Update app.py

app.py

@@ -4,8 +4,8 @@ import base64
 import uuid
 import os
 import json
+import time
 from groq import Groq
-from huggingface_hub import model_info
 from fpdf import FPDF, XPos, YPos
 
 # ============================
@@ -25,55 +25,75 @@ client = Groq(api_key=GROQ_API_KEY)
 # MODEL ENDPOINTS (FREE)
 # ============================
 MODEL_ENDPOINTS = {
-    "SDXL": "stabilityai/stable-diffusion-xl-base-1.0",
+    "SD Turbo": "stabilityai/sd-turbo",
     "Playground v2.5": "playgroundai/playground-v2.5-1024px-aesthetic",
-    "PixArt-XL": "PixArt-alpha/PixArt-XL-2-1024-MS",
-    "SD Turbo": "stabilityai/sd-turbo"
+    "SDXL": "stabilityai/stable-diffusion-xl-base-1.0",
+    "PixArt-XL": "PixArt-alpha/PixArt-XL-2-1024-MS"
 }
 
 # ============================
-# ENGINE GAMBAR v2.0 (ANTI-GAGAL)
+# ENGINE GAMBAR v3.2 (ULTRA STABLE)
 # ============================
-def hf_generate_image(model, prompt):
-    url = f"https://api-inference.huggingface.co/models/{model}"
-
-    payload = {
-        "inputs": prompt,
-        "options": {"wait_for_model": True}
-    }
-
-    # Retry 3x untuk cold start
-    for attempt in range(3):
-        response = requests.post(url, headers=HEADERS, json=payload)
-
-        # Jika model masih loading
-        try:
-            data = response.json()
-            if "error" in data:
-                if "loading" in data["error"].lower():
-                    continue
-        except:
-            pass
-
-        # Jika response adalah gambar bytes
-        if response.status_code == 200:
-            content_type = response.headers.get("Content-Type", "")
-
-            # Jika raw image
-            if "image" in content_type:
-                filename = f"img_{uuid.uuid4().hex}.png"
-                os.makedirs("outputs", exist_ok=True)
-                filepath = os.path.join("outputs", filename)
-
-                with open(filepath, "wb") as f:
-                    f.write(response.content)
-
-                return filepath
-
-            # Jika base64
+def hf_generate_image_ultra(prompt, log_callback):
+
+    fallback_rounds = [
+        ["SD Turbo", "Playground v2.5", "SDXL", "PixArt-XL"],
+        ["Playground v2.5", "SDXL", "PixArt-XL", "SD Turbo"],
+        ["SDXL", "PixArt-XL", "Playground v2.5", "SD Turbo"]
+    ]
+
+    delays = [30, 60, 120, 180, 300, 600]
+
+    attempt = 1
+    total_attempts = 12
+
+    for round_idx, models in enumerate(fallback_rounds, start=1):
+        for model_name in models:
+
+            log_callback(f"[Try {attempt}/{total_attempts}] Model: {model_name}")
+
+            model = MODEL_ENDPOINTS[model_name]
+            url = f"https://api-inference.huggingface.co/models/{model}"
+
+            payload = {
+                "inputs": prompt,
+                "options": {"wait_for_model": True}
+            }
+
             try:
-                data = response.json()
-                if isinstance(data, dict):
+                response = requests.post(url, headers=HEADERS, json=payload)
+
+                # Cek JSON error
+                try:
+                    data = response.json()
+                    if "error" in data:
+                        log_callback(f"  → HF Error: {data['error']}")
+                        time.sleep(delays[min(attempt-1, len(delays)-1)])
+                        attempt += 1
+                        continue
+                except:
+                    pass
+
+                # Cek MIME type
+                content_type = response.headers.get("Content-Type", "")
+
+                if "image" in content_type:
+                    filename = f"img_{uuid.uuid4().hex}.png"
+                    os.makedirs("outputs", exist_ok=True)
+                    filepath = os.path.join("outputs", filename)
+
+                    with open(filepath, "wb") as f:
+                        f.write(response.content)
+
+                    if os.path.getsize(filepath) > 10000:
+                        log_callback("  → SUCCESS (image bytes)")
+                        return filepath
+                    else:
+                        log_callback("  → FAILED (image too small)")
+
+                # Cek base64
+                try:
+                    data = response.json()
                     if "generated_image" in data:
                         b64 = data["generated_image"].split(",")[-1]
                         img_bytes = base64.b64decode(b64)
@@ -85,10 +105,25 @@ def hf_generate_image(model, prompt):
                         with open(filepath, "wb") as f:
                             f.write(img_bytes)
 
