Update app.py

app.py

@@ -9,7 +9,7 @@ from PIL import Image
 from gradio_client import Client
 
 # ==========================================
-# 1. API Keys (তোমার দেওয়া লিস্ট)
+# 1. API Keys
 # ==========================================
 KEYS = {
     "deepai": "16e69995-00d4-4ffd-993c-a026632f6de6",
@@ -27,181 +27,147 @@ KEYS = {
 }
 
 # ==========================================
-# 2. API Functions (Private API)
+# 2. Private API Functions (FAST)
 # ==========================================
 def get_image_from_url(url):
-    response = requests.get(url)
-    return Image.open(BytesIO(response.content))
+    return Image.open(BytesIO(requests.get(url).content))
 
-def generate_stability(prompt, w, h, ar):
-    response = requests.post(
-        "https://api.stability.ai/v2beta/stable-image/generate/core",
-        headers={"Authorization": f"Bearer {KEYS['stability']}", "Accept": "image/*"},
-        files={"none": ''},
-        data={"prompt": prompt, "output_format": "jpeg", "aspect_ratio": ar}
-    )
-    if response.status_code == 200: return Image.open(BytesIO(response.content))
+def generate_stability(p, w, h, ar):
+    res = requests.post("https://api.stability.ai/v2beta/stable-image/generate/core", headers={"Authorization": f"Bearer {KEYS['stability']}", "Accept": "image/*"}, files={"none": ''}, data={"prompt": p, "output_format": "jpeg", "aspect_ratio": ar})
+    if res.status_code == 200: return Image.open(BytesIO(res.content))
     raise Exception("Error")
 
-def generate_getimg(prompt, w, h, ar):
-    response = requests.post(
-        "https://api.getimg.ai/v1/essential/text-to-image",
-        headers={"Authorization": f"Bearer {KEYS['getimg']}"},
-        json={"prompt": prompt, "width": w, "height": h}
-    )
-    if response.status_code == 200: return Image.open(BytesIO(base64.b64decode(response.json()["image"])))
+def generate_getimg(p, w, h, ar):
+    res = requests.post("https://api.getimg.ai/v1/essential/text-to-image", headers={"Authorization": f"Bearer {KEYS['getimg']}"}, json={"prompt": p, "width": w, "height": h})
+    if res.status_code == 200: return Image.open(BytesIO(base64.b64decode(res.json()["image"])))
     raise Exception("Error")
 
-def generate_segmind(prompt, w, h, ar):
-    response = requests.post(
-        "https://api.segmind.com/v1/sdxl1.0-txt2img",
-        headers={"x-api-key": KEYS['segmind']},
-        json={"prompt": prompt, "samples": 1, "width": w, "height": h}
-    )
-    if response.status_code == 200: return Image.open(BytesIO(response.content))
+def generate_segmind(p, w, h, ar):
+    res = requests.post("https://api.segmind.com/v1/sdxl1.0-txt2img", headers={"x-api-key": KEYS['segmind']}, json={"prompt": p, "samples": 1, "width": w, "height": h})
+    if res.status_code == 200: return Image.open(BytesIO(res.content))
     raise Exception("Error")
 
-def generate_fireworks(prompt, w, h, ar):
-    response = requests.post(
-        "https://api.fireworks.ai/inference/v1/image_generation/accounts/fireworks/models/stable-diffusion-xl-1024-v1-0",
-        headers={"Authorization": f"Bearer {KEYS['fireworks']}"},
-        json={"text_prompts": [{"text": prompt}], "width": w, "height": h}
-    )
-    if response.status_code == 200: return Image.open(BytesIO(base64.b64decode(response.json()[0]["base64"])))
+def generate_fireworks(p, w, h, ar):
+    res = requests.post("https://api.fireworks.ai/inference/v1/image_generation/accounts/fireworks/models/stable-diffusion-xl-1024-v1-0", headers={"Authorization": f"Bearer {KEYS['fireworks']}"}, json={"text_prompts": [{"text": p}], "width": w, "height": h})
+    if res.status_code == 200: return Image.open(BytesIO(base64.b64decode(res.json()[0]["base64"])))
     raise Exception("Error")
 
