Update app.py

app.py

@@ -1,19 +1,22 @@
 import os
-import cv2
 import torch
 import numpy as np
 from PIL import Image
+import cv2
 import gradio as gr
 from gradio_client import Client, handle_file
 from torchvision.transforms import Normalize
 from facenet_pytorch.models.mtcnn import MTCNN
 import concurrent.futures
 import tempfile
+from huggingface_hub import get_token
+from fastapi import FastAPI, UploadFile, File
+from fastapi.responses import HTMLResponse
+import shutil
 
 # ==========================================
 # 1. API ROUTER CONFIGURATION
 # ==========================================
-# These must match your exact Hugging Face Worker Space names
 WORKER_SPACES = [
     "bithal26/DeepFake-Worker-1",
     "bithal26/DeepFake-Worker-2",
@@ -24,21 +27,19 @@ WORKER_SPACES = [
     "bithal26/DeepFake-Worker-7"
 ]
 
-# Note: If your worker spaces are PRIVATE, you must add your HF_TOKEN 
-# to this UI Space's Secrets for the Client to connect successfully.
 clients = []
 print("Initializing connections to 7 API Workers...")
+hf_token = get_token()
 
-# Explicitly grab the token from the Space Secrets
-hf_token = os.environ.get("HF_TOKEN") 
+if not hf_token:
+    print("CRITICAL WARNING: No HF_TOKEN found! Private workers will fail to connect.")
 
 for space in WORKER_SPACES:
     try:
-        # Pass the token directly to the Client so it can unlock the private spaces
         clients.append(Client(space, token=hf_token))
     except Exception as e:
-        print(f"Warning: Could not connect to {space}. Is it private/sleeping? Error: {e}")
-        
+        print(f"Warning: Could not connect to {space}. Error: {e}")
+
 # ==========================================
 # 2. MTCNN PREPROCESSING ENGINE
 # ==========================================
@@ -125,32 +126,43 @@ def confident_strategy(pred, t=0.8):
     else:
         return np.mean(pred)
 
-# ==========================================
-# 3. PARALLEL API EXECUTION
-# ==========================================
 def call_worker(client, tensor_filepath):
-    """Pings a single Hugging Face API Worker"""
     try:
         result = client.predict(tensor_file=handle_file(tensor_filepath), api_name="/predict")
-        # Result should be a dictionary: {"predictions": [...]}
         preds = result.get("predictions", [])
-        if not preds:
-            return 0.5 # Default middle ground if error
+        if not preds: return 0.5 
         return confident_strategy(preds)
     except Exception as e:
         print(f"API Call Failed: {e}")
         return 0.5 
 
-def analyze_video(video_path):
-    if not video_path:
-        return "<div style='color:var(--red); font-family:Syne;'>Please upload a video file.</div>"
-    
-    # 1. Extract Faces locally
+# ==========================================
+# 3. FASTAPI SERVER & DIRECT HTML INJECTION
+# ==========================================
+app = FastAPI()
+
+# 1. Serve your custom HTML file as the main page
+@app.get("/")
+def read_root():
+    with open("deepfake-detector.html", "r", encoding="utf-8") as f:
+        html_content = f.read()
+    return HTMLResponse(content=html_content)
+
+# 2. Create the hidden API endpoint your HTML will call
+@app.post("/api/analyze")
+async def analyze_api(file: UploadFile = File(...)):
+    # Save uploaded video temporarily
+    temp_dir = tempfile.mkdtemp()
+    video_path = os.path.join(temp_dir, file.filename)
+    with open(video_path, "wb") as buffer:
+        shutil.copyfileobj(file.file, buffer)
+
+    # Extract Faces
     input_size = 380
     faces = face_extractor.process_video(video_path, frames_per_video=16)
     
     if len(faces) == 0:
-        return "<div style='color:var(--amber); font-family:Syne; padding:20px;'>No faces detected. Please upload a clear video.</div>"
+        return {"error": "No faces detected."}
 
