initial commit

app.py

@@ -1,63 +1,38 @@
+from transformers import pipeline, set_seed, GenerationConfig, AutoModelForCausalLM
 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)",
-        ),
+import torch
+
+gpt2_generator = pipeline('text-generation', model='gpt2')
+tinyllama_generator = pipeline('text-generation', model='as-cle-bert/tinyllama-essay-scorer')
+
+def load_model(model_name):
+    global generator
+    generator = pipeline('text-generation', model=model_name, trust_remote_code=True)
+
+def generate_text(model, prompt, temperature, max_length, top_p):
+    if temperature == 0:
+        do_sample = False
+    else:
+        do_sample = True
+
+    load_model(model)
+
+    response = generator(prompt, max_length=max_length, do_sample=do_sample, temperature=temperature, top_p=top_p)[0]["generated_text"]
+    return response
+
+interface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.components.Dropdown(label="Choose a Model", choices=['gpt2', 'as-cle-bert/tinyllama-essay-scorer'], value='gpt2', info="Select the model for generating text."),
+        gr.components.Dropdown(label="Prompt", choices=['Write a tagline for an ice cream shop', 'Write a poem about spring', 'Write an introduction to the University of Zurich'], value='Write a tagline for an ice cream shop'),
+        gr.components.Slider(minimum=0, maximum=2, step=0.01, value = 1, label="Temperature", info = "(For τ = 1, the distribution is unchanged;For τ > 1, the distribution becomes more uniform; For τ < 1, the distribution becomes more peaked.)"),
+        gr.components.Slider(minimum=1, maximum=256, step=1, value = 16, label="Max Length", info ="(Maximum length is the maximum limit of the generated text.)"),
+        gr.components.Slider(minimum=0, maximum=1, step=0.01, value=1, label="Top-p", info="(Top-p sampling is to keep the top p percent of the probability mass.)")
     ],
+    outputs=[gr.Textbox(label="Output", lines=3, placeholder = "Hello, World!")],
+    title="Text Generation Control Panel",
+    description="Adjust the settings to control the text generation parameters."
 )
 
 
-if __name__ == "__main__":
-    demo.launch()
+interface.launch(share=True)