Update app.py

app.py

@@ -1,64 +1,46 @@
 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})
+from llama_cpp import Llama
+
+# Load the model (only once)
+llm = Llama.from_pretrained(
+    repo_id="google/gemma-3-1b-it-qat-q4_0-gguf",
+    filename="gemma-3-1b-it-q4_0.gguf",
+    n_ctx=32768,
+    verbose=False  # Mute llama.cpp logs
+)
 
-    response = ""
+# Define the function that runs the model
+def chat_with_gemma(user_input, temperature, top_p, frequency_penalty, presence_penalty):
+    full_prompt = f"{user_input}\nAnswer in no more than 150 words."
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
+    response = llm.create_chat_completion(
+        messages=[{"role": "user", "content": full_prompt}],
+        max_tokens=200,
         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)",
-        ),
+        frequency_penalty=frequency_penalty,
+        presence_penalty=presence_penalty
+    )
+
+    return response["choices"][0]["message"]["content"].strip()
+
+# Set up the Gradio interface
+demo = gr.Interface(
+    fn=chat_with_gemma,
+    inputs=[
+        gr.Textbox(label="Enter your message to Gemma"),
+        gr.Slider(0.0, 1.5, value=0.7, step=0.05, label="Temperature"),
+        gr.Slider(0.0, 1.0, value=0.9, step=0.05, label="Top-p (Nucleus Sampling)"),
+        gr.Slider(0.0, 2.0, value=0.4, step=0.1, label="Frequency Penalty"),
+        gr.Slider(0.0, 2.0, value=0.2, step=0.1, label="Presence Penalty")
     ],
+    outputs=gr.Textbox(label="Gemma's Response", lines=8),
+    title="Talk to Gemma",
+    description="Generate short responses using Google's Gemma model with adjustable settings."
 )
 
+# Launch the app
+demo.launch(share=True, enable_api=False)
 
-if __name__ == "__main__":
-    demo.launch()
+#demo.launch(auth=("username", "password"))
+#enable the above and remove the current demo.launch settings to enable api useage, but enable a password and username to prevent someone form using your api. Currently set to default username 'username' and default password 'password'.