Update app.py

app.py

@@ -1,64 +1,43 @@
 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 transformers import pipeline
+
+# Load a text generation pipeline from Hugging Face
+rap_generator = pipeline("text-generation", model="gpt2")  # You can use a rap-specific model if available
+
+def generate_rap(quantity, style, countries, theme, word_list, freestyle):
+    # Format the prompt for the AI model
+    prompt = (
+        f"{quantity} lines of {style} rap inspired by {' and '.join(countries)} "
+        f"about {theme}. Featuring words like {' '.join(word_list)}. "
+        f"{'Freestyle' if freestyle else 'Structured'}."
+    )
+    # Generate rap lyrics using the model
+    response = rap_generator(prompt, max_length=50, num_return_sequences=1)
+    return response[0]['generated_text']
+
+demo = gr.Interface(
+    generate_rap,
+    [
+        gr.Slider(2, 20, value=4, label="Number of Lines", info="Choose the number of lines for your rap."),
+        gr.Dropdown(
+            ["chill", "hardcore", "freestyle", "battle"], label="Rap Style", info="Select the style of rap."
         ),
+        gr.CheckboxGroup(["USA", "Japan", "UK", "France"], label="Countries", info="Where's the rap vibe from?"),
+        gr.Radio(["love", "struggle", "success", "hustle"], label="Theme", info="What’s the rap about?"),
+        gr.Dropdown(
+            ["money", "dream", "fight", "night"], value=["money", "fight"], multiselect=True,
+            label="Key Words", info="Words to include in the rap."
+        ),
+        gr.Checkbox(label="Freestyle", info="Make it a freestyle?")
     ],
+    "text",
+    examples=[
+        [4, "chill", ["USA"], "love", ["money", "dream"], True],
+        [10, "hardcore", ["UK", "Japan"], "hustle", ["fight", "night"], False],
+        [8, "battle", ["France"], "struggle", ["dream"], True],
+    ]
 )
 
-
 if __name__ == "__main__":
     demo.launch()
+