     x = []
     for frame_data in faces:
@@ -158,8 +170,7 @@ def analyze_video(video_path):
             resized_face = isotropically_resize_image(face, input_size)
             resized_face = put_to_center(resized_face, input_size)
             x.append(resized_face)
-            if len(x) >= 16 * 4: 
-                break
+            if len(x) >= 16 * 4: break
 
     x = np.array(x, dtype=np.uint8)
     x = torch.tensor(x, device=device).float()
@@ -167,167 +178,29 @@ def analyze_video(video_path):
     for i in range(len(x)):
         x[i] = normalize_transform(x[i] / 255.)
 
-    # 2. Save the math to a temporary file
-    temp_dir = tempfile.gettempdir()
+    # Save Tensor
     tensor_path = os.path.join(temp_dir, "batch_tensor.pt")
     torch.save(x, tensor_path)
 
-    # 3. Ping all 7 Workers in parallel
+    # Ping Workers
     worker_scores = []
     with concurrent.futures.ThreadPoolExecutor(max_workers=7) as executor:
         futures = [executor.submit(call_worker, client, tensor_path) for client in clients]
         for future in concurrent.futures.as_completed(futures):
             worker_scores.append(future.result())
 
-    # 4. Aggregate results
+    # Aggregate
     final_score = np.mean(worker_scores)
-    is_fake = final_score > 0.5
-    display_score = (final_score * 100) if is_fake else ((1 - final_score) * 100)
-    
-    # Format the individual scores for the UI
-    model_bars_html = ""
-    for i, score in enumerate(worker_scores):
-        percentage = score * 100
-        color = "var(--red)" if percentage > 50 else "var(--green)"
-        model_bars_html += f"""
-        <div class="metric-row">
-            <div class="metric-header"><span class="metric-name">EfficientNet Node {i+1}</span><span class="metric-value">{percentage:.1f}%</span></div>
-            <div class="metric-bar"><div class="metric-fill" style="width:{percentage}%; background:{color}"></div></div>
-        </div>
-        """
-
-    # 5. Inject into your Custom HTML Template
-    verdict_color = "var(--red)" if is_fake else "var(--green)"
-    verdict_text = "DEEPFAKE DETECTED" if is_fake else "AUTHENTIC CONTENT"
-    verdict_desc = "High confidence manipulation detected. Neural forensics indicate spatial anomalies and blending artifacts typical of synthetic face-swapping." if is_fake else "No significant facial manipulation detected. Spatial forensics are within normal parameters. Content appears to be authentic media."
-    
-    # Calculate a proxy for "Face Anomaly" vs "Temporal" based on the raw score to fill your template's visual metrics
-    face_anomaly_score = (final_score * 100) if is_fake else (final_score * 100)
-    
-    html_report = f"""
-    <div class="report-layout">
-        <div class="report-card accent">
-            <div class="card-title"><span class="dot"></span>Forensic Analysis Report</div>
-            <div style="margin-top:8px">
-                <div style="display:flex;justify-content:space-between;align-items:center;margin-bottom:24px">
-                    <div>
-                        <div style="font-family:'JetBrains Mono',monospace;font-size:10px;letter-spacing:2px;color:var(--text-faint);text-transform:uppercase">Verdict</div>
-                        <div style="font-family:'Bebas Neue',sans-serif;font-size:32px;color:{verdict_color};margin-top:4px">{verdict_text}</div>
-                    </div>
-                    <div style="text-align:right">
-                        <div style="font-family:'Bebas Neue',sans-serif;font-size:48px;color:{verdict_color};text-shadow:0 0 20px {verdict_color};line-height:1">{display_score:.1f}%</div>
-                        <div style="font-family:'JetBrains Mono',monospace;font-size:9px;letter-spacing:2px;color:{verdict_color};text-transform:uppercase">Confidence</div>
-                    </div>
-                </div>
-                <p style="color:var(--text-dim); font-size:14px; line-height:1.6; margin-bottom:20px;">{verdict_desc}</p>
-                <ul class="forensic-list">
-                    <li class="forensic-item">
-                        <div class="forensic-icon"><svg viewBox="0 0 24 24"><circle cx="12" cy="8" r="4"/><path d="M20 21a8 8 0 1 0-16 0"/></svg></div>
-                        <span class="forensic-name">Spatial Artifact Detection</span>
-                        <span class="forensic-status {'alert' if is_fake else 'pass'}">{'Anomaly' if is_fake else 'Pass'}</span>
-                    </li>
-                    <li class="forensic-item">
-                        <div class="forensic-icon"><svg viewBox="0 0 24 24"><path d="M4 15s1-1 4-1 5 2 8 2 4-1 4-1V3s-1 1-4 1-5-2-8-2-4 1-4 1z"/><line x1="4" y1="22" x2="4" y2="15"/></svg></div>
