feat(block): Add AI video generator block with Fal txt 2 vid

autogpt_platform/backend/backend/blocks/fal/_auth.py

@@ -0,0 +1,36 @@
+from typing import Literal
+
+from pydantic import SecretStr
+
+from backend.data.model import APIKeyCredentials, CredentialsField, CredentialsMetaInput
+
+FalCredentials = APIKeyCredentials
+FalCredentialsInput = CredentialsMetaInput[
+    Literal["fal"],
+    Literal["api_key"],
+]
+
+TEST_CREDENTIALS = APIKeyCredentials(
+    id="01234567-89ab-cdef-0123-456789abcdef",
+    provider="fal",
+    api_key=SecretStr("mock-fal-api-key"),
+    title="Mock FAL API key",
+    expires_at=None,
+)
+TEST_CREDENTIALS_INPUT = {
+    "provider": TEST_CREDENTIALS.provider,
+    "id": TEST_CREDENTIALS.id,
+    "type": TEST_CREDENTIALS.type,
+    "title": TEST_CREDENTIALS.title,
+}
+
+
+def FalCredentialsField() -> FalCredentialsInput:
+    """
+    Creates a FAL credentials input on a block.
+    """
+    return CredentialsField(
+        provider="fal",
+        supported_credential_types={"api_key"},
+        description="The FAL integration can be used with an API Key.",
+    )

autogpt_platform/backend/backend/blocks/fal/ai_video_generator.py

@@ -0,0 +1,199 @@
+import logging
+import time
+from enum import Enum
+from typing import Any, Dict
+
+import httpx
+
+from backend.blocks.fal._auth import (
+    TEST_CREDENTIALS,
+    TEST_CREDENTIALS_INPUT,
+    FalCredentials,
+    FalCredentialsField,
+    FalCredentialsInput,
+)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import SchemaField
+
+logger = logging.getLogger(__name__)
+
+
+class FalModel(str, Enum):
+    MOCHI = "fal-ai/mochi-v1"
+    LUMA = "fal-ai/luma-dream-machine"
+
+
+class AIVideoGeneratorBlock(Block):
+    class Input(BlockSchema):
+        prompt: str = SchemaField(
+            description="Description of the video to generate.",
+            placeholder="A dog running in a field.",
+        )
+        model: FalModel = SchemaField(
+            title="FAL Model",
+            default=FalModel.MOCHI,
+            description="The FAL model to use for video generation.",
+        )
+        credentials: FalCredentialsInput = FalCredentialsField()
+
+    class Output(BlockSchema):
+        video_url: str = SchemaField(description="The URL of the generated video.")
+        error: str = SchemaField(
+            description="Error message if video generation failed."
+        )
+        logs: list[str] = SchemaField(
+            description="Generation progress logs.", optional=True
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="530cf046-2ce0-4854-ae2c-659db17c7a46",
+            description="Generate videos using FAL AI models.",
+            categories={BlockCategory.AI},
+            input_schema=self.Input,
+            output_schema=self.Output,
+            test_input={
+                "prompt": "A dog running in a field.",
+                "model": FalModel.MOCHI,
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[("video_url", "https://fal.media/files/example/video.mp4")],
+            test_mock={
+                "generate_video": lambda *args, **kwargs: "https://fal.media/files/example/video.mp4"
+            },
+        )
+
+    def _get_headers(self, api_key: str) -> Dict[str, str]:
+        """Get headers for FAL API requests."""
+        return {
+            "Authorization": f"Key {api_key}",
+            "Content-Type": "application/json",
+        }
+
+    def _submit_request(
+        self, url: str, headers: Dict[str, str], data: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Submit a request to the FAL API."""
+        try:
+            response = httpx.post(url, headers=headers, json=data)
+            response.raise_for_status()
+            return response.json()
+        except httpx.HTTPError as e:
+            logger.error(f"FAL API request failed: {str(e)}")
+            raise RuntimeError(f"Failed to submit request: {str(e)}")
+
+    def _poll_status(self, status_url: str, headers: Dict[str, str]) -> Dict[str, Any]:
+        """Poll the status endpoint until completion or failure."""
+        try:
+            response = httpx.get(status_url, headers=headers)
+            response.raise_for_status()
+            return response.json()
+        except httpx.HTTPError as e:
+            logger.error(f"Failed to get status: {str(e)}")
+            raise RuntimeError(f"Failed to get status: {str(e)}")
+
+    def generate_video(self, input_data: Input, credentials: FalCredentials) -> str:
+        """Generate video using the specified FAL model."""
+        base_url = "https://queue.fal.run"
+        api_key = credentials.api_key.get_secret_value()
+        headers = self._get_headers(api_key)
+
+        # Submit generation request
+        submit_url = f"{base_url}/{input_data.model.value}"
+        submit_data = {"prompt": input_data.prompt}
+
+        seen_logs = set()
+
+        try:
+            # Submit request to queue
+            submit_response = httpx.post(submit_url, headers=headers, json=submit_data)
+            submit_response.raise_for_status()
+            request_data = submit_response.json()
+
+            # Get request_id and urls from initial response
+            request_id = request_data.get("request_id")
+            status_url = request_data.get("status_url")
+            result_url = request_data.get("response_url")
+
+            if not all([request_id, status_url, result_url]):
+                raise ValueError("Missing required data in submission response")
+
+            # Poll for status with exponential backoff
+            max_attempts = 30
+            attempt = 0
+            base_wait_time = 5
+
+            while attempt < max_attempts:
+                status_response = httpx.get(f"{status_url}?logs=1", headers=headers)
+                status_response.raise_for_status()
+                status_data = status_response.json()
+
+                # Process new logs only
+                logs = status_data.get("logs", [])
+                if logs and isinstance(logs, list):
+                    for log in logs:
+                        if isinstance(log, dict):
+                            # Create a unique key for this log entry
+                            log_key = (
+                                f"{log.get('timestamp', '')}-{log.get('message', '')}"
+                            )
+                            if log_key not in seen_logs:
+                                seen_logs.add(log_key)
+                                message = log.get("message", "")
+                                if message:
+                                    logger.debug(
+                                        f"[FAL Generation] [{log.get('level', 'INFO')}] [{log.get('source', '')}] [{log.get('timestamp', '')}] {message}"
+                                    )
+
+                status = status_data.get("status")
+                if status == "COMPLETED":
+                    # Get the final result
+                    result_response = httpx.get(result_url, headers=headers)
+                    result_response.raise_for_status()
+                    result_data = result_response.json()
+
+                    if "video" not in result_data or not isinstance(
+                        result_data["video"], dict
+                    ):
+                        raise ValueError("Invalid response format - missing video data")
+
+                    video_url = result_data["video"].get("url")
+                    if not video_url:
+                        raise ValueError("No video URL in response")
+
+                    return video_url
+
+                elif status == "FAILED":
+                    error_msg = status_data.get("error", "No error details provided")
+                    raise RuntimeError(f"Video generation failed: {error_msg}")
+                elif status == "IN_QUEUE":
+                    position = status_data.get("queue_position", "unknown")
+                    logger.debug(
+                        f"[FAL Generation] Status: In queue, position: {position}"
+                    )
+                elif status == "IN_PROGRESS":
+                    logger.debug(
+                        "[FAL Generation] Status: Request is being processed..."
+                    )
+                else:
+                    logger.info(f"[FAL Generation] Status: Unknown status: {status}")
+
+                wait_time = min(base_wait_time * (2**attempt), 60)  # Cap at 60 seconds
+                time.sleep(wait_time)
+                attempt += 1
+
+            raise RuntimeError("Maximum polling attempts reached")
+
+        except httpx.HTTPError as e:
+            raise RuntimeError(f"API request failed: {str(e)}")
+
+    def run(
+        self, input_data: Input, *, credentials: FalCredentials, **kwargs
+    ) -> BlockOutput:
+        try:
+            video_url = self.generate_video(input_data, credentials)
+            yield "video_url", video_url
+        except Exception as e:
+            error_message = str(e)
+            yield "error", error_message