-                        return filepath
-            except:
-                pass
+                        if os.path.getsize(filepath) > 10000:
+                            log_callback("  → SUCCESS (base64)")
+                            return filepath
+                        else:
+                            log_callback("  → FAILED (base64 too small)")
+                except:
+                    pass
+
+            except Exception as e:
+                log_callback(f"  → Exception: {str(e)}")
+
+            # Delay adaptif
+            delay = delays[min(attempt-1, len(delays)-1)]
+            log_callback(f"  → Waiting {delay} seconds before retry...")
+            time.sleep(delay)
+
+            attempt += 1
 
+    log_callback("❌ Gagal menghasilkan gambar setelah 12 percobaan.")
     return None
 
 # ============================
@@ -137,12 +172,12 @@ def build_visual_prompt(panel_text, style):
     """
 
 # ============================
-# PDF BUILDER (AMAN)
+# PDF BUILDER
 # ============================
 class ComicPDF(FPDF):
     pass
 
-def build_pdf(story, style, model, chapters):
+def build_pdf(story, style, chapters, log_callback):
     pdf = ComicPDF()
     pdf.set_auto_page_break(True, margin=15)
 
@@ -158,7 +193,9 @@ def build_pdf(story, style, model, chapters):
             pdf.cell(0, 8, f"Panel {p_idx}", new_x=XPos.LMARGIN, new_y=YPos.NEXT)
 
             visual_prompt = build_visual_prompt(panel, style)
-            img_path = hf_generate_image(model, visual_prompt)
+
+            log_callback(f"\n=== GENERATING PANEL {p_idx} ===")
+            img_path = hf_generate_image_ultra(visual_prompt, log_callback)
 
             if img_path:
                 pdf.image(img_path, x=20, w=170)
@@ -174,115 +211,40 @@ def build_pdf(story, style, model, chapters):
 # ============================
 # GRADIO UI
 # ============================
+def run_comic(story, style, chapters):
+    logs = []
+
+    def log_callback(msg):
+        logs.append(msg)
+
+    pdf_file = build_pdf(story, style, chapters, log_callback)
+    return pdf_file, "\n".join(logs)
+
+
 with gr.Blocks() as app:
 
