refactor(backend/blocks): Add retry function to replicate models & fix AI Music Generator block

autogpt_platform/backend/backend/blocks/ai_image_customizer.py

@@ -6,6 +6,7 @@
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
 
+from backend.blocks.replicate._helper import run_replicate_with_retry
 from backend.data.block import (
     Block,
     BlockCategory,
@@ -183,9 +184,10 @@ async def run_model(
         if images:
             input_params["image_input"] = [str(img) for img in images]
 
-        output: FileOutput | str = await client.async_run(  # type: ignore
+        output: FileOutput | str = await run_replicate_with_retry(  # type: ignore
+            client,
             model_name,
-            input=input_params,
+            input_params,
             wait=False,
         )
 

autogpt_platform/backend/backend/blocks/ai_image_generator_block.py

@@ -5,6 +5,7 @@
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
 
+from backend.blocks.replicate._helper import run_replicate_with_retry
 from backend.data.block import Block, BlockCategory, BlockSchemaInput, BlockSchemaOutput
 from backend.data.model import (
     APIKeyCredentials,
@@ -181,7 +182,9 @@ async def _run_client(
             client = ReplicateClient(api_token=credentials.api_key.get_secret_value())
 
             # Run the model with input parameters
-            output = await client.async_run(model_name, input=input_params, wait=False)
+            output = await run_replicate_with_retry(
+                client, model_name, input_params, wait=False
+            )
 
             # Process output
             if isinstance(output, list) and len(output) > 0:

autogpt_platform/backend/backend/blocks/ai_music_generator.py

@@ -6,6 +6,9 @@
 from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 
+from replicate.helpers import FileOutput
+
+from backend.blocks.replicate._helper import run_replicate_with_retry
 from backend.data.block import (
     Block,
     BlockCategory,
@@ -43,12 +46,14 @@ class MusicGenModelVersion(str, Enum):
     STEREO_LARGE = "stereo-large"
     MELODY_LARGE = "melody-large"
     LARGE = "large"
+    MINIMAX_MUSIC_1_5 = "minimax/music-1.5"
 
 
 # Audio format enum
 class AudioFormat(str, Enum):
     WAV = "wav"
     MP3 = "mp3"
+    PCM = "pcm"
 
 
 # Normalization strategy enum
@@ -72,6 +77,14 @@ class Input(BlockSchemaInput):
             placeholder="e.g., 'An upbeat electronic dance track with heavy bass'",
             title="Prompt",
         )
+        lyrics: str | None = SchemaField(
+            description=(
+                "Lyrics for the song (required for Minimax Music 1.5). "
+                "Use \\n to separate lines. Supports tags like [intro], [verse], [chorus], etc."
+            ),
+            default=None,
+            title="Lyrics",
+        )
         music_gen_model_version: MusicGenModelVersion = SchemaField(
             description="Model to use for generation",
             default=MusicGenModelVersion.STEREO_LARGE,
@@ -126,6 +139,7 @@ def __init__(self):
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "prompt": "An upbeat electronic dance track with heavy bass",
+                "lyrics": None,
                 "music_gen_model_version": MusicGenModelVersion.STEREO_LARGE,
                 "duration": 8,
                 "temperature": 1.0,
@@ -142,56 +156,43 @@ def __init__(self):
                 ),
             ],
             test_mock={
-                "run_model": lambda api_key, music_gen_model_version, prompt, duration, temperature, top_k, top_p, classifier_free_guidance, output_format, normalization_strategy: "https://replicate.com/output/generated-audio-url.wav",
+                "run_model": lambda api_key, music_gen_model_version, prompt, lyrics, duration, temperature, top_k, top_p, classifier_free_guidance, output_format, normalization_strategy: "https://replicate.com/output/generated-audio-url.wav",
             },
             test_credentials=TEST_CREDENTIALS,
         )
 
