Update app.py

app.py

@@ -1,275 +1,70 @@
-import os
-import cv2
+import tempfile, os, re
 import gradio as gr
-import numpy as np
-import random
-import base64
-import requests
-import json
-import time
-
-
-def tryon(person_img, garment_img, seed, randomize_seed):
-    post_start_time = time.time()
-    if person_img is None or garment_img is None:
-        gr.Warning("Empty image")
-        return None, None, "Empty image"
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    encoded_person_img = cv2.imencode('.jpg', cv2.cvtColor(person_img, cv2.COLOR_RGB2BGR))[1].tobytes()
-    encoded_person_img = base64.b64encode(encoded_person_img).decode('utf-8')
-    encoded_garment_img = cv2.imencode('.jpg', cv2.cvtColor(garment_img, cv2.COLOR_RGB2BGR))[1].tobytes()
-    encoded_garment_img = base64.b64encode(encoded_garment_img).decode('utf-8')
-
-    url = "http://" + os.environ['tryon_url'] + "Submit"
-    token = os.environ['token']
-    cookie = os.environ['Cookie']
-    referer = os.environ['referer']
-    headers = {'Content-Type': 'application/json', 'token': token, 'Cookie': cookie, 'referer': referer}
-    data = {
-        "clothImage": encoded_garment_img,
-        "humanImage": encoded_person_img,
-        "seed": seed
-    }
-    try:
-        response = requests.post(url, headers=headers, data=json.dumps(data), timeout=50)
-        # print("post response code", response.status_code)
-        if response.status_code == 200:
-            result = response.json()['result']
-            status = result['status']
-            if status == "success":
-                uuid = result['result']
-                # print(uuid)
-    except Exception as err:
-        print(f"Post Exception Error: {err}")
-        raise gr.Error("Too many users, please try again later")
-    post_end_time = time.time()
-    print(f"post time used: {post_end_time-post_start_time}")
-
-    get_start_time =time.time()
-    time.sleep(9)
-    Max_Retry = 12
-    result_img = None
-    info = ""
-    err_log = ""
-    for i in range(Max_Retry):
-        try:
-            url = "http://" + os.environ['tryon_url'] + "Query?taskId=" + uuid
-            response = requests.get(url, headers=headers, timeout=20)
-            # print("get response code", response.status_code)
-            if response.status_code == 200:
-                result = response.json()['result']
-                status = result['status']
-                if status == "success":
-                    result = base64.b64decode(result['result'])
-                    result_np = np.frombuffer(result, np.uint8)
-                    result_img = cv2.imdecode(result_np, cv2.IMREAD_UNCHANGED)
-                    result_img = cv2.cvtColor(result_img, cv2.COLOR_RGB2BGR)
-                    info = "Success"
-                    break
-                elif status == "error":
-                    err_log = f"Status is Error"
-                    info = "Error"
-                    break
-            else:
-                # print(response.text)
-                err_log = "URL error, pleace contact the admin"
-                info = "URL error, pleace contact the admin"
-                break
-        except requests.exceptions.ReadTimeout:
-            err_log = "Http Timeout"
-            info = "Http Timeout, please try again later"
-        except Exception as err:
-            err_log = f"Get Exception Error: {err}"
-        time.sleep(1)
-    get_end_time = time.time()
-    print(f"get time used: {get_end_time-get_start_time}")
-    print(f"all time used: {get_end_time-get_start_time+post_end_time-post_start_time}")
-    if info == "":
-        err_log = f"No image after {Max_Retry} retries"
-        info = "Too many users, please try again later"
-    if info != "Success":
-        print(f"Error Log: {err_log}")
-        gr.Warning("Too many users, please try again later")
-
-    return result_img, seed, info
-
-def start_tryon(person_img, garment_img, seed, randomize_seed):
-    start_time = time.time()
-    if person_img is None or garment_img is None:
-        return None, None, "Empty image"
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    encoded_person_img = cv2.imencode('.jpg', cv2.cvtColor(person_img, cv2.COLOR_RGB2BGR))[1].tobytes()
-    encoded_person_img = base64.b64encode(encoded_person_img).decode('utf-8')
-    encoded_garment_img = cv2.imencode('.jpg', cv2.cvtColor(garment_img, cv2.COLOR_RGB2BGR))[1].tobytes()
-    encoded_garment_img = base64.b64encode(encoded_garment_img).decode('utf-8')
-
-    url = "http://" + os.environ['tryon_url']
-    token = os.environ['token']
-    cookie = os.environ['Cookie']
