Update app.py

app.py

@@ -1,63 +1,82 @@
+import argparse
+
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", 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=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+from openai import OpenAI
+
+# Argument parser setup
+parser = argparse.ArgumentParser(
+    description='Chatbot Interface with Customizable Parameters')
+parser.add_argument('--model-url',
+                    type=str,
+                    default=' https://55ad-165-204-156-250.ngrok-free.app/v1',
+                    help='Model URL')
+parser.add_argument('-m',
+                    '--model',
+                    type=str,
+                    required=True,
+                    help='Model name for the chatbot')
+parser.add_argument('--temp',
+                    type=float,
+                    default=0.2,
+                    help='Temperature for text generation')
+parser.add_argument('--stop-token-ids',
+                    type=str,
+                    default='128001,128009',
+                    help='Comma-separated stop token IDs')
+parser.add_argument("--host", type=str, default=None)
+parser.add_argument("--port", type=int, default=8001)
+
+# Parse the arguments
+args = parser.parse_args()
+
+# Set OpenAI's API key and API base to use vLLM's API server.
+openai_api_key = "EMPTY"
+openai_api_base = args.model_url
+
+# Create an OpenAI client to interact with the API server
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
 )
 
 
-if __name__ == "__main__":
-    demo.launch()
+def predict(message, history):
+    # Convert chat history to OpenAI format
+    history_openai_format = [{
+        "role": "system",
+        "content": "You are a great ai assistant."
+    }]
+    for human, assistant in history:
+        history_openai_format.append({"role": "user", "content": human})
+        history_openai_format.append({
+            "role": "assistant",
+            "content": assistant
+        })
+    history_openai_format.append({"role": "user", "content": message})
+
+    # Create a chat completion request and send it to the API server
+    stream = client.chat.completions.create(
+        model="/app/model/llama3_8b_lora_indica_sft/",  # Model name to use
+        messages=history_openai_format,  # Chat history
+        temperature=args.temp,  # Temperature for text generation
+        stream=True,  # Stream response
+        extra_body={
+            'repetition_penalty':
+            1,
+            'stop_token_ids': [
+                int(id.strip()) for id in args.stop_token_ids.split(',')
+                if id.strip()
+            ] if args.stop_token_ids else []
+        })
+
+    # Read and return generated text from response stream
+    partial_message = ""
+    for chunk in stream:
+        partial_message += (chunk.choices[0].delta.content or "")
+        yield partial_message
+
+
+# Create and launch a chat interface with Gradio
+gr.ChatInterface(predict).queue().launch(server_name=args.host,
+                                         server_port=args.port,
+                                         share=True)