-                        <span class="forensic-name">Feature Extraction Integrity</span>
-                        <span class="forensic-status {'alert' if face_anomaly_score > 60 else 'pass'}">{'Fail' if face_anomaly_score > 60 else 'Normal'}</span>
-                    </li>
-                </ul>
-            </div>
-        </div>
-
-        <div style="display:flex;flex-direction:column;gap:2px">
-            <div class="report-card" style="flex:1">
-                <div class="card-title"><span class="dot"></span>Ensemble Node Breakdown</div>
-                <div style="margin-top:16px">
-                    {model_bars_html}
-                </div>
-            </div>
-        </div>
-    </div>
-    """
-    return html_report
-
-# ==========================================
-# 4. MASTER UI - NETFLIX HTML INTEGRATION
-# ==========================================
-# We pull your exact CSS variables and styling directly from your deepfake-detector.html
-css = """
-@import url('https://fonts.googleapis.com/css2?family=Bebas+Neue&family=Syne:wght@400;600;700;800&family=JetBrains+Mono:wght@300;400;500&display=swap');
-
-:root {
-    --bg: #030508;
-    --bg2: #070c12;
-    --panel: rgba(8, 18, 30, 0.85);
-    --border: rgba(0, 210, 255, 0.12);
-    --border-bright: rgba(0, 210, 255, 0.45);
-    --cyan: #00d2ff;
-    --red: #ff2d55;
-    --green: #00ff88;
-    --amber: #ffb800;
-    --text: #e8f4ff;
-    --text-dim: rgba(232, 244, 255, 0.5);
-    --text-faint: rgba(232, 244, 255, 0.25);
-}
-
-body, .gradio-container { background-color: var(--bg) !important; color: var(--text) !important; font-family: 'Syne', sans-serif !important; }
-.gr-panel { background: var(--panel) !important; border: 1px solid var(--border) !important; border-radius: 4px !important; }
-
-/* Dashboard Titles */
-.veridex-title { font-family: 'Bebas Neue', sans-serif; font-size: 60px; letter-spacing: 4px; color: var(--text); text-align: center; margin-top: 40px;}
-.veridex-title span { color: var(--cyan); }
-.veridex-sub { font-family: 'JetBrains Mono', monospace; font-size: 12px; letter-spacing: 2px; text-transform: uppercase; color: var(--cyan); text-align: center; margin-bottom: 40px; }
-
-/* Custom HTML injected classes from your design */
-.report-layout { display: grid; grid-template-columns: 1fr 1fr; gap: 16px; margin-top: 20px; }
-.report-card { background: var(--panel); border: 1px solid var(--border); padding: 30px; }
-.report-card.accent { border-color: rgba(0,210,255,0.2); background: rgba(0, 210, 255, 0.04); }
-.card-title { font-family: 'JetBrains Mono', monospace; font-size: 10px; letter-spacing: 3px; text-transform: uppercase; color: var(--cyan); margin-bottom: 16px; display: flex; align-items: center; gap: 8px; }
-.card-title .dot { width: 5px; height: 5px; border-radius: 50%; background: var(--cyan); box-shadow: 0 0 8px var(--cyan); }
-
-.forensic-list { list-style: none; display: flex; flex-direction: column; gap: 12px; padding:0; }
-.forensic-item { display: flex; align-items: center; gap: 12px; padding: 14px 16px; border: 1px solid var(--border); }
-.forensic-icon { width: 32px; height: 32px; border: 1px solid var(--border-bright); display: flex; align-items: center; justify-content: center; }
-.forensic-icon svg { width: 14px; height: 14px; stroke: var(--cyan); fill: none; stroke-width: 2; }
-.forensic-name { font-size: 13px; font-weight: 600; flex: 1; font-family: 'Syne', sans-serif;}
-.forensic-status { font-family: 'JetBrains Mono', monospace; font-size: 9px; letter-spacing: 2px; text-transform: uppercase; padding: 3px 8px; }
-.forensic-status.pass { color: var(--green); border: 1px solid rgba(0,255,136,0.3); background: rgba(0,255,136,0.05); }
-.forensic-status.alert { color: var(--red); border: 1px solid rgba(255,45,85,0.3); background: rgba(255,45,85,0.05); }
-
-.metric-row { margin-bottom: 14px; }
-.metric-header { display: flex; justify-content: space-between; margin-bottom: 6px; }
-.metric-name { font-family: 'JetBrains Mono', monospace; font-size: 10px; letter-spacing: 1.5px; text-transform: uppercase; color: var(--text-dim); }
-.metric-value { font-family: 'JetBrains Mono', monospace; font-size: 10px; color: var(--text); }
-.metric-bar { height: 3px; background: rgba(255,255,255,0.06); width: 100%; overflow: hidden; }
-.metric-fill { height: 100%; transition: width 1s ease; }
-
-@media (max-width: 900px) { .report-layout { grid-template-columns: 1fr; } }
-"""
-
-with gr.Blocks(css=css, theme=gr.themes.Default(neutral_hue="slate", primary_hue="cyan")) as app:
-    gr.HTML("""
-    <div class="veridex-title">VERI<span>DEX</span></div>
-    <div class="veridex-sub">Neural Detection Engine v4.2 // Distributed Architecture</div>
-    """)
     