-def generate_deepai(prompt, w, h, ar):
-    response = requests.post("https://api.deepai.org/api/text2img", data={'text': prompt}, headers={'api-key': KEYS['deepai']})
-    if response.status_code == 200: return get_image_from_url(response.json()['output_url'])
+def generate_google_imagen(p, w, h, ar):
+    res = requests.post(f"https://generativelanguage.googleapis.com/v1beta/models/imagen-3.0-generate-001:predict?key={KEYS['google']}", json={"instances": [{"prompt": p}]})
+    if res.status_code == 200: return Image.open(BytesIO(base64.b64decode(res.json()['predictions'][0]['bytesBase64Encoded'])))
     raise Exception("Error")
 
-def generate_google_imagen(prompt, w, h, ar):
-    response = requests.post(
-        f"https://generativelanguage.googleapis.com/v1beta/models/imagen-3.0-generate-001:predict?key={KEYS['google']}",
-        json={"instances": [{"prompt": prompt}]}
-    )
-    if response.status_code == 200: return Image.open(BytesIO(base64.b64decode(response.json()['predictions'][0]['bytesBase64Encoded'])))
+def generate_deepai(p, w, h, ar):
+    res = requests.post("https://api.deepai.org/api/text2img", data={'text': p}, headers={'api-key': KEYS['deepai']})
+    if res.status_code == 200: return get_image_from_url(res.json()['output_url'])
     raise Exception("Error")
 
-def generate_edenai(prompt, w, h, ar):
-    response = requests.post(
-        "https://api.edenai.run/v2/image/generation",
-        headers={"Authorization": f"Bearer {KEYS['edenai']}"},
-        json={"providers": "openai", "text": prompt, "resolution": "1024x1024"}
-    )
-    if response.status_code == 200: return get_image_from_url(response.json()['openai']['items'][0]['image_resource_url'])
+def generate_edenai(p, w, h, ar):
+    res = requests.post("https://api.edenai.run/v2/image/generation", headers={"Authorization": f"Bearer {KEYS['edenai']}"}, json={"providers": "openai", "text": p, "resolution": "1024x1024"})
+    if res.status_code == 200: return get_image_from_url(res.json()['openai']['items'][0]['image_resource_url'])
     raise Exception("Error")
 
-def generate_modelslab(prompt, w, h, ar):
-    response = requests.post(
-        "https://modelslab.com/api/v6/images/text2img",
-        json={"key": KEYS['modelslab'], "prompt": prompt, "width": str(w), "height": str(h)}
-    )
-    if response.status_code == 200 and "output" in response.json(): return get_image_from_url(response.json()["output"][0])
+def generate_modelslab(p, w, h, ar):
+    res = requests.post("https://modelslab.com/api/v6/images/text2img", json={"key": KEYS['modelslab'], "prompt": p, "width": str(w), "height": str(h)})
+    if res.status_code == 200 and "output" in res.json(): return get_image_from_url(res.json()["output"][0])
     raise Exception("Error")
 
-def generate_openrouter(prompt, w, h, ar):
-    response = requests.post(
-        "https://openrouter.ai/api/v1/chat/completions",
-        headers={"Authorization": f"Bearer {KEYS['openrouter']}"},
-        json={"model": "google/imagen-3-fast-generate", "messages": [{"role": "user", "content": prompt}]}
-    )
-    if response.status_code == 200:
-        content = response.json()['choices'][0]['message']['content']
-        if "http" in content:
-            url = content.split("(")[-1].split(")")[0] if "(" in content else content.strip()
-            return get_image_from_url(url)
+def generate_openrouter(p, w, h, ar):
+    res = requests.post("https://openrouter.ai/api/v1/chat/completions", headers={"Authorization": f"Bearer {KEYS['openrouter']}"}, json={"model": "google/imagen-3-fast-generate", "messages": [{"role": "user", "content": p}]})
+    if res.status_code == 200:
+        content = res.json()['choices'][0]['message']['content']
+        if "http" in content: return get_image_from_url(content.split("(")[-1].split(")")[0] if "(" in content else content.strip())
     raise Exception("Error")
 
