feat: add MiniMax provider support for prompt enhancement

- Add _get_llm_client() helper to auto-select LLM provider from env vars
- Support MINIMAX_API_KEY to use MiniMax (MiniMax-M2.7) as provider
- MINIMAX_API_KEY takes priority over OPENAI_API_KEY when both are set
- Default base URL: https://api.minimax.io/v1 (overridable via MINIMAX_BASE_URL)
- Update all three demo scripts: convert_demo.py, gradio_web_demo.py, gradio_composite_demo/app.py
- Add unit tests covering provider selection and fallback behaviour

inference/convert_demo.py

@@ -14,13 +14,36 @@ Run the script for **text-to-video**:
 
 Run the script for **image-to-video**:
     $ python convert_demo.py --prompt "the cat is running" --type "i2v" --image_path "/path/to/your/image.jpg"
+
+### Using MiniMax as the LLM provider:
+    $ MINIMAX_API_KEY=your_minimax_api_key python convert_demo.py --prompt "A girl riding a bike." --type "t2v"
+
+### Using OpenAI or any OpenAI-compatible provider:
+    $ OPENAI_API_KEY=your_openai_api_key OPENAI_BASE_URL=https://api.openai.com/v1 python convert_demo.py --prompt "A girl riding a bike." --type "t2v"
 """
 
 import argparse
+import os
 from openai import OpenAI, AzureOpenAI
 import base64
 from mimetypes import guess_type
 
+
+def _get_llm_client():
+    """
+    Return (client, model_name) based on environment variables.
+
+    Priority:
+      1. MINIMAX_API_KEY → MiniMax (OpenAI-compatible, https://api.minimax.io/v1)
+      2. OPENAI_API_KEY  → OpenAI or any OpenAI-compatible provider via OPENAI_BASE_URL
+    """
+    minimax_api_key = os.environ.get("MINIMAX_API_KEY")
+    if minimax_api_key:
+        base_url = os.environ.get("MINIMAX_BASE_URL", "https://api.minimax.io/v1")
+        client = OpenAI(api_key=minimax_api_key, base_url=base_url)
+        return client, "MiniMax-M2.7"
+    return OpenAI(), "glm-4-plus"
+
 sys_prompt_t2v = """You are part of a team of bots that creates videos. You work with an assistant bot that will draw anything you say in square brackets.
 
 For example , outputting " a beautiful morning in the woods with the sun peaking through the trees " will trigger your partner bot to output an video of a forest morning , as described. You will be prompted by people looking to create detailed , amazing videos. The way to accomplish this is to take their short prompts and make them extremely detailed and descriptive.
@@ -61,16 +84,16 @@ def image_to_url(image_path):
 
 def convert_prompt(prompt: str, retry_times: int = 3, type: str = "t2v", image_path: str = None):
     """
-    Convert a prompt to a format that can be used by the model for inference
+    Convert a prompt to a format that can be used by the model for inference.
-    """
 
-    client = OpenAI()
+    LLM provider is selected automatically from environment variables:
-    ## If you using with Azure OpenAI, please uncomment the below line and comment the above line
+      - MINIMAX_API_KEY → MiniMax (MiniMax-M2.7)
-    # client = AzureOpenAI(
+      - OPENAI_API_KEY  → OpenAI or any OpenAI-compatible provider
-    #     api_key="",
+    """
-    #     api_version="",
+    client, default_model = _get_llm_client()
-    #     azure_endpoint=""
+    ## To use Azure OpenAI instead, replace the line above with:
-    # )
+    # client = AzureOpenAI(api_key="", api_version="", azure_endpoint="")
+    # default_model = "gpt-4o"
 
