initial commit

README.md

@@ -1,6 +1,6 @@
 ---
-title: Gradio Chatbot
-emoji: 💬
+title: Kurunkathai
+emoji: 📚
 colorFrom: yellow
 colorTo: purple
 sdk: gradio
@@ -9,4 +9,4 @@ app_file: app.py
 pinned: false
 ---
 
-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).
+A space to showcase tiny story telling models from [Kurunkathai](https://ml4toddlers.github.io/kurunkathai/)

app.py

@@ -1,64 +1,45 @@
+from operator import ge
 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)",
+client = InferenceClient("tniranjan/finetuned_tinystories_33M_tinystories_ta")
+
+def generate(
+    model_name,
+    text,
+    max_new_tokens,
+    top_k
+):    
+    if model_name == "Medium-GPTNeo":
+        model = "tniranjan/finetuned_gptneo-base-tinystories-ta_v3"
+    elif model_name == "Small-GPTNeo":
+        model = "tniranjan/finetuned_tinystories_33M_tinystories_ta"
+    elif model_name == "Small-LLaMA":
+        model = "tniranjan/finetuned_Llama_tinystories_tinystories_ta"
+    return client.text_generation(
+        model=model,
+        prompt = text,
+        max_new_tokens=max_new_tokens,
+        top_k=top_k,
+    )
+
+demo = gr.Interface(
+    generate,
+    title="Kurunkathai: Tinystories in Tamil",
+    description="Generate Tamil stories for toddlers using Kurunkathai. Write the first line or so and click 'Submit' to generate a story.",
+    inputs=[
+        gr.Dropdown(
+            choices=["Medium-GPTNeo","Small-GPTNeo", "Small-LLaMA"],
+            label="Model",
+            value="Small-GPTNeo",
         ),
-    ],
-)
-
+        gr.Textbox(value="சிறிய குட்டி செல்லி, ஒரு அழகான நாய்க்குட்டியைக் கண்டாள்.", label="Text"),
+        gr.Number(minimum=25, maximum=250, value=100, step=1, label="Max new tokens"),
+        gr.Number(minimum=1,  maximum=150, value=35, step=1, label="Top-k"),   
+    ], 
+    outputs=[
+        gr.Textbox(label="Generated Story"),
+    ],)
 
 if __name__ == "__main__":
     demo.launch()