Update index.mjs

index.mjs

@@ -1,90 +1,40 @@
-// Import necessary libraries
 import express from "express";
-import 'dotenv/config.js'
+import 'dotenv/config.js';
 import { createServer } from "http";
 import { Server } from "socket.io";
 import { client } from "@gradio/client";
 import { createRequire } from 'node:module';
-import fs from 'fs';
-import path from 'path';
-
 const require = createRequire(import.meta.url);
 global.EventSource = require('eventsource');
 
-// Initialize the Express app and HTTP server
 const app = express();
 const httpServer = createServer(app);
-
-// Serve static files from 'public' directory
 app.use(express.static('public'));
+const io = new Server(httpServer, { /* options */ });
 
-// Initialize Socket.IO for real-time web socket communication
-const io = new Server(httpServer, {});
-
-// Directory for saving inference files
-const outputDir = 'inference_outputs';
-if (!fs.existsSync(outputDir)){
-    fs.mkdirSync(outputDir);
-}
-
-// Handle new socket connection
 io.on("connection", (socket) => {
-    console.log("🔌 New socket connection");
+    console.log("new socket connection 🎉");
     
-    // Listen for 'ask_api' events from the client
     socket.on("ask_api", (client_data) => {
-        console.log("📸 Received data from client");
+        console.log("Received data from client 📸", client_data);
+        console.log("Attempting to reach API... 🚀");   
         asyncAPICall(client_data, socket);
     });
 });
 
-// Example function to test server communication with a Gradio API
-async function test_servers(){
-    try{
-        const grapi_test = await client("https://gradio-hello-world.hf.space");
-        const apitest_result = await grapi_test.predict("/predict", ["John"]);
-        console.log(apitest_result);
-    }
-    catch(e){
-        console.log("❌ Error testing servers:", e);
-    }
-}
-
-// Function to call the Gradio API asynchronously
 async function asyncAPICall(data, socket) {
-    const grapi = await client("fffiloni/mndrm-call");
-    try{
-        // Perform the API call with the provided image blob and question
-        const api_result = await grapi.predict("/infer", [data[0], data[1]]);
-        console.log("✅ API Result:", api_result);
-        
-        // Emit the API response back to the client
-        socket.emit("api_response", api_result.data);
-        
-        // Save the inference input and output
-        saveInference(data, api_result.data);
-    }
-    catch(e){
-        console.log("❌ API Call Error:", e);
-        socket.emit("api_error", "ERROR ON API SIDE, SORRY...");
-    }    
-}
-
-// Save the inference input (image and question) and output (API response) to files
-function saveInference(data, apiResponse) {
-    const timestamp = new Date().toISOString().replace(/[:.]/g, '-');
-    const basePath = path.join(outputDir, timestamp);
-    
-    // Save image
-    const imageData = data[0].split(',')[1]; // Assuming data[0] is a base64 image string
-    fs.writeFileSync(`${basePath}_image.png`, imageData, 'base64');
-    
-    // Save question and API response in a text file
-    const textContent = `Question: ${data[1]}\nAPI Response: ${apiResponse}`;
-    fs.writeFileSync(`${basePath}_info.txt`, textContent);
-    
-    console.log("📁 Inference data saved:", basePath);
+  const grapi = await client("fffiloni/mndrm-call");
+  try {
+      const api_result = await grapi.predict("/infer", [
+          data[0], // blob in 'image' Image component
+          data[1], // string in 'Question' Textbox component
+      ]);
+      console.log("API response received ✅", api_result);
+      socket.emit("api_response", (api_result.data));
+  } catch (e) {
+      console.error("API call failed ❌", e);
+      socket.emit("api_error", ("ERROR ON API SIDE, SORRY..."));
+  }    
 }
 
-// Start the HTTP server
-httpServer.listen(7860, () => console.log("🚀 App running on localhost:7860"));
+httpServer.listen(7860, () => console.log("App running on localhost:7860 🌍"));