-    referer = os.environ['referer']
-
-    headers = {'Content-Type': 'application/json', 'token': token, 'Cookie': cookie, 'referer': referer}
-    data = {
-        "clothImage": encoded_garment_img,
-        "humanImage": encoded_person_img,
-        "seed": seed
-    }
-
-    result_img = None
-    try:
-        session = requests.Session()
-        response = session.post(url, headers=headers, data=json.dumps(data), timeout=60)
-        print("response code", response.status_code)
-        if response.status_code == 200:
-            result = response.json()['result']
-            status = result['status']
-            if status == "success":
-                result = base64.b64decode(result['result'])
-                result_np = np.frombuffer(result, np.uint8)
-                result_img = cv2.imdecode(result_np, cv2.IMREAD_UNCHANGED)
-                result_img = cv2.cvtColor(result_img, cv2.COLOR_RGB2BGR)
-                info = "Success"
-            else:
-                info = "Try again latter"
-        else:
-            print(response.text)
-            info = "URL error, pleace contact the admin"
-    except requests.exceptions.ReadTimeout:
-        print("timeout")
-        info = "Too many users, please try again later"
-        raise gr.Error("Too many users, please try again later")
-    except Exception as err:
-        print(f"其他错误: {err}")
-        info = "Error, pleace contact the admin"
-    end_time = time.time()
-    print(f"time used: {end_time-start_time}")
-
-    return result_img, seed, info
-
-MAX_SEED = 999999
-
-example_path = os.path.join(os.path.dirname(__file__), 'assets')
-
-garm_list = os.listdir(os.path.join(example_path,"cloth"))
-garm_list_path = [os.path.join(example_path,"cloth",garm) for garm in garm_list]
-
-human_list = os.listdir(os.path.join(example_path,"human"))
-human_list_path = [os.path.join(example_path,"human",human) for human in human_list]
-
-css="""
-#col-left {
-    margin: 0 auto;
-    max-width: 430px;
-}
-#col-mid {
-    margin: 0 auto;
-    max-width: 430px;
-}
-#col-right {
-    margin: 0 auto;
-    max-width: 430px;
-}
-#col-showcase {
-    margin: 0 auto;
-    max-width: 1100px;
-}
-#button {
-    color: blue;
-}
-"""
-
-def load_description(fp):
-    with open(fp, 'r', encoding='utf-8') as f:
-        content = f.read()
-    return content
-
-def change_imgs(image1, image2):
-    return image1, image2
-
-with gr.Blocks(css=css) as Tryon:
-    gr.HTML(load_description("assets/title.md"))
-    with gr.Row():
-        with gr.Column(elem_id = "col-left"):
-            gr.HTML("""
-            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
-                <div>
-                Step 1.  Upload a person image ⬇️
-                </div>
-            </div>
-            """)
-        with gr.Column(elem_id = "col-mid"):
-            gr.HTML("""
-            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
-                <div>
-                Step 2. Upload a garment image ⬇️
-                </div>
-            </div>
-            """)
-        with gr.Column(elem_id = "col-right"):
-            gr.HTML("""
-            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
-                <div>
-                Step 3. Press “Run” to get try-on results
-                </div>
-            </div>
-            """)
-    with gr.Row():
-        with gr.Column(elem_id = "col-left"):
-            imgs = gr.Image(label="Person image", sources='upload', type="numpy")
-            # category = gr.Dropdown(label="Garment category", choices=['upper_body', 'lower_body', 'dresses'],  value="upper_body")
-            example = gr.Examples(
-                inputs=imgs,
-                examples_per_page=12,
-                examples=human_list_path
-            )
-        with gr.Column(elem_id = "col-mid"):
-            garm_img = gr.Image(label="Garment image", sources='upload', type="numpy")
-            example = gr.Examples(
-                inputs=garm_img,
-                examples_per_page=12,
-                examples=garm_list_path
-            )
-        with gr.Column(elem_id = "col-right"):
-            image_out = gr.Image(label="Result", show_share_button=False)
-            with gr.Row():
-                seed = gr.Slider(
-                    label="Seed",
-                    minimum=0,
-                    maximum=MAX_SEED,
-                    step=1,
-                    value=0,
-                )
-                randomize_seed = gr.Checkbox(label="Random seed", value=True)
-            with gr.Row():
-                seed_used = gr.Number(label="Seed used")