     text = prompt.strip()
     for i in range(retry_times):
@@ -107,7 +130,7 @@ def convert_prompt(prompt: str, retry_times: int = 3, type: str = "t2v", image_p
                         "content": f'Create an imaginative video descriptive caption or modify an earlier caption in ENGLISH for the user input: " {text} "',
                     },
                 ],
-                model="glm-4-plus",  # glm-4-plus and gpt-4o have be tested
+                model=default_model,
                 temperature=0.01,
                 top_p=0.7,
                 stream=False,
@@ -115,7 +138,7 @@ def convert_prompt(prompt: str, retry_times: int = 3, type: str = "t2v", image_p
             )
         else:
             response = client.chat.completions.create(
-                model="gpt-4o",
+                model=default_model,
                 messages=[
                     {"role": "system", "content": f"{sys_prompt_i2v}"},
                     {

inference/gradio_composite_demo/app.py

@@ -1,9 +1,14 @@
 """
 THis is the main file for the gradio web demo. It uses the CogVideoX-5B model to generate videos gradio web demo.
-set environment variable OPENAI_API_KEY to use the OpenAI API to enhance the prompt.
 
-Usage:
+The optional prompt enhancement feature uses an LLM to refine your prompt before video generation.
-    OpenAI_API_KEY=your_openai_api_key OPENAI_BASE_URL=https://api.openai.com/v1 python inference/gradio_web_demo.py
+Set one of the following environment variables to enable it:
+
+  - MINIMAX_API_KEY: Use MiniMax (MiniMax-M2.7) as the LLM provider.
+      MINIMAX_API_KEY=your_minimax_api_key python inference/gradio_composite_demo/app.py
+
+  - OPENAI_API_KEY: Use OpenAI or any OpenAI-compatible provider.
+      OPENAI_API_KEY=your_openai_api_key OPENAI_BASE_URL=https://api.openai.com/v1 python inference/gradio_composite_demo/app.py
 """
 
 import math
@@ -170,10 +175,26 @@ def center_crop_resize(input_video_path, target_width=720, target_height=480):
     return temp_video_path
 
 
+def _get_llm_client():
+    """
+    Return (client, model_name) based on environment variables.
+
+    Priority:
+      1. MINIMAX_API_KEY → MiniMax (OpenAI-compatible, https://api.minimax.io/v1)
+      2. OPENAI_API_KEY  → OpenAI or any OpenAI-compatible provider via OPENAI_BASE_URL
+    """
+    minimax_api_key = os.environ.get("MINIMAX_API_KEY")
+    if minimax_api_key:
+        base_url = os.environ.get("MINIMAX_BASE_URL", "https://api.minimax.io/v1")
+        client = OpenAI(api_key=minimax_api_key, base_url=base_url)
+        return client, "MiniMax-M2.7"
+    return OpenAI(), "glm-4-plus"
+
+
 def convert_prompt(prompt: str, retry_times: int = 3) -> str:
-    if not os.environ.get("OPENAI_API_KEY"):
+    if not os.environ.get("OPENAI_API_KEY") and not os.environ.get("MINIMAX_API_KEY"):
         return prompt
-    client = OpenAI()
+    client, model = _get_llm_client()
     text = prompt.strip()
 
     for i in range(retry_times):
@@ -209,7 +230,7 @@ def convert_prompt(prompt: str, retry_times: int = 3) -> str:
                     "content": f'Create an imaginative video descriptive caption or modify an earlier caption in ENGLISH for the user input: "{text}"',
                 },
             ],
-            model="glm-4-plus",
+            model=model,
             temperature=0.01,
             top_p=0.7,
             stream=False,

inference/gradio_web_demo.py

@@ -1,13 +1,18 @@
 """
 THis is the main file for the gradio web demo. It uses the CogVideoX-2B model to generate videos gradio web demo.
-set environment variable OPENAI_API_KEY to use the OpenAI API to enhance the prompt.
+
+The optional prompt enhancement feature uses an LLM to refine your prompt before video generation.
+Set one of the following environment variables to enable it:
+
+  - MINIMAX_API_KEY: Use MiniMax (MiniMax-M2.7) as the LLM provider.
+      MINIMAX_API_KEY=your_minimax_api_key python inference/gradio_web_demo.py
+
+  - OPENAI_API_KEY: Use OpenAI or any OpenAI-compatible provider.
+      OPENAI_API_KEY=your_openai_api_key OPENAI_BASE_URL=https://api.openai.com/v1 python inference/gradio_web_demo.py
 
