Create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+from huggingface_hub import InferenceClient
+import os
+from huggingface_hub import login
+
+# Fetch token from environment (automatically loaded from secrets)
+hf_token = os.getenv("gemma3")
+login(hf_token)
+
+# Initialize the client with your model
+client = InferenceClient("hackergeek/gemma-finetuned")
+
+def respond(
+    message: str,
+    history: list[tuple[str, str]],
+    system_message: str,
+    max_tokens: int,
+    temperature: float,
+    top_p: float,
+):
+    # Build a prompt from the system message and conversation history
+    prompt = f"{system_message}\n"
+    for user_msg, assistant_msg in history:
+        if user_msg:
+            prompt += f"User: {user_msg}\n"
+        if assistant_msg:
+            prompt += f"Assistant: {assistant_msg}\n"
+    prompt += f"User: {message}\nAssistant: "
+
+    # Call the text generation API with updated parameter name
+    response = client.text_generation(
+        model="hackergeek98/gemma-finetuned",
+        prompt=prompt,
+        max_new_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+    )
+
+    return response["generated_text"]
+
+# Set up the Gradio Chat Interface
+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)"),
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch()
+
+
+