-def generate_stable_horde(prompt, w, h, ar):
-    response = requests.post(
-        "https://stablehorde.net/api/v2/generate/async",
-        headers={"apikey": KEYS['stablehorde']},
-        json={"prompt": prompt, "params": {"n": 1, "width": 512, "height": 512}}
-    )
-    if response.status_code == 202:
-        job_id = response.json()['id']
-        for _ in range(15):
-            time.sleep(2)
-            check = requests.get(f"https://stablehorde.net/api/v2/generate/status/{job_id}")
-            if check.status_code == 200 and check.json().get("done", False):
-                return get_image_from_url(check.json()['generations'][0]['img'])
+def generate_pollinations(p, w, h, ar):
+    encoded = urllib.parse.quote(p)
+    res = requests.get(f"https://image.pollinations.ai/prompt/{encoded}?width={w}&height={h}&nologo=true")
+    if res.status_code == 200: return Image.open(BytesIO(res.content))
     raise Exception("Error")
 
 # ==========================================
-# 3. New Unlimited & HF Free API Functions
+# 3. HF & Slow API Functions (SLOW)
 # ==========================================
-def generate_pollinations(prompt, w, h, ar):
-    # এটি সম্পূর্ণ ফ্রি এবং আনলিমিটেড
-    encoded_prompt = urllib.parse.quote(prompt)
-    url = f"https://image.pollinations.ai/prompt/{encoded_prompt}?width={w}&height={h}&nologo=true"
-    response = requests.get(url)
-    if response.status_code == 200:
-        return Image.open(BytesIO(response.content))
+def generate_stable_horde(p, w, h, ar):
+    res = requests.post("https://stablehorde.net/api/v2/generate/async", headers={"apikey": KEYS['stablehorde']}, json={"prompt": p, "params": {"n": 1, "width": 512, "height": 512}})
+    if res.status_code == 202:
+        job_id = res.json()['id']
+        for _ in range(15):
+            time.sleep(2)
+            check = requests.get(f"https://stablehorde.net/api/v2/generate/status/{job_id}")
+            if check.status_code == 200 and check.json().get("done", False): return get_image_from_url(check.json()['generations'][0]['img'])
     raise Exception("Error")
 
-def generate_hf_api(prompt, model_id):
-    # HF Direct API (কোনো Gradio Client বা লাইনের ঝামেলা নেই)
+def generate_hf_space(client_name, api_name, p):
+    try:
+        client = Client(client_name)
+        result = client.predict(p, api_name=api_name)
+        return Image.open(result[0] if isinstance(result, (list, tuple)) else result)
+    except:
+        raise Exception("Error")
+
+def generate_hf_api(prompt, model_id, w, h):
+    # এই ফাংশনে সাইজ (width, height) ফিক্স করে দেওয়া হয়েছে!
     url = f"https://api-inference.huggingface.co/models/{model_id}"
-    response = requests.post(url, json={"inputs": prompt})
-    if response.status_code == 200:
-        return Image.open(BytesIO(response.content))
+    payload = {"inputs": prompt, "parameters": {"width": w, "height": h}}
+    res = requests.post(url, json=payload)
+    if res.status_code == 200: return Image.open(BytesIO(res.content))
     raise Exception("Error")
 