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
-        max_retries = 3
-        retry_delay = 5  # seconds
-        last_error = None
-
-        for attempt in range(max_retries):
-            try:
-                logger.debug(
-                    f"[AIMusicGeneratorBlock] - Running model (attempt {attempt + 1})"
-                )
-                result = await self.run_model(
-                    api_key=credentials.api_key,
-                    music_gen_model_version=input_data.music_gen_model_version,
-                    prompt=input_data.prompt,
-                    duration=input_data.duration,
-                    temperature=input_data.temperature,
-                    top_k=input_data.top_k,
-                    top_p=input_data.top_p,
-                    classifier_free_guidance=input_data.classifier_free_guidance,
-                    output_format=input_data.output_format,
-                    normalization_strategy=input_data.normalization_strategy,
-                )
-                if result and isinstance(result, str) and result.startswith("http"):
-                    yield "result", result
-                    return
-                else:
-                    last_error = "Model returned empty or invalid response"
-                    raise ValueError(last_error)
-            except Exception as e:
-                last_error = f"Unexpected error: {str(e)}"
-                logger.error(f"[AIMusicGeneratorBlock] - Error: {last_error}")
-                if attempt < max_retries - 1:
-                    await asyncio.sleep(retry_delay)
-                    continue
-
-        # If we've exhausted all retries, yield the error
-        yield "error", f"Failed after {max_retries} attempts. Last error: {last_error}"
+        try:
+            result = await self.run_model(
+                api_key=credentials.api_key,
+                music_gen_model_version=input_data.music_gen_model_version,
+                prompt=input_data.prompt,
+                lyrics=input_data.lyrics,
+                duration=input_data.duration,
+                temperature=input_data.temperature,
+                top_k=input_data.top_k,
+                top_p=input_data.top_p,
+                classifier_free_guidance=input_data.classifier_free_guidance,
+                output_format=input_data.output_format,
+                normalization_strategy=input_data.normalization_strategy,
+            )
+            if result and isinstance(result, str) and result.startswith("http"):
+                yield "result", result
+            else:
+                yield "error", "Model returned empty or invalid response"
+
+        except Exception as e:
+            logger.error(f"[AIMusicGeneratorBlock] - Error: {str(e)}")
+            yield "error", f"Failed to generate music: {str(e)}"
 
     async def run_model(
         self,
         api_key: SecretStr,
         music_gen_model_version: MusicGenModelVersion,
         prompt: str,
+        lyrics: str | None,
         duration: int,
         temperature: float,
         top_k: int,
@@ -203,10 +204,24 @@ async def run_model(
         # Initialize Replicate client with the API key
         client = ReplicateClient(api_token=api_key.get_secret_value())
 
-        # Run the model with parameters
-        output = await client.async_run(
-            "meta/musicgen:671ac645ce5e552cc63a54a2bbff63fcf798043055d2dac5fc9e36a837eedcfb",
-            input={
+        if music_gen_model_version == MusicGenModelVersion.MINIMAX_MUSIC_1_5:
+            if not lyrics:
+                raise ValueError("Lyrics are required for Minimax Music 1.5 model")
+
+            # Validate prompt length (10-300 chars)
+            if len(prompt) < 10:
+                prompt = prompt.ljust(10, ".")
+            elif len(prompt) > 300:
+                prompt = prompt[:300]
+
+            input_params = {
+                "prompt": prompt,
+                "lyrics": lyrics,
+                "audio_format": output_format.value,
+            }
+            model_name = "minimax/music-1.5"
+        else:
+            input_params = {
                 "prompt": prompt,
                 "music_gen_model_version": music_gen_model_version,
                 "duration": duration,
@@ -216,14 +231,24 @@ async def run_model(
                 "classifier_free_guidance": classifier_free_guidance,
                 "output_format": output_format,
                 "normalization_strategy": normalization_strategy,
-            },
+            }
+            model_name = "meta/musicgen:671ac645ce5e552cc63a54a2bbff63fcf798043055d2dac5fc9e36a837eedcfb"
+
+        # Run the model with parameters
+        output = await run_replicate_with_retry(
+            client,
+            model_name,
+            input_params,
+            wait=True,
         )
 
         # Handle the output
         if isinstance(output, list) and len(output) > 0:
             result_url = output[0]  # If output is a list, get the first element
         elif isinstance(output, str):
             result_url = output  # If output is a string, use it directly
+        elif isinstance(output, FileOutput):
+            result_url = output.url
         else:
             result_url = (
                 "No output received"  # Fallback message if output is not as expected

autogpt_platform/backend/backend/blocks/flux_kontext.py

@@ -5,6 +5,7 @@
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
 
+from backend.blocks.replicate._helper import run_replicate_with_retry
 from backend.data.block import (
     Block,
     BlockCategory,
@@ -173,9 +174,10 @@ async def run_model(
             **({"seed": seed} if seed is not None else {}),
         }
 