-    gr.Markdown("# 🌟 AIPromptLab 3.1 — FREE MODE (HuggingFace Engine)")
-
-    with gr.Tabs():
-
-        # ============================
-        # TAB 1 — COMIC GENERATOR
-        # ============================
-        with gr.Tab("Comic Generator"):
-            story = gr.Textbox(label="Ide Cerita Komik")
-            style = gr.Dropdown(
-                label="Style Visual",
-                choices=[
-                    "Pastel 3D Isometric",
-                    "Cute 3D Cartoon",
-                    "Realistic Cinematic",
-                    "Lowpoly Diorama",
-                    "Claymation 3D",
-                    "Toy Photography",
-                    "Anime 3D Soft Light"
-                ],
-                value="Pastel 3D Isometric"
-            )
-            model = gr.Dropdown(
-                label="Model Gratis",
-                choices=list(MODEL_ENDPOINTS.keys()),
-                value="Playground v2.5"
-            )
-            chapters = gr.Slider(1, 10, value=1, step=1, label="Jumlah Bab")
-            btn = gr.Button("Generate Comic PDF")
-            out = gr.File()
-
-            def run_comic(story, style, model, chapters):
-                return build_pdf(story, style, MODEL_ENDPOINTS[model], chapters)
-
-            btn.click(run_comic, [story, style, model, chapters], out)
-
-        # ============================
-        # TAB 2 — IMAGE GENERATOR
-        # ============================
-        with gr.Tab("Image Generator"):
-            img_prompt = gr.Textbox(label="Prompt Gambar")
-            img_style = gr.Dropdown(
-                label="Style Visual",
-                choices=[
-                    "Pastel 3D Isometric",
-                    "Cute 3D Cartoon",
-                    "Realistic Cinematic",
-                    "Lowpoly Diorama",
-                    "Claymation 3D",
-                    "Toy Photography",
-                    "Anime 3D Soft Light"
-                ],
-                value="Pastel 3D Isometric"
-            )
-            img_model = gr.Dropdown(
-                label="Model Gratis",
-                choices=list(MODEL_ENDPOINTS.keys()),
-                value="Playground v2.5"
-            )
-            btn2 = gr.Button("Generate Image")
-            img_out = gr.Image()
-
-            def generate_image(p, s, m):
-                prompt = f"{s}. Scene: {p}"
-                return hf_generate_image(MODEL_ENDPOINTS[m], prompt)
-
-            btn2.click(generate_image, [img_prompt, img_style, img_model], img_out)
-
-        # ============================
-        # TAB 3 — VIDEO STORYBOARD
-        # ============================
-        with gr.Tab("Video Storyboard"):
-            vid_desc = gr.Textbox(label="Deskripsi Video")
-            vid_style = gr.Dropdown(
-                label="Style Visual",
-                choices=[
-                    "Pastel 3D Isometric",
-                    "Cute 3D Cartoon",
-                    "Realistic Cinematic",
-                    "Lowpoly Diorama",
-                    "Claymation 3D",
-                    "Toy Photography",
-                    "Anime 3D Soft Light"
-                ],
-                value="Pastel 3D Isometric"
-            )
-            vid_model = gr.Dropdown(
-                label="Model Gratis",
-                choices=list(MODEL_ENDPOINTS.keys()),
-                value="SD Turbo"
-            )
-            btn3 = gr.Button("Generate Storyboard Frames")
-            frame_gallery = gr.Gallery(label="Frames")
-
-            def generate_storyboard(desc, style, model):
-                prompt = f"Buatkan 8 scene storyboard. Deskripsi: {desc}"
-                scenes = ai(prompt).split("\n")
-                frames = []
-
-                for s in scenes[:8]:
-                    visual = build_visual_prompt(s, style)
-                    img_path = hf_generate_image(MODEL_ENDPOINTS[model], visual)
-                    if img_path:
-                        frames.append(img_path)
-
-                return frames
-
-            btn3.click(generate_storyboard, [vid_desc, vid_style, vid_model], frame_gallery)
+    gr.Markdown("# 🌟 AIPromptLab 3.2 — Ultra Stable Free Mode (HuggingFace Engine)")
+
+    with gr.Tab("Comic Generator"):
+        story = gr.Textbox(label="Ide Cerita Komik")
+        style = gr.Dropdown(
+            label="Style Visual",
+            choices=[
+                "Pastel 3D Isometric",
+                "Cute 3D Cartoon",
+                "Realistic Cinematic",
+                "Lowpoly Diorama",
+                "Claymation 3D",
+                "Toy Photography",
+                "Anime 3D Soft Light"
+            ],
+            value="Pastel 3D Isometric"
+        )
+        chapters = gr.Slider(1, 10, value=1, step=1, label="Jumlah Bab")
+        btn = gr.Button("Generate Comic PDF")
+        out_pdf = gr.File()
+        out_log = gr.Textbox(label="Log Proses", lines=30)
+
+        btn.click(run_comic, [story, style, chapters], [out_pdf, out_log])
 
 app.launch(ssr_mode=False)