feat: using self api key to create openai client

.gitignore

@@ -0,0 +1 @@
+.env

README.md

@@ -11,4 +11,4 @@ license: mit
 short_description: Using self api key to talk with gpt
 ---
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+An sample for using your own OPENAI API in huggingface space

app.py

@@ -1,64 +1,41 @@
+from openai import OpenAI
 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")
+api_key = ""
+client = None
 
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+def initialize_openai(api_key):
+    print("Initializing OpenAI client for key: ", api_key[:10])
+    global client
+    client = OpenAI(api_key=api_key)
+    print("OpenAI API Initialized")
 
-    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})
+def generate_response(message, history):
+    if not client:
+        return "Please enter an API key to initialize the OpenAI API."
 
-    response = ""
+    formatted_history = []
+    for msg in history:
+        formatted_history.append({"role": msg["role"], "content": msg["content"]})
 
-    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
+    formatted_history.append({"role": "user", "content": message})
+    print("DEBUG formatted_history", formatted_history)
+    response = client.chat.completions.create(
+        model="gpt-3.5-turbo", messages=formatted_history, temperature=1.0
+    )
 
-        response += token
-        yield response
+    return response.choices[0].message.content
 
 
-"""
-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)",
-        ),
-    ],
-)
+with gr.Blocks() as demo:
+    api_key_input = gr.Textbox(
+        label="Enter API Key", type="password", placeholder="Enter your OpenAI API key"
+    )
+    api_key_input.change(initialize_openai, inputs=api_key_input, outputs=None)
+    chatbot = gr.Chatbot(height=300, type="messages")
+    gr.ChatInterface(fn=generate_response, type="messages", chatbot=chatbot)
 
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()

requirements.txt

@@ -1 +1,2 @@
-huggingface_hub==0.25.2
+gradio=5.7.1
+openai=1.51.0