-# HF Public Spaces (আগেরগুলো)
-def generate_hf_flux(prompt, w, h, ar):
-    client = Client("black-forest-labs/FLUX.1-schnell")
-    result = client.predict(prompt=prompt, seed=0, randomize_seed=True, width=1024, height=1024, num_inference_steps=4, api_name="/infer")
-    return Image.open(result[0])
-
-def generate_hf_sdxl_lightning(prompt, w, h, ar):
-    client = Client("ByteDance/SDXL-Lightning")
-    result = client.predict(prompt, "4-Step", api_name="/generate_image")
-    return Image.open(result)
-
-def generate_hf_sd3(prompt, w, h, ar):
-    client = Client("stabilityai/stable-diffusion-3.5-large")
-    result = client.predict(prompt=prompt, negative_prompt="", seed=0, randomize_seed=True, width=1024, height=1024, guidance_scale=4.5, num_inference_steps=40, api_name="/infer")
-    return Image.open(result[0])
-
 # ==========================================
-# 4. Mega Pipeline Dictionary (18 Servers!)
+# 4. API Categorization
 # ==========================================
-API_PIPELINE = {
-    "Pollinations AI (Unlimited)": generate_pollinations,  # আনলিমিটেড ফ্রি
-    "Stability AI": generate_stability, 
+FAST_APIS = {
     "Google Imagen": generate_google_imagen,
+    "Stability AI": generate_stability, 
     "GetImg AI": generate_getimg, 
     "Segmind": generate_segmind, 
     "Fireworks": generate_fireworks,
+    "Pollinations AI (Unlimited)": generate_pollinations,
     "OpenRouter": generate_openrouter,
     "DeepAI": generate_deepai,
     "Eden AI": generate_edenai,
-    "ModelsLab": generate_modelslab,
-    "HF API: OpenJourney (Midjourney V4)": lambda p, w, h, ar: generate_hf_api(p, "prompthero/openjourney"),
-    "HF API: RealVisXL (Realistic)": lambda p, w, h, ar: generate_hf_api(p, "SG161222/RealVisXL_V4.0"),
-    "HF API: Animagine XL (Anime)": lambda p, w, h, ar: generate_hf_api(p, "cagliostrolab/animagine-xl-3.1"),
-    "HF API: Playground v2.5": lambda p, w, h, ar: generate_hf_api(p, "playgroundai/playground-v2.5-1024px-aesthetic"),
+    "ModelsLab": generate_modelslab
+}
+
+SLOW_APIS = {
     "Stable Horde (Crowdsourced)": generate_stable_horde,
-    "HF Space: FLUX.1 (Public)": generate_hf_flux,
-    "HF Space: SDXL Light (Public)": generate_hf_sdxl_lightning,
-    "HF Space: SD3.5 (Public)": generate_hf_sd3
+    "HF Space: FLUX.1": lambda p, w, h, ar: generate_hf_space("black-forest-labs/FLUX.1-schnell", "/infer", p),
+    "HF Space: SDXL Light": lambda p, w, h, ar: generate_hf_space("ByteDance/SDXL-Lightning", "/generate_image", p),
+    
+    # আগের ৪টি HF API
+    "HF API: OpenJourney": lambda p, w, h, ar: generate_hf_api(p, "prompthero/openjourney", w, h),
+    "HF API: RealVisXL": lambda p, w, h, ar: generate_hf_api(p, "SG161222/RealVisXL_V4.0", w, h),
+    "HF API: Animagine XL": lambda p, w, h, ar: generate_hf_api(p, "cagliostrolab/animagine-xl-3.1", w, h),
+    "HF API: Playground v2.5": lambda p, w, h, ar: generate_hf_api(p, "playgroundai/playground-v2.5-1024px-aesthetic", w, h),
+    
+    # তোমার চাওয়া নতুন ১০টি HF API! 
+    "HF API: DreamShaper XL": lambda p, w, h, ar: generate_hf_api(p, "Lykon/dreamshaper-xl-1.0", w, h),
+    "HF API: OpenDalle V1.1": lambda p, w, h, ar: generate_hf_api(p, "dataautogpt3/OpenDalleV1.1", w, h),
+    "HF API: Anything V5 (Anime)": lambda p, w, h, ar: generate_hf_api(p, "stablediffusionapi/anything-v5", w, h),
+    "HF API: Disney Pixar Cartoon": lambda p, w, h, ar: generate_hf_api(p, "stablediffusionapi/disney-pixar-cartoon", w, h),
+    "HF API: CyberRealistic": lambda p, w, h, ar: generate_hf_api(p, "stablediffusionapi/cyberrealistic", w, h),
+    "HF API: Absolute Reality": lambda p, w, h, ar: generate_hf_api(p, "stablediffusionapi/absolute-reality-v181", w, h),
+    "HF API: Juggernaut XL": lambda p, w, h, ar: generate_hf_api(p, "stablediffusionapi/juggernaut-xl-v5", w, h),
+    "HF API: SDXL Base 1.0": lambda p, w, h, ar: generate_hf_api(p, "stabilityai/stable-diffusion-xl-base-1.0", w, h),
+    "HF API: Realistic Vision V5": lambda p, w, h, ar: generate_hf_api(p, "SG161222/Realistic_Vision_V5.1_noVAE", w, h),
+    "HF API: Stable Diffusion 1.5": lambda p, w, h, ar: generate_hf_api(p, "runwayml/stable-diffusion-v1-5", w, h)
 }
 
