Create app.py

app.py

@@ -0,0 +1,64 @@
+import gradio as gr
+from typing import List
+import requests
+import json
+
+def get_text_embedding(text: str, model: str = "mxbai-embed-large", api_url: str = "http://localhost:11434/api/embeddings") -> List[float]:
+    """
+    Sends a prompt to the embedding API and retrieves the embedding.
+
+    Args:
+        text (str): The text to embed.
+        model (str): The model to use for generating the embedding (default is "mxbai-embed-large").
+        api_url (str): The API endpoint URL (default is "http://localhost:11434/api/embeddings").
+
+    Returns:
+        list: A list of floats representing the embedding vector.
+
+    Raises:
+        Exception: If the API request fails.
+    """
+    payload = {
+        "model": model,
+        "prompt": text
+    }
+
+    try:
+        response = requests.post(api_url, data=json.dumps(payload), headers={"Content-Type": "application/json"})
+        response.raise_for_status()  # Raise an error for non-200 status codes
+        data = response.json()
+        return data.get("embedding", [])
+    except requests.exceptions.RequestException as e:
+        raise Exception(f"Error communicating with the embedding API: {e}")
+
+def process_text_to_embedding(text: str) -> str:
+    """Process the text input and return the embedding as a string."""
+    try:
+        embedding = get_text_embedding(text)
+        return json.dumps(embedding, indent=2)
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Define the Gradio interface
+def main():
+    title = "Text Embedding Generator"
+    description = "Enter a text input, and this tool will generate an embedding using the specified model via API."
+
+    with gr.Blocks() as demo:
+        gr.Markdown(f"# {title}")
+        gr.Markdown(description)
+
+        with gr.Row():
+            text_input = gr.Textbox(label="Input Text", placeholder="Enter your text here")
+        
+        with gr.Row():
+            output = gr.Textbox(label="Embedding Output", lines=10)
+
+        submit_button = gr.Button("Generate Embedding")
+
+        submit_button.click(fn=process_text_to_embedding, inputs=[text_input], outputs=[output])
+
+    demo.launch()
+
+if __name__ == "__main__":
+    main()