From 820a79db6ef5263c4a18d56c7f9e9120af19f378 Mon Sep 17 00:00:00 2001
From: Anindya Maiti <am@ou.edu>
Date: Sat, 15 Mar 2025 09:19:06 -0500
Subject: [PATCH] example api usage of chat.secretlab.page endpoints

---
 api-openwebui/deepseek_classify.py | 75 ++++++++++++++++++++++++++++++
 1 file changed, 75 insertions(+)
 create mode 100644 api-openwebui/deepseek_classify.py

diff --git a/api-openwebui/deepseek_classify.py b/api-openwebui/deepseek_classify.py
new file mode 100644
index 0000000..2f9631f
--- /dev/null
+++ b/api-openwebui/deepseek_classify.py
@@ -0,0 +1,75 @@
+import os
+import requests
+from dotenv import load_dotenv
+
+def main():
+    # 1. Load the environment file with your API key
+    home_dir = os.path.expanduser("~")
+    env_file = os.path.join(home_dir, ".secretlab.env")
+    load_dotenv(env_file)
+    api_key = os.getenv("SECRETLAB_API_KEY")
+
+    if not api_key:
+        raise ValueError("SECRETLAB_API_KEY not found in ~/.secretlab.env")
+
+    # 2. The correct endpoint
+    url = "https://chat.secretlab.page/api/chat/completions"
+
+    # 3. Sentences to classify
+    sentences = [
+        "I love sunny days!",
+        "This is a bland sandwich.",
+        "I really hate waiting in long lines.",
+        "The meeting was okay, nothing special.",
+        "I am so excited about the upcoming holidays!"
+    ]
+
+    # 4. For each sentence, send a separate request
+    for index, sentence in enumerate(sentences, start=1):
+        # Prepare the request payload
+        # We use Chat Completion format, but the system message strictly instructs the assistant
+        # to return only "Positive" or "Negative" with no extra explanation.
+        payload = {
+            "model": "deepseek-r1:32b",
+            "messages": [
+                {
+                    "role": "system",
+                    "content": (
+                        "You are a helpful text classifier. You must respond with only "
+                        "'Positive' or 'Negative' for the given text. No other words or punctuation."
+                    )
+                },
+                {
+                    "role": "user",
+                    "content": f"Classify this sentence as Positive or Negative:\n{sentence}"
+                }
+            ],
+            "temperature": 0.0
+        }
+
+        # Set the headers, including authorization
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key}"
+        }
+
+        # POST the request
+        response = requests.post(url, headers=headers, json=payload)
+
+        # Check for success
+        if response.status_code == 200:
+            completion = response.json()
+            # Extract the assistant's response text
+            result_text = completion["choices"][0]["message"]["content"].strip()
+
+            # Print the classification result for each sentence
+            print(f"Sentence {index}: {sentence}")
+            print(f"Classification: {result_text}")
+            print("---------")
+        else:
+            print(f"Request for sentence {index} failed with status code: {response.status_code}")
+            print("Response:", response.text)
+            print("---------")
+
+if __name__ == "__main__":
+    main()