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("### 1. Ingest Video Evidence")
-            video_in = gr.Video(label="Upload Media (.mp4, .avi)")
-            analyze_btn = gr.Button("Run Distributed Ensemble Analysis", variant="primary", size="lg")
-            
-            gr.HTML("""
-            <div style="margin-top:20px; font-family:'JetBrains Mono'; font-size:10px; color:var(--text-faint); line-height:1.8;">
-                › Local MTCNN Node Active<br>
-                › 7 Parallel EfficientNet Endpoints Linked<br>
-                › Awaiting input...
-            </div>
-            """)
-            
-        with gr.Column(scale=2):
-            gr.Markdown("### 2. Forensic Output")
-            report_out = gr.HTML(value="<div style='color:var(--text-dim); padding:40px; text-align:center; border:1px dashed var(--border);'>Awaiting video analysis...</div>")
-            
-    analyze_btn.click(fn=analyze_video, inputs=video_in, outputs=report_out)
-
-if __name__ == "__main__":
-    app.launch()
+    # Clean up temp files
+    shutil.rmtree(temp_dir, ignore_errors=True)
+
+    # Return pure JSON data to the HTML frontend
+    return {
+        "final_score": float(final_score),
+        "worker_scores": [float(s) for s in worker_scores]
+    }
+
+# Gradio wrapper just to keep Hugging Face happy, but we mount our custom FastAPI app
+demo = gr.Blocks()
+app = gr.mount_gradio_app(app, demo, path="/gradio")