+# সব মিলিয়ে একটি মাস্টার ডিকশনারি (ম্যানুয়াল সিলেকশনের জন্য)
+ALL_APIS = {**FAST_APIS, **SLOW_APIS}
+
 # ==========================================
-# 5. Generation Logic (Auto + Manual)
+# 5. Generation Logic (Fast/Slow + Manual)
 # ==========================================
-def generate_image_logic(prompt, style, size, selected_api):
+def generate_image_logic(prompt, style, size, speed_mode, selected_api):
     if not prompt.strip():
         yield None, "⚠️ **দয়া করে একটি প্রম্পট লিখুন!**"
         return
 
-    if size == "ল্যান্ডস্কেপ (16:9) - YouTube":
-        w, h, ar = 1024, 576, "16:9"
-    elif size == "পোর্ট্রেট (9:16) - TikTok":
-        w, h, ar = 576, 1024, "9:16"
-    else:
-        w, h, ar = 1024, 1024, "1:1"
+    if size == "ল্যান্ডস্কেপ (16:9) - YouTube": w, h, ar = 1024, 576, "16:9"
+    elif size == "পোর্ট্রেট (9:16) - TikTok": w, h, ar = 576, 1024, "9:16"
+    else: w, h, ar = 1024, 1024, "1:1"
 
     style_modifiers = {
         "ফটোরিয়ালিস্টিক (Photorealistic)": ", ultra realistic, highly detailed, photorealistic, 8k resolution, raw photo, masterpiece, soft cinematic lighting",
@@ -211,43 +177,42 @@ def generate_image_logic(prompt, style, size, selected_api):
     }
     enhanced_prompt = f"{prompt.strip()}{style_modifiers.get(style, '')}, aspect ratio {ar}"
 
-    # অটোমেটিক মোড
+    # লজিক ১: অটোমেটিক (Fast বা Slow মোড অনুযায়ী)
     if selected_api == "অটোমেটিক (Auto Fallback)":
-        yield None, "⏳ শুরু হচ্ছে..."
-        for api_name, api_func in API_PIPELINE.items():
-            yield None, f"⏳ **{api_name}**-এর সাথে কানেক্ট করা হচ্ছে..."
+        target_apis = FAST_APIS if speed_mode == "Fast (ফাস্ট)" else SLOW_APIS
+        yield None, f"⏳ {speed_mode} মোডে শুরু হচ্ছে..."
+        
+        for api_name, api_func in target_apis.items():
+            yield None, f"⏳ **{api_name}**-এ কল করা হচ্ছে..."
             try:
                 image = api_func(enhanced_prompt, w, h, ar)
                 if image:
-                    yield image, f"✅ **সফল!** {api_name} থেকে অটোমেটিকভাবে ছবি তৈরি হয়েছে।"
+                    yield image, f"✅ **সফল!** অটোমেটিক {api_name} থেকে ছবি তৈরি হয়েছে।"
                     return 
             except Exception as e:
-                yield None, f"❌ **{api_name}** ফেইল করেছে। অটোমেটিক পরেরটিতে যাচ্ছে..."
+                yield None, f"❌ **{api_name}** ব্যস্ত বা লিমিট শেষ। পরেরটিতে যাচ্ছে..."
                 continue
-        yield None, "🚫 **দুঃখিত! সবগুলো সার্ভার ব্যস্ত বা লিমিট শেষ।**"
+        yield None, "🚫 **দুঃখিত! এই মোডের সবগুলো সার্ভার বর্তমানে ব্যস্ত।**"
 