autogpt_platform/backend/backend/util/settings.py

@@ -290,6 +290,8 @@ class Secrets(UpdateTrackingModel["Secrets"], BaseSettings):
     jina_api_key: str = Field(default="", description="Jina API Key")
     unreal_speech_api_key: str = Field(default="", description="Unreal Speech API Key")
 
+    fal_key: str = Field(default="", description="FAL API key")
+
     # Add more secret fields as needed
 
     model_config = SettingsConfigDict(

autogpt_platform/frontend/src/components/integrations/credentials-input.tsx

@@ -64,6 +64,7 @@ export const providerIcons: Record<
   open_router: fallbackIcon,
   pinecone: fallbackIcon,
   replicate: fallbackIcon,
+  fal: fallbackIcon,
   revid: fallbackIcon,
   unreal_speech: fallbackIcon,
   hubspot: fallbackIcon,

autogpt_platform/frontend/src/components/integrations/credentials-provider.tsx

@@ -38,6 +38,7 @@ const providerDisplayNames: Record<CredentialsProviderName, string> = {
   open_router: "Open Router",
   pinecone: "Pinecone",
   replicate: "Replicate",
+  fal: "FAL",
   revid: "Rev.ID",
   unreal_speech: "Unreal Speech",
   hubspot: "Hubspot",

autogpt_platform/frontend/src/lib/autogpt-server-api/types.ts

@@ -116,6 +116,7 @@ export const PROVIDER_NAMES = {
   OPEN_ROUTER: "open_router",
   PINECONE: "pinecone",
   REPLICATE: "replicate",
+  FAL: "fal",
   REVID: "revid",
   UNREAL_SPEECH: "unreal_speech",
   HUBSPOT: "hubspot",