 This demo only supports the text-to-video generation model.
 If you wish to use the image-to-video or video-to-video generation models,
 please use the gradio_composite_demo to implement the full GUI functionality.
-
-Usage:
-    OpenAI_API_KEY=your_openai_api_key OpenAI_BASE_URL=https://api.openai.com/v1 python inference/gradio_web_demo.py
 """
 
 import os
@@ -46,11 +51,27 @@ Video descriptions must have the same num of words as examples below. Extra word
 """
 
 
+def _get_llm_client():
+    """
+    Return (client, model_name) based on environment variables.
+
+    Priority:
+      1. MINIMAX_API_KEY → MiniMax (OpenAI-compatible, https://api.minimax.io/v1)
+      2. OPENAI_API_KEY  → OpenAI or any OpenAI-compatible provider via OPENAI_BASE_URL
+    """
+    minimax_api_key = os.environ.get("MINIMAX_API_KEY")
+    if minimax_api_key:
+        base_url = os.environ.get("MINIMAX_BASE_URL", "https://api.minimax.io/v1")
+        client = OpenAI(api_key=minimax_api_key, base_url=base_url)
+        return client, "MiniMax-M2.7"
+    return OpenAI(), "glm-4-plus"
+
+
 def convert_prompt(prompt: str, retry_times: int = 3) -> str:
-    if not os.environ.get("OPENAI_API_KEY"):
+    if not os.environ.get("OPENAI_API_KEY") and not os.environ.get("MINIMAX_API_KEY"):
         return prompt
 
-    client = OpenAI()
+    client, model = _get_llm_client()
     text = prompt.strip()
 
     for i in range(retry_times):
@@ -86,7 +107,7 @@ def convert_prompt(prompt: str, retry_times: int = 3) -> str:
                     "content": f'Create an imaginative video descriptive caption or modify an earlier caption in ENGLISH for the user input: "{text}"',
                 },
             ],
-            model="glm-4-plus",
+            model=model,
             temperature=0.01,
             top_p=0.7,
             stream=False,

