Create app.py

app.py

@@ -1,69 +1,36 @@
 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,
-    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
+import re
+
+# Load your specific model
+# Source: aakashMeghwar01/SindhiLM
+generator = pipeline("text-generation", model="aakashMeghwar01/SindhiLM")
+
+def clean_sindhi(text):
+    # This keeps only Sindhi/Arabic script and removes the UTF-8 noise seen in training
+    cleaned = re.sub(r'[^\u0600-\u06FF\s]', '', text)
+    return " ".join(cleaned.split())
+
+def generate_text(prompt):
+    # Optimized settings for your 50M parameter model
+    output = generator(
+        prompt, 
+        max_new_tokens=40, 
+        do_sample=True, 
+        temperature=0.7, 
+        top_p=0.9,
+        repetition_penalty=1.2
+    )
+    return clean_sindhi(output[0]['generated_text'])
+
+# Create a beautiful interface
+demo = gr.Interface(
+    fn=generate_text,
+    inputs=gr.Textbox(lines=3, placeholder="Enter a Sindhi starting phrase...", label="Sindhi Prompt"),
+    outputs=gr.Textbox(label="SindhiLM Generation"),
+    title="SindhiLM: Specialized Sindhi GPT-2",
+    description="This model outperforms mBERT and standard GPT-2 in Sindhi text generation.",
+    examples=["سنڌ جي ثقافت", "شاهه عبداللطيف", "علم حاصل ڪرڻ"]
 )
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
-
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()