-    # ম্যানুয়াল মোড
+    # লজিক ২: ম্যানুয়ালি নির্দিষ্ট API সিলেক্ট করলে
     else:
-        yield None, f"⏳ শুধুমাত্র **{selected_api}**-এর সাথে কানেক্ট করা হচ্ছে..."
-        api_func = API_PIPELINE[selected_api]
+        yield None, f"⏳ শুধুমাত্র **{selected_api}**-এ কল করা হচ্ছে..."
+        api_func = ALL_APIS[selected_api]
         try:
             image = api_func(enhanced_prompt, w, h, ar)
-            if image:
-                yield image, f"✅ **সফল!** আপনি ম্যানুয়ালি {selected_api} থেকে ছবি বানিয়েছেন।"
-            else:
-                yield None, f"❌ **দুঃখিত!** {selected_api} বর্তমানে ব্যস্ত বা ডাউন। অন্য একটি সিলেক্ট করুন।"
+            if image: yield image, f"✅ **সফল!** আপনি ম্যানুয়ালি {selected_api} ব্যবহার করেছেন।"
+            else: yield None, f"❌ **দুঃখিত!** {selected_api} বর্তমানে ডাউন।"
         except Exception as e:
-            yield None, f"❌ **দুঃখিত!** {selected_api} এর সার্ভার ফুল অথবা লিমিট শেষ। অন্য একটি সিলেক্ট করুন।"
+            yield None, f"❌ **দুঃখিত!** {selected_api} এর সার্ভার ফুল অথবা লিমিট শেষ। অন্য একটি ট্রাই করুন।"
 
 # ==========================================
 # 6. Gradio Web UI
 # ==========================================
-api_choices = ["অ��োমেটিক (Auto Fallback)"] + list(API_PIPELINE.keys())
+api_choices = ["অটোমেটিক (Auto Fallback)"] + list(ALL_APIS.keys())
 
 with gr.Blocks(theme=gr.themes.Base()) as demo:
-    gr.Markdown("<h1 style='text-align: center;'>🎨 Pro AI Image Generator (18 Servers)</h1>")
-    gr.Markdown("<p style='text-align: center;'>অটোমেটিক মোড ব্যবহার করুন অথবা নির্দিষ্ট সার্ভার সিলেক্ট করুন।</p>")
-
+    gr.Markdown("<h1 style='text-align: center;'>🎨 Pro AI Image Generator (28 Servers)</h1>")
+    
     with gr.Row():
         with gr.Column(scale=1):
             prompt_input = gr.Textbox(label="ইমেজের বর্ণনা লিখুন (Prompt)", placeholder="A highly detailed futuristic city...", lines=3)
@@ -255,21 +220,21 @@ with gr.Blocks(theme=gr.themes.Base()) as demo:
             with gr.Row():
                 style_input = gr.Dropdown(
                     choices=["ফটোরিয়ালিস্টিক (Photorealistic)", "এনিমে (Anime)", "থ্রিডি আর্ট (3D)", "ডিফল্ট (Default)"],
-                    label="ছবির স্টাইল (Style)",
-                    value="ফটোরিয়ালিস্টিক (Photorealistic)"
+                    label="ছবির স্টাইল (Style)", value="ফটোরিয়ালিস্টিক (Photorealistic)"
                 )
-                
                 size_input = gr.Dropdown(
                     choices=["স্কয়ার (1:1) - Instagram", "ল্যান্ডস্কেপ (16:9) - YouTube", "পোর্ট্রেট (9:16) - TikTok"],
-                    label="ছবির সাইজ (Size)",
-                    value="স্কয়ার (1:1) - Instagram"
+                    label="ছবির সাইজ (Size)", value="স্কয়ার (1:1) - Instagram"
                 )
             
+            # নতুন Speed Mode অপশন
+            speed_mode = gr.Radio(
+                choices=["Fast (ফাস্ট)", "Slow (স্লো) - High Quality"],
+                label="স্পিড মোড (অটোমেটিকের জন্য)", value="Fast (ফাস্ট)"
+            )
+            
             api_selector = gr.Dropdown(
-                choices=api_choices,
-                label="সার্ভার / API সিলেক্ট করুন",
-                value="অটোমেটিক (Auto Fallback)",
-                interactive=True
+                choices=api_choices, label="সার্ভার ম্যানুয়ালি সিলেক্ট করুন", value="অটোমেটিক (Auto Fallback)", interactive=True
             )
             
             generate_btn = gr.Button("Generate Image", variant="primary")
@@ -280,7 +245,7 @@ with gr.Blocks(theme=gr.themes.Base()) as demo:
 
     generate_btn.click(
         fn=generate_image_logic,
-        inputs=[prompt_input, style_input, size_input, api_selector],
+        inputs=[prompt_input, style_input, size_input, speed_mode, api_selector],
         outputs=[image_output, log_box]
     )