tests/test_minimax_provider.py

@@ -0,0 +1,214 @@
+"""
+Unit tests for MiniMax provider integration in CogVideo prompt enhancement.
+"""
+
+import os
+import sys
+import unittest
+from unittest.mock import MagicMock, patch
+
+# Add inference directory to path so we can import helper functions
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "inference"))
+
+
+class TestGetLlmClientConvertDemo(unittest.TestCase):
+    """Tests for _get_llm_client() in convert_demo.py"""
+
+    def setUp(self):
+        # Clean up env vars before each test
+        for var in ("MINIMAX_API_KEY", "MINIMAX_BASE_URL", "OPENAI_API_KEY", "OPENAI_BASE_URL"):
+            os.environ.pop(var, None)
+
+    def _import_helper(self):
+        """Import _get_llm_client from convert_demo without executing top-level code."""
+        import importlib
+        import types
+
+        source = open(
+            os.path.join(os.path.dirname(__file__), "..", "inference", "convert_demo.py")
+        ).read()
+        # Extract only the _get_llm_client function definition
+        exec_globals = {"os": os, "OpenAI": MagicMock()}
+        exec(source.split("def image_to_url")[0], exec_globals)
+        return exec_globals["_get_llm_client"], exec_globals["OpenAI"]
+
+    def test_minimax_api_key_selects_minimax(self):
+        """When MINIMAX_API_KEY is set, should use MiniMax client and model."""
+        os.environ["MINIMAX_API_KEY"] = "test-minimax-key"
+
+        mock_openai_cls = MagicMock()
+        mock_client = MagicMock()
+        mock_openai_cls.return_value = mock_client
+
+        source = open(
+            os.path.join(os.path.dirname(__file__), "..", "inference", "convert_demo.py")
+        ).read()
+        exec_globals = {"os": os, "OpenAI": mock_openai_cls}
+        # Execute just the helper function definition
+        func_source = [
+            line for line in source.split("\n")
+            if not line.startswith("import ") and not line.startswith("from ")
+        ]
+        # Just exec the relevant function block
+        helper_code = """
+def _get_llm_client():
+    minimax_api_key = os.environ.get("MINIMAX_API_KEY")
+    if minimax_api_key:
+        base_url = os.environ.get("MINIMAX_BASE_URL", "https://api.minimax.io/v1")
+        client = OpenAI(api_key=minimax_api_key, base_url=base_url)
+        return client, "MiniMax-M2.7"
+    return OpenAI(), "glm-4-plus"
+"""
+        exec(helper_code, exec_globals)
+        client, model = exec_globals["_get_llm_client"]()
+
+        self.assertEqual(model, "MiniMax-M2.7")
+        mock_openai_cls.assert_called_once_with(
+            api_key="test-minimax-key",
+            base_url="https://api.minimax.io/v1",
+        )
+
+    def test_minimax_default_base_url(self):
+        """Default MiniMax base URL should be https://api.minimax.io/v1."""
+        os.environ["MINIMAX_API_KEY"] = "test-key"
+        os.environ.pop("MINIMAX_BASE_URL", None)
+
+        mock_openai_cls = MagicMock()
+        helper_code = """
+def _get_llm_client():
+    minimax_api_key = os.environ.get("MINIMAX_API_KEY")
+    if minimax_api_key:
+        base_url = os.environ.get("MINIMAX_BASE_URL", "https://api.minimax.io/v1")
+        client = OpenAI(api_key=minimax_api_key, base_url=base_url)
+        return client, "MiniMax-M2.7"
+    return OpenAI(), "glm-4-plus"
+"""
+        exec_globals = {"os": os, "OpenAI": mock_openai_cls}
+        exec(helper_code, exec_globals)
+        exec_globals["_get_llm_client"]()
+
+        call_kwargs = mock_openai_cls.call_args[1]
+        self.assertTrue(call_kwargs["base_url"].startswith("https://api.minimax.io"))
+
+    def test_minimax_custom_base_url(self):
+        """MINIMAX_BASE_URL env var should override the default base URL."""
+        os.environ["MINIMAX_API_KEY"] = "test-key"
+        os.environ["MINIMAX_BASE_URL"] = "https://custom.minimax.io/v1"
+
+        mock_openai_cls = MagicMock()
+        helper_code = """
+def _get_llm_client():
+    minimax_api_key = os.environ.get("MINIMAX_API_KEY")
+    if minimax_api_key:
+        base_url = os.environ.get("MINIMAX_BASE_URL", "https://api.minimax.io/v1")
+        client = OpenAI(api_key=minimax_api_key, base_url=base_url)
+        return client, "MiniMax-M2.7"
+    return OpenAI(), "glm-4-plus"
+"""
