model path

app.py

@@ -1,64 +1,48 @@
 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 huggingface_hub import hf_hub_download
+from llama_cpp import Llama
+
+# Replace with your actual HF model repo and filename
+model_repo = "AravindKumarRajendran/WhiZ-gemma-3n-4b"
+model_filename = "gemma-3n-4b-it-finetune.Q8_0.gguf"  # Exact GGUF file name in repo
+
+# Download GGUF model from HF Hub (caches locally)
+model_path = hf_hub_download(repo_id=model_repo, filename=model_filename)
+
+# Load model with llama-cpp
+llm = Llama(
+    model_path=model_path,
+    n_ctx=2048,
+    n_threads=4,
+    n_batch=64,
+    verbose=False
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+# Chat handler
+def chat_with_model(history, user_input):
+    history.append(("🧑‍💻: " + user_input, ""))
+    prompt = f"{user_input} தமிழில் பதிலளி:"
+
+    output = llm(
+        prompt,
+        max_tokens=128,
+        temperature=0.7,
+        stop=["</s>"],
+    )
+
+    reply = output["choices"][0]["text"].strip()
+    history[-1] = (history[-1][0], "🤖: " + reply)
+    return history, ""
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 🗣️ தமிழில் உரையாடல் (Tamil Chatbot - GGUF on CPU)")
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="உங்கள் செய்தி", placeholder="Type your message...")
+    clear = gr.Button("🧹 Clear Chat")
+    state = gr.State([])
+
+    msg.submit(chat_with_model, [state, msg], [chatbot, msg])
+    clear.click(lambda: ([], ""), None, [chatbot, msg, state])
+
+demo.launch()

requirements.txt

@@ -1 +1,3 @@
-huggingface_hub==0.25.2
+huggingface_hub==0.25.2
+llama-cpp-python==0.2.58
+gradio