Update app.py

app.py

@@ -1,70 +1,111 @@
+# 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,
+#     type="messages",
+#     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)",
+#         ),
+#     ],
+# )
+
+# with gr.Blocks() as demo:
+#     with gr.Sidebar():
+#         gr.LoginButton()
+#     chatbot.render()
+
+
+# if __name__ == "__main__":
+#     demo.launch()
+
+
+
 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,
-    type="messages",
-    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 llama_cpp import Llama
+from huggingface_hub import hf_hub_download
+
+# Download your model automatically
+model_path = hf_hub_download(
+    repo_id="stevendhasoi/Iriseder",
+    filename="model_q4_k_m.gguf"
+)
+
+# Load GGUF model
+llm = Llama(
+    model_path=model_path,
+    n_ctx=2048,
+    n_threads=4,
 )
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+def chat_fn(message, history):
+    prompt = ""
+    for user, bot in history:
+        prompt += f"User: {user}\nAssistant: {bot}\n"
+
+    prompt += f"User: {message}\nAssistant:"
+
+    output = llm(
+        prompt,
+        max_tokens=256,
+        stop=["User:"],
+        echo=False
+    )
+
+    reply = output["choices"][0]["text"].strip()
+    return reply
+
+gr.ChatInterface(chat_fn).launch()
+
 
 
-if __name__ == "__main__":
-    demo.launch()