first minimal working version using gradio

Makefile

@@ -0,0 +1,2 @@
+deps:
+	poetry export --without-hashes --format=requirements.txt > requirements.txt

app.py

@@ -0,0 +1,34 @@
+import os
+
+import gradio as gr
+import weaviate
+
+collection_name = "Chunk"
+
+
+def predict(input_text):
+    client = weaviate.Client(
+        url=os.environ["WEAVIATE_URL"],
+        auth_client_secret=weaviate.AuthApiKey(api_key=os.environ["WEAVIATE_API_KEY"]),
+        additional_headers={
+            "X-OpenAI-Api-Key": os.environ["OPENAI_API_KEY"]
+        }
+    )
+
+    return (
+        client.query
+        .get(class_name=collection_name, properties=["text"])
+        .with_near_text({"concepts": input_text})
+        .with_limit(1)
+        .with_generate(single_prompt="{text}")
+        .do()
+    )
+
+iface = gr.Interface(
+    fn=predict,  # the function to wrap
+    inputs="text",  # the input type
+    outputs="text",  # the output type
+)
+
+if __name__ == "__main__":
+    iface.launch()

pyproject.toml

@@ -12,6 +12,7 @@ llama-index = "^0.8.29.post1"
 weaviate-client = "^3.24.1"
 pypdf = "^3.16.1"
 goldenverba = "^0.2.3"
+gradio = "^3.44.4"
 
 
 [build-system]