finally

app.py

@@ -3,7 +3,7 @@ import spaces
 import os, torch, io
 import json
 import re
-# os.system("python -m unidic download")
+os.system("python -m unidic download")
 import httpx
 # print("Make sure you've downloaded unidic (python -m unidic download) for this WebUI to work.")
 from melo.api import TTS
@@ -13,18 +13,33 @@ from pydub import AudioSegment
 from gradio_client import Client
 
 client = Client("eswardivi/AIO_Chat")
+
+def validate_url(url):
+    try:
+        response = httpx.get(url, timeout=60.0)
+        response.raise_for_status()
+        return response.text
+    except httpx.RequestError as e:
+        return f"An error occurred while requesting {url}: {str(e)}"
+    except httpx.HTTPStatusError as e:
+        return f"Error response {e.response.status_code} while requesting {url}"
+    except Exception as e:
+        return f"An unexpected error occurred: {str(e)}"
+
 def fetch_text(url):
     print("Entered Webpage Extraction")
     prefix_url = "https://r.jina.ai/"
-    url = prefix_url + url
-    response = httpx.get(url, timeout=120.0)
-    print("Response Received")
-    return response.text
-
-
+    full_url = prefix_url + url
+    return validate_url(full_url)
 @spaces.GPU
-def synthesize(article_url, progress=gr.Progress()):
+def synthesize(article_url,progress_audio=gr.Progress()):
+    if not article_url.startswith("http://") and article_url.startswith("https://"):
+        return "URL must start with 'http://' or 'https://'",None
+
     text = fetch_text(article_url)
+    if "Error" in text:
+        return text, None
+
     device = "cuda" if torch.cuda.is_available() else "cpu"
     
     template = """
@@ -50,38 +65,40 @@ def synthesize(article_url, progress=gr.Progress()):
     else:
         conversation = template
     speed = 1.0
-    models = {
-        "EN": TTS(language="EN", device=device),
-    }
+    models = {"EN": TTS(language="EN", device=device)}
     speakers = ["EN-Default", "EN-US"]
-
     combined_audio = AudioSegment.empty()
-    conversation = json.loads(conversation)
-    for i, turn in enumerate(conversation["conversation"]):
+
+    conversation_dict = json.loads(conversation)
+    for i, turn in enumerate(conversation_dict["conversation"]):
         bio = io.BytesIO()
         text = turn["text"]
         speaker = speakers[i % 2]
         speaker_id = models["EN"].hps.data.spk2id[speaker]
-        models["EN"].tts_to_file(
-            text, speaker_id, bio, speed=speed, pbar=progress.tqdm, format="wav"
-        )
+        models["EN"].tts_to_file(text, speaker_id, bio, speed=1.0, pbar=progress_audio.tqdm, format="wav")
         bio.seek(0)
         audio_segment = AudioSegment.from_file(bio, format="wav")
         combined_audio += audio_segment
-
     final_audio_path = "final.mp3"
     combined_audio.export(final_audio_path, format="mp3")
-    return final_audio_path
+    return conversation, final_audio_path
 
 
 with gr.Blocks() as demo:
-    gr.Markdown("# Not Ready to USE")
-    gr.Markdown("# Turn Any Article into Podcast")
-    gr.Markdown("## Easily convert articles from URLs into listenable audio Podcast.")
+    gr.Markdown("# Turn Any Article into a Podcast")
+    gr.Markdown("## Easily convert articles from URLs into listenable audio podcasts.")
+    gr.Markdown("### Instructions")
+    gr.Markdown("""
+    - **Step 1:** Paste the URL of the article you want to convert into the textbox.
+    - **Step 2:** Click on "Podcastify" to generate the podcast.
+    - **Step 3:** Listen to the podcast or view the conversation.
+    """)
     with gr.Group():
         text = gr.Textbox(label="Article Link")
-    btn = gr.Button("Podcasitfy", variant="primary")
-    aud = gr.Audio(interactive=False)
-    btn.click(synthesize, inputs=[text], outputs=[aud])
+        btn = gr.Button("Podcastify", variant="primary")
+    with gr.Row():
+        conv_display = gr.Textbox(label="Conversation", interactive=False)
+        aud = gr.Audio(interactive=False)
+    btn.click(synthesize, inputs=[text], outputs=[conv_display, aud])
 
 demo.queue(api_open=True, default_concurrency_limit=10).launch(show_api=True,share=True)