,55 @@
+import sys
+import spaces
+import os
 import gradio as gr
-from huggingface_hub import InferenceClient
+import torch
+import torch
+import threading
+from together import Together
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    TextIteratorStreamer,
+)
 
+TOGETHER_MODEL_ID = "EssentialAI/RNJ-1-instruct"  
+MAX_INPUT_TOKEN_LENGTH = 8192
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+client = Together()
 
-    messages = [{"role": "system", "content": system_message}]
+def chat_fn(message, history, system_message, max_tokens, temperature, top_p):
 
-    messages.extend(history)
+    # Add system prompt
+    messages = [{"role": "system", "content": system_message}]
+    # Convert history into messages list
+    for user_msg, bot_msg in history:
+        messages.append({"role": "user", "content": user_msg})
+        messages.append({"role": "assistant", "content": bot_msg})
 
+    # Append latest user message
     messages.append({"role": "user", "content": message})
 
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
+    response = client.chat.completions.create(
+        model=TOGETHER_MODEL_ID,
+        messages=messages,
         temperature=temperature,
         top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
+        max_tokens=max_tokens,
+    )
 
-        response += token
-        yield response
+    response_str = response.choices[0].message.content
+    print(response.choices[0].message.content)
 
+    yield response_str
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
+demo = gr.ChatInterface(
+    fn=chat_fn,
+    title="Chat Demo with RNJ1",
+    description=" Chat with RNJ1",
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Textbox(value="You are a helpful AI assistant.", label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.2, step=0.1, label="Temperature"),
         gr.Slider(
             minimum=0.1,
             maximum=1.0,
@@ -58,13 +58,12 @@ chatbot = gr.ChatInterface(
             label="Top-p (nucleus sampling)",
         ),
     ],
+    examples=[
+           ["Hello there! How are you doing?"],
+           ["Can you write a Python program that adds two numbers?"],
+           ["Which one of these mountains is not located in Europe? Hoverla, Mont-Blanc, Gran Paradiso, Everest"],
+       ]
 )
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
-
 if __name__ == "__main__":
     demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+together
+transformers==4.51.2
+torch