Update app.py

app.py

@@ -1,70 +1,25 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-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")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    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,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = 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
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    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 transformers import pipeline
+
+# 1. Define the Model ID
+# Your model is already on the Hugging Face Hub, so you can load it directly.
+model_id = "dilexsan/bertweet_base_sentimental"
+
+# 2. Initialize the pipeline
+# The pipeline will automatically download the model and tokenizer.
+sentiment_analyzer = pipeline("text-classification", model=model_id)
+
+# 3. Create the Gradio Interface from the pipeline
+# Gradio automatically infers the input (Textbox) and output (Label/JSON)
+# components based on the pipeline's task.
+demo = gr.Interface.from_pipeline(
+    sentiment_analyzer,
+    title="BERTweet Base Sentiment Analyzer",
+    description="Text classification model for sentiment analysis using a Hugging Face pipeline."
 )
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
-
+# 4. Launch the demo
+# The launch() call is necessary for local testing but also serves as the entry point
+# for Hugging Face Spaces.
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()