Update app.py

app.py

@@ -1,64 +1,58 @@
 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)",
-        ),
-    ],
+# Initialize inference client
+client = InferenceClient(
+    provider="hf-inference",
+    # api_key=os.getenv("HF_TOKEN")
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+def zigistry_ai(user_query, package_data):
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are ZigistryAI, an expert assistant that understands the Zig programming language, "
+                "its ecosystem, and package data. You read structured package info and determine the best matches "
+                "to a user's request. Always explain reasoning briefly."
+            ),
+        },
+        {
+            "role": "user",
+            "content": f"{user_query}\nData: {package_data}",
+        },
+    ]
+
+    completion = client.chat.completions.create(
+        model="HuggingFaceTB/SmolLM3-3B",
+        messages=messages,
+        temperature=0.3,
+    )
+
+    return completion.choices[0].message.content
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## 🤖 Zigistry AI — Package Language Filter")
+    with gr.Row():
+        user_query = gr.Textbox(label="Your Query", value="Which of the following packages are implemented entirely in Zig?", lines=2)
+    with gr.Row():
+        package_data = gr.Textbox(label="Package Data (JSON)", lines=10, value='''[
+  {
+    "name": "zig-http",
+    "description": "Pure Zig HTTP server with no C dependencies",
+    "language_mix": "zig_only"
+  },
+  {
+    "name": "c-wrapper",
+    "description": "Zig bindings for a C library",
+    "language_mix": "zig_and_c"
+  }
+]''')
+    with gr.Row():
+        output = gr.Textbox(label="AI Response", lines=6)
+    submit_btn = gr.Button("Ask Zigistry AI")
+
+    submit_btn.click(zigistry_ai, inputs=[user_query, package_data], outputs=output)
+
+demo.launch()