Update index.html

index.html

@@ -777,33 +777,30 @@ async function run(){
   try {
     const HF_API = "https://sattfaceee123-deepscan.hf.space";
 
-    // Step 1: Upload image — Gradio 6 endpoint
+    // Convert base64 image to blob
     const blob = await fetch(currentImageData).then(r=>r.blob());
-    const fd = new FormData();
-    fd.append("files", blob, "image.jpg");
-
-    const upRes = await fetch(`${HF_API}/upload`, {method:"POST", body:fd});
-    if(!upRes.ok) throw new Error("Upload failed: "+upRes.status);
-    const upJson = await upRes.json();
-    // Gradio 6 returns array of objects with .path or array of strings
-    let filePath;
-    if(Array.isArray(upJson)){
-      filePath = (typeof upJson[0] === 'object') ? upJson[0].path : upJson[0];
-    } else {
-      filePath = upJson.path || upJson;
-    }
 
-    // Step 2: Run prediction — Gradio 6 endpoint
+    // Gradio 6: convert image to base64 and send directly in data array
+    const toBase64 = b => new Promise((res,rej)=>{
+      const reader = new FileReader();
+      reader.onload = ()=>res(reader.result);
+      reader.onerror = rej;
+      reader.readAsDataURL(b);
+    });
+    const b64 = await toBase64(blob);
+
+    // Gradio 6 direct predict — send image as base64 data URL in data[]
+    const session = Math.random().toString(36).slice(2);
     const predRes = await fetch(`${HF_API}/run/predict`, {
-      method:"POST",
-      headers:{"Content-Type":"application/json"},
+      method: "POST",
+      headers: {"Content-Type":"application/json"},
       body: JSON.stringify({
         fn_index: 0,
-        data: [{"path": filePath, "orig_name":"image.jpg", "mime_type":"image/jpeg", "size": blob.size}],
-        event_data: null,
-        session_hash: Math.random().toString(36).slice(2)
+        data: [b64],
+        session_hash: session
       })
     });
+
     if(!predRes.ok) throw new Error("Predict failed: "+predRes.status);
     const predJson = await predRes.json();
 
@@ -812,28 +809,29 @@ async function run(){
     document.getElementById("proc-bar").style.width="100%";
     st.textContent="Analysis complete ✦";
 
-    // predJson.data[0] = label dict {"AI Generated": 0.87, "Real": 0.13}
-    // predJson.data[1] = markdown verdict string
+    // Parse Gradio 6 Label output
+    // data[0] = {label: "AI Generated", confidences: [{label,confidence},...]}
+    // data[1] = markdown string
     const labelObj  = predJson.data ? predJson.data[0] : {};
-    const detailStr = predJson.data ? (predJson.data[1] || "") : "";
-
-    let aiScore, realScore;
-    if(labelObj && typeof labelObj === "object" && "confidences" in labelObj){
-      // Gradio Label component format
-      const confs = labelObj.confidences || [];
-      const aiConf   = confs.find(c=>c.label==="AI Generated");
-      const realConf = confs.find(c=>c.label==="Real");
-      aiScore   = aiConf   ? Math.round(aiConf.confidence*100)   : 50;
-      realScore = realConf ? Math.round(realConf.confidence*100) : 50;
+    const detailStr = predJson.data ? String(predJson.data[1]||"") : "";
+
+    let aiScore = 50, realScore = 50;
+
+    if(labelObj && labelObj.confidences){
+      const confs = labelObj.confidences;
+      const aiC   = confs.find(c=>c.label==="AI Generated");
+      const reC   = confs.find(c=>c.label==="Real");
+      if(aiC)   aiScore   = Math.round(aiC.confidence*100);
+      if(reC)   realScore = Math.round(reC.confidence*100);
     } else if(labelObj && "AI Generated" in labelObj){
       aiScore   = Math.round((labelObj["AI Generated"]||0)*100);
       realScore = Math.round((labelObj["Real"]||0)*100);
     } else {
-      // Fallback: parse from markdown
-      const aiM  = detailStr.match(/AI[^*]*\*\*([\d.]+)%\*\*/);
-      const rlM  = detailStr.match(/Real[^*]*\*\*([\d.]+)%\*\*/);
-      aiScore   = aiM  ? parseFloat(aiM[1])  : (detailStr.includes("AI") ? 75 : 20);
-      realScore = rlM  ? parseFloat(rlM[1])  : Math.max(0, 100-aiScore);
+      // last resort: parse markdown
+      const am = detailStr.match(/AI[^*]*\*\*([\d.]+)%\*\*/);
+      const rm = detailStr.match(/Real[^*]*\*\*([\d.]+)%\*\*/);
+      if(am) aiScore   = parseFloat(am[1]);
+      if(rm) realScore = parseFloat(rm[1]);
     }
     const dfScore = Math.max(0, 100 - aiScore - realScore);
     setTimeout(()=>show(Math.round(aiScore), Math.round(realScore), dfScore), 500);
@@ -937,4 +935,4 @@ document.getElementById('btn-reset').addEventListener('click',()=>{
   res.style.display='none';pw.style.display='none';ip.src='';fi.value='';ba.style.display='none';
 });
 </script>
-</body></html>
+</body></html>