Gradio plugin integration to covalent

README.md

@@ -70,6 +70,7 @@ const zee = new ZeeWorkflow({
 })();
 ```
 
+### Refer Configs for other SDK's Integrations like Hugging face, Gemini, Gradio etc. on [llms.mdx Readme File](https://github.com/covalenthq/ai-agent-sdk/blob/main/docs/concepts/llms.mdx)
 ## 🤝 Contributing
 
 Contributions, issues and feature requests are welcome!

docs/concepts/llms.mdx

@@ -45,6 +45,10 @@ const llm = new LLM({
 "gemini-1.5-pro"
 ```
 
+```plaintext Gradio
+"Any Supported and hosted inference and generation algorithms using gradio python library"
+```
+
 </CodeGroup>
 
 ## Environment Variables
@@ -69,6 +73,73 @@ GEMINI_API_KEY
 
 </CodeGroup>
 
+## Example Usage:
+
+For deepseek-ai/deepseek-vl2-small:
+
+```typescript
+const gradioConfig: GradioConfig = {
+    provider: "GRADIO",
+    name: "deepseek-vl2-small",
+    appUrl: "deepseek-ai/deepseek-vl2-small",
+    parameters: [
+        [["Hello!", null]],
+        0.9, // topP
+        0.1, // temperature
+        0, // repetitionPenalty
+        100, // maxGenerationTokens
+        0, // maxHistoryTokens
+        "deepseek-ai/deepseek-vl2-small", // model name
+    ],
+};
+
+const llm = new LLM(gradioConfig);
+const response = await llm.generate(
+    [{ role: "user", content: "Hello!" }],
+    { text: z.string() },
+    {}
+);
+console.log(response.value);
+```
+For r3gm/RVC_HF:
+```typescript
+const gradioConfig: GradioConfig = {
+    provider: "GRADIO",
+    name: "RVC_HF",
+    appUrl: "r3gm/RVC_HF",
+    endpoint: "/run_infer_script",
+    parameters: {
+        f0up_key: 0,
+        filter_radius: 5,
+        index_rate: 0,
+        rms_mix_rate: 0,
+        protect: 0,
+        hop_length: 1,
+        f0method: "rmvpe",
+        input_path: "https://raw.githubusercontent.com/Philotheephilix/Tunify/main/public/amidreaming.wav",
+        output_path: "/home/user/app/assets/audios/output.wav",
+        pth_path: "logs/Justin_Bieber_v2/Justin_Bieber_v2.pth",
+        index_path: "logs/Justin_Bieber_v2/added_IVF1005_Flat_nprobe_1_Justin_Bieber_v2_v2.index",
+        clean_strength: 0,
+        export_format: "WAV",
+        embedder_model: "hubert",
+        embedder_model_custom: "",
+        upscale_audio: true,
+    },
+};
+```
+Gradio is used for custom made inference and algorithms so the working depends solely on the developer of the Gradio Project and the api configured for it.
+
+### Initialization example with Gradio
+
+const llm = new LLM(gradioConfig);
+const response = await llm.generate(
+    [], // Messages not used for this model
+    { result: z.string() },
+    {}
+);
+console.log(response.value);
+
 ## Use Cases
 
 ### Image Analysis

packages/ai-agent-sdk/package.json

@@ -49,6 +49,7 @@
     },
     "dependencies": {
         "@covalenthq/client-sdk": "^2.2.3",
+        "@gradio/client": "^1.11.0",
         "commander": "^13.1.0",
         "dotenv": "^16.4.7",
         "openai": "^4.79.1",

packages/ai-agent-sdk/src/core/llm/llm.ts

@@ -11,6 +11,7 @@ import type {
 import type { AnyZodObject } from "zod";
 import { z } from "zod";
 import { zodToJsonSchema } from "zod-to-json-schema";
+import { Client as GradioClient } from "@gradio/client";
 
 const entryToObject = ([key, value]: [string, AnyZodObject]) => {
     return z.object({ type: z.literal(key), value });
@@ -60,10 +61,11 @@ export class LLM extends Base {
         response_schema: T,
         tools: Record<string, Tool>
     ): Promise<FunctionToolCall | LLMResponse<T>> {
+        const provider = this.model.provider;
+
         const config: ConstructorParameters<typeof OpenAI>[0] = {
             apiKey: this.model.apiKey,
         };
-        const provider = this.model.provider;
         switch (provider) {
             case "OPEN_AI":
                 break;
@@ -83,6 +85,8 @@ export class LLM extends Base {
                 config.apiKey =
                     process.env["GEMINI_API_KEY"] || this.model.apiKey;
                 break;
+            case "GRADIO":
+                break;
             default:
                 var _exhaustiveCheck: never = provider;
                 throw new Error(
@@ -157,6 +161,15 @@ export class LLM extends Base {
             } as LLMResponse<T>;
         }
 
+        if (provider === "GRADIO") {
+            const config = this.model as any;
+            const client = await new GradioClient(config.appUrl);
+            const result = await client.predict(config.endpoint || "/predict", config.parameters || []);
+            const [schemaKey, schemaValue] = Object.entries(response_schema)[0] || [];
+            if (!schemaKey || !schemaValue) throw new Error("Invalid schema");
+            return { type: schemaKey, value: schemaValue.parse(result.data) } as LLMResponse<T>;
+        }
+
         if (parsed?.response) {
             return parsed.response as LLMResponse<T>;
         }

packages/ai-agent-sdk/src/core/llm/llm.types.ts

@@ -47,11 +47,22 @@ export type GeminiConfig = {
     apiKey?: string;
 };
 
+export type GradioConfig = {
+    provider: "GRADIO";
+    name: string;
+    appUrl: string;
+    endpoint?: string;
+    parameters?: unknown[];
+    temperature?: number; 
+    apiKey?: string;
+};
+
 export type ModelConfig =
     | OpenAIConfig
     | DeepSeekConfig
     | GrokConfig
-    | GeminiConfig;
+    | GeminiConfig
+    | GradioConfig;
 
 export type LLMResponse<T extends Record<string, AnyZodObject>> = {
     [K in keyof T]: {
@@ -64,3 +75,17 @@ export type FunctionToolCall = {
     type: "tool_call";
     value: ParsedFunctionToolCall[];
 };
+
+export type ChatCompletionMessageParam = {
+    role: "user" | "assistant" | "system";
+    content: string;
+};
+
+export type ChatCompletionTool = {
+    type: "function";
+    function: {
+        name: string;
+        description?: string;
+        parameters: Record<string, unknown>;
+    };
+};