Update app.py

app.py

@@ -1,70 +1,27 @@
+# Instale o Gradio: !pip install gradio
 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)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
-
-if __name__ == "__main__":
-    demo.launch()
+from transformers import pipeline
+ 
+# Carrega o modelo (exatamente o mesmo código de antes)
+classifier = pipeline("zero-shot-classification")
+ 
+# Define a função que a interface web vai chamar
+def classificar_emoji(frase):
+    labels = ["😄", "😭", "😠", "🤔", "🎉", "❤️", "🚀"]
+    resultado = classifier(frase, candidate_labels=labels)
+    # Formata o resultado para o Gradio
+    # (retorna um dicionário de {label: score})
+    return {label: score for label, score in zip(resultado['labels'], resultado['scores'])}
+ 
+# Cria a interface
+# Inputs: "text" (caixa de texto)
+# Outputs: "label" (mostra os scores)
+# Título e descrição
+iface = gr.Interface(fn=classificar_emoji, 
+                     inputs="text", 
+                     outputs="label",
+                     title="Classificador de Emojis 🤖",
+                     description="Escreva uma frase e veja a IA sugerir o emoji perfeito.")
+ 
+# Lança a aplicação web
+iface.launch()