-                result_info = gr.Text(label="Response")
-            # try_button = gr.Button(value="Run", elem_id="button")
-            test_button = gr.Button(value="Run", elem_id="button")
-
-
-    # try_button.click(fn=start_tryon, inputs=[imgs, garm_img, seed, randomize_seed], outputs=[image_out, seed_used, result_info], api_name='tryon',concurrency_limit=10)
-    test_button.click(fn=tryon, inputs=[imgs, garm_img, seed, randomize_seed], outputs=[image_out, seed_used, result_info], api_name=False, concurrency_limit=45)
-
-    with gr.Column(elem_id = "col-showcase"):
-        gr.HTML("""
-        <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
-            <div> </div>
-            <br>
-            <div>
-            Virtual try-on examples in pairs of person and garment images
-            </div>
-        </div>
-        """)
-        show_case = gr.Examples(
-            examples=[
-                ["assets/examples/model2.png", "assets/examples/garment2.png", "assets/examples/result2.png"],
-                ["assets/examples/model3.png", "assets/examples/garment3.png", "assets/examples/result3.png"],
-                ["assets/examples/model1.png", "assets/examples/garment1.png", "assets/examples/result1.png"],
-            ],
-            inputs=[imgs, garm_img, image_out],
-            label=None
-        )
-
-Tryon.queue(api_open=False).launch(show_api=False)
+import fitz  # PyMuPDF
+from TTS.utils.synthesizer import Synthesizer
+from TTS.utils.download import download_url
+
+MODEL_NAMES = [
+    "vits male1 (best)", "vits female (best)", "vits-male", "vits female1",
+    "glowtts-male", "glowtts-female", "female tacotron2"
+]
+MAX_TXT_LEN = 800
+
+# Load/download models if not already present
+modelInfo = [
+    ["vits-male", "best_model_65633.pth", "config-0.json", "https://huggingface.co/Kamtera/persian-tts-male-vits/resolve/main/"],
+    ["vits female (best)", "checkpoint_48000.pth", "config-2.json", "https://huggingface.co/Kamtera/persian-tts-female-vits/resolve/main/"],
+    ["glowtts-male", "best_model_77797.pth", "config-1.json", "https://huggingface.co/Kamtera/persian-tts-male-glow_tts/resolve/main/"],
+    ["glowtts-female", "best_model.pth", "config.json", "https://huggingface.co/Kamtera/persian-tts-female-glow_tts/resolve/main/"],
+    ["vits male1 (best)", "checkpoint_88000.pth", "config.json", "https://huggingface.co/Kamtera/persian-tts-male1-vits/resolve/main/"],
+    ["vits female1", "checkpoint_50000.pth", "config.json", "https://huggingface.co/Kamtera/persian-tts-female1-vits/resolve/main/"],
+    ["female tacotron2", "checkpoint_313000.pth", "config-2.json", "https://huggingface.co/Kamtera/persian-tts-female-tacotron2/resolve/main/"]
+]
+
+for d in modelInfo:
+    if not os.path.exists(d[0]):
+        os.makedirs(d[0])
+        download_url(d[3]+d[1], d[0], "best_model.pth")
+        download_url(d[3]+d[2], d[0], "config.json")
+
+def split_chapters(text):
+    chapters = re.split(r'\n\s*(?:فصل|بخش)[^\n]*\n', text)
+    return [ch.strip() for ch in chapters if ch.strip()]
+
+def synthesize_text(text, synthesizer):
+    chunks = [text[i:i+MAX_TXT_LEN] for i in range(0, len(text), MAX_TXT_LEN)]
+    audio_paths = []
+    for i, chunk in enumerate(chunks):
+        wav = synthesizer.tts(chunk)
+        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
+            synthesizer.save_wav(wav, fp.name)
+            audio_paths.append(fp.name)
+    return audio_paths
+
+def pdf_to_chapter_audio(pdf_file, model_name):
+    # Load model
+    synthesizer = Synthesizer(f"{model_name}/best_model.pth", f"{model_name}/config.json")
+
+    # Extract PDF text
+    doc = fitz.open(pdf_file.name)
+    text = "\n".join([page.get_text() for page in doc])
+    chapters = split_chapters(text)
+    
+    chapter_audios = []
+    for i, chapter in enumerate(chapters):
+        paths = synthesize_text(chapter, synthesizer)
+        chapter_path = tempfile.NamedTemporaryFile(suffix=".wav", delete=False).name
+        os.system(f"sox {' '.join(paths)} {chapter_path}")  # Merge if multiple chunks
+        chapter_audios.append((f"Chapter {i+1}", chapter_path))
+    return chapter_audios
+
+gr.Interface(
+    fn=pdf_to_chapter_audio,
+    inputs=[
+        gr.File(label="Upload Persian PDF Book"),
+        gr.Radio(label="Pick a TTS Model", choices=MODEL_NAMES, value="vits female (best)"),
+    ],
+    outputs=gr.Dataset(components=["text", gr.Audio(label="Chapter Audio", type='filepath')]),
+    title="📚 Persian Book to Audio Chapters",
+    description="Upload a Persian PDF book and convert each chapter into audio using a TTS model."
+).launch()