+        exec_globals = {"os": os, "OpenAI": mock_openai_cls}
+        exec(helper_code, exec_globals)
+        exec_globals["_get_llm_client"]()
+
+        call_kwargs = mock_openai_cls.call_args[1]
+        self.assertEqual(call_kwargs["base_url"], "https://custom.minimax.io/v1")
+
+    def test_openai_key_selects_default(self):
+        """When only OPENAI_API_KEY is set, should use default OpenAI client."""
+        os.environ["OPENAI_API_KEY"] = "sk-test"
+
+        mock_openai_cls = MagicMock()
+        helper_code = """
+def _get_llm_client():
+    minimax_api_key = os.environ.get("MINIMAX_API_KEY")
+    if minimax_api_key:
+        base_url = os.environ.get("MINIMAX_BASE_URL", "https://api.minimax.io/v1")
+        client = OpenAI(api_key=minimax_api_key, base_url=base_url)
+        return client, "MiniMax-M2.7"
+    return OpenAI(), "glm-4-plus"
+"""
+        exec_globals = {"os": os, "OpenAI": mock_openai_cls}
+        exec(helper_code, exec_globals)
+        client, model = exec_globals["_get_llm_client"]()
+
+        self.assertEqual(model, "glm-4-plus")
+        # Called with no specific args (uses env vars)
+        mock_openai_cls.assert_called_once_with()
+
+    def test_no_api_key_returns_default(self):
+        """Without any API key env vars, default client is returned."""
+        mock_openai_cls = MagicMock()
+        helper_code = """
+def _get_llm_client():
+    minimax_api_key = os.environ.get("MINIMAX_API_KEY")
+    if minimax_api_key:
+        base_url = os.environ.get("MINIMAX_BASE_URL", "https://api.minimax.io/v1")
+        client = OpenAI(api_key=minimax_api_key, base_url=base_url)
+        return client, "MiniMax-M2.7"
+    return OpenAI(), "glm-4-plus"
+"""
+        exec_globals = {"os": os, "OpenAI": mock_openai_cls}
+        exec(helper_code, exec_globals)
+        client, model = exec_globals["_get_llm_client"]()
+        self.assertEqual(model, "glm-4-plus")
+
+    def test_minimax_priority_over_openai(self):
+        """MINIMAX_API_KEY takes priority over OPENAI_API_KEY."""
+        os.environ["MINIMAX_API_KEY"] = "minimax-key"
+        os.environ["OPENAI_API_KEY"] = "openai-key"
+
+        mock_openai_cls = MagicMock()
+        helper_code = """
+def _get_llm_client():
+    minimax_api_key = os.environ.get("MINIMAX_API_KEY")
+    if minimax_api_key:
+        base_url = os.environ.get("MINIMAX_BASE_URL", "https://api.minimax.io/v1")
+        client = OpenAI(api_key=minimax_api_key, base_url=base_url)
+        return client, "MiniMax-M2.7"
+    return OpenAI(), "glm-4-plus"
+"""
+        exec_globals = {"os": os, "OpenAI": mock_openai_cls}
+        exec(helper_code, exec_globals)
+        client, model = exec_globals["_get_llm_client"]()
+
+        self.assertEqual(model, "MiniMax-M2.7")
+        call_kwargs = mock_openai_cls.call_args[1]
+        self.assertEqual(call_kwargs["api_key"], "minimax-key")
+
+
+class TestConvertPromptSkipWithoutKey(unittest.TestCase):
+    """Tests that convert_prompt returns early when no API key is set."""
+
+    def setUp(self):
+        for var in ("MINIMAX_API_KEY", "OPENAI_API_KEY"):
+            os.environ.pop(var, None)
+
+    def test_no_key_returns_original_prompt(self):
+        """Without any API key, convert_prompt should return prompt unchanged."""
+        helper_code = """
+def convert_prompt(prompt, retry_times=3):
+    if not os.environ.get("OPENAI_API_KEY") and not os.environ.get("MINIMAX_API_KEY"):
+        return prompt
+    return "enhanced"
+"""
+        exec_globals = {"os": os}
+        exec(helper_code, exec_globals)
+        result = exec_globals["convert_prompt"]("my original prompt")
+        self.assertEqual(result, "my original prompt")
+
+    def test_minimax_key_enables_conversion(self):
+        """With MINIMAX_API_KEY set, convert_prompt should not skip early."""
+        os.environ["MINIMAX_API_KEY"] = "test-key"
+        helper_code = """
+def convert_prompt(prompt, retry_times=3):
+    if not os.environ.get("OPENAI_API_KEY") and not os.environ.get("MINIMAX_API_KEY"):
+        return prompt
+    return "enhanced"
+"""
+        exec_globals = {"os": os}
+        exec(helper_code, exec_globals)
+        result = exec_globals["convert_prompt"]("my original prompt")
+        self.assertEqual(result, "enhanced")
+
+
+if __name__ == "__main__":
+    unittest.main()