-        output: FileOutput | list[FileOutput] = await client.async_run(  # type: ignore
+        output: FileOutput | list[FileOutput] = await run_replicate_with_retry(  # type: ignore
+            client,
             model_name,
-            input=input_params,
+            input_params=input_params,
             wait=False,
         )
 

autogpt_platform/backend/backend/blocks/replicate/_helper.py

@@ -1,5 +1,8 @@
+import asyncio
 import logging
+from typing import Any
 
+from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
 
 logger = logging.getLogger(__name__)
@@ -37,3 +40,56 @@ def extract_result(output: ReplicateOutputs) -> str:
         )
 
     return result
+
+
+async def run_replicate_with_retry(
+    client: ReplicateClient,
+    model: str,
+    input_params: dict[str, Any],
+    wait: bool = False,
+    max_retries: int = 3,
+    **kwargs: Any,
+) -> Any:
+    last_error = None
+    retry_delay = 2  # seconds
+
+    for attempt in range(max_retries):
+        try:
+            output = await client.async_run(
+                model, input=input_params, wait=wait, **kwargs
+            )
+
+            # Check for failed status in response
+            is_failed = False
+            if isinstance(output, dict) and output.get("status") == "failed":
+                is_failed = True
+            elif hasattr(output, "status") and getattr(output, "status") == "failed":
+                is_failed = True
+
+            if is_failed:
+                # Try to get error message
+                error_msg = "Replicate prediction failed"
+                if isinstance(output, dict):
+                    error = output.get("error")
+                    if error:
+                        error_msg = f"{error_msg}: {error}"
+                elif hasattr(output, "error"):
+                    error = getattr(output, "error")
+                    if error:
+                        error_msg = f"{error_msg}: {error}"
+
+                raise RuntimeError(error_msg)
+
+            return output
+
+        except Exception as e:
+            last_error = e
+            if attempt < max_retries - 1:
+                wait_time = retry_delay * (2 ** attempt)
+                logger.warning(
+                    f"Replicate attempt {attempt + 1} failed: {str(e)}. Retrying in {wait_time}s..."
+                )
+                await asyncio.sleep(wait_time)
+            else:
+                logger.error(f"Replicate failed after {max_retries} attempts: {str(e)}")
+                raise last_error

autogpt_platform/backend/backend/blocks/replicate/flux_advanced.py

@@ -9,7 +9,11 @@
     TEST_CREDENTIALS_INPUT,
     ReplicateCredentialsInput,
 )
-from backend.blocks.replicate._helper import ReplicateOutputs, extract_result
+from backend.blocks.replicate._helper import (
+    ReplicateOutputs,
+    extract_result,
+    run_replicate_with_retry,
+)
 from backend.data.block import (
     Block,
     BlockCategory,
@@ -188,9 +192,10 @@ async def run_model(
         client = ReplicateClient(api_token=api_key.get_secret_value())
 
         # Run the model with additional parameters
-        output: ReplicateOutputs = await client.async_run(  # type: ignore This is because they changed the return type, and didn't update the type hint! It should be overloaded depending on the value of `use_file_output` to `FileOutput | list[FileOutput]` but it's `Any | Iterator[Any]`
+        output: ReplicateOutputs = await run_replicate_with_retry(  # type: ignore This is because they changed the return type, and didn't update the type hint! It should be overloaded depending on the value of `use_file_output` to `FileOutput | list[FileOutput]` but it's `Any | Iterator[Any]`
+            client,
             f"{model_name}",
-            input={
+            input_params={
                 "prompt": prompt,
                 "seed": seed,
                 "steps": steps,

autogpt_platform/backend/backend/blocks/replicate/replicate_block.py

@@ -9,7 +9,11 @@
     TEST_CREDENTIALS_INPUT,
     ReplicateCredentialsInput,
 )
-from backend.blocks.replicate._helper import ReplicateOutputs, extract_result
+from backend.blocks.replicate._helper import (
+    ReplicateOutputs,
+    extract_result,
+    run_replicate_with_retry,
+)
 from backend.data.block import (
     Block,
     BlockCategory,
@@ -129,8 +133,8 @@ async def run_model(self, model_ref: str, model_inputs: dict, api_key: SecretStr
         """
         api_key_str = api_key.get_secret_value()
         client = ReplicateClient(api_token=api_key_str)
-        output: ReplicateOutputs = await client.async_run(
-            model_ref, input=model_inputs, wait=False
+        output: ReplicateOutputs = await run_replicate_with_retry(
+            client, model_ref, input_params=model_inputs, wait=False
         )  # type: ignore they suck at typing
 
         result = extract_result(output)