uses openrouter

code/main.py

@@ -2,11 +2,21 @@
 import base64
 import json
 import os
+import re
 import urllib.request
 
+from dotenv import load_dotenv
+
+_env_path = os.path.join(os.path.dirname(__file__), '.env')
+if os.path.exists(_env_path):
+    load_dotenv(_env_path)
+
 import openai
 
-client = openai.OpenAI(api_key="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")
+client = openai.OpenAI(
+    api_key=os.environ["OPENROUTER_API_KEY"],
+    base_url="https://openrouter.ai/api/v1",
+)
 
 
 def slurp_file(filename):
@@ -36,7 +46,7 @@ IMPORTANT:
 - Do NOT skip entries because some fields are missing. Extract what you can.
 - For statements/summaries, each row in an invoice table is a separate invoice entry.
 - If OCR fails completely and no text can be extracted at all, return an array with one object containing only the explanation field.
-- Your FINAL response to the user must be ONLY a JSON array. Do NOT wrap it in markdown code blocks. Do NOT add any prose before or after the JSON."""
+- Your FINAL response must be ONLY a JSON array. Do NOT wrap it in markdown code blocks. Do NOT add any prose before or after the JSON."""
 
 
 def analyze_pdf(pdf_path):
@@ -45,28 +55,32 @@ def analyze_pdf(pdf_path):
 
     base64_string = base64.b64encode(pdf_data).decode("utf-8")
 
-    response = client.responses.create(
-        model="gpt-4o",
-        instructions=BASE_PROMPT,
-        input=[
+    response = client.chat.completions.create(
+        model="openai/gpt-4o",
+        messages=[
+            {
+                "role": "system",
+                "content": BASE_PROMPT,
+            },
             {
                 "role": "user",
                 "content": [
                     {
-                        "type": "input_file",
-                        "filename": os.path.basename(pdf_path),
-                        "file_data": f"data:application/pdf;base64,{base64_string}",
+                        "type": "file",
+                        "file": {
+                            "filename": os.path.basename(pdf_path),
+                            "file_data": f"data:application/pdf;base64,{base64_string}",
+                        },
                     },
                     {
-                        "type": "input_text",
+                        "type": "text",
                         "text": "extract the invoice(s) and/or credit(s) details from this document.",
                     },
                 ],
-            }
+            },
         ],
     )
-    text = response.output_text
-    import re
+    text = response.choices[0].message.content
     match = re.search(r'```(?:json)?\s*\n(.*?)\n```', text, re.DOTALL)
     if match:
         text = match.group(1)