Update app.py

app.py

@@ -1,7 +1,5 @@
 import gradio as gr
 from pydub import AudioSegment
-from google import genai
-from google.genai import types
 import json
 import uuid
 import edge_tts
@@ -11,11 +9,23 @@ import os
 import time
 import mimetypes
 from typing import List, Dict
+from transformers import AutoTokenizer, AutoModelForCausalLM  # Replaced gemini
+import torch  # Replaced gemini
 
 # Constants
 MAX_FILE_SIZE_MB = 20
 MAX_FILE_SIZE_BYTES = MAX_FILE_SIZE_MB * 1024 * 1024  # Convert MB to bytes
 
+# Model setup
+MODEL_ID = "tabularisai/german-gemma-3-1b-it"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto",
+).eval()
+
+
 class PodcastGenerator:
     def __init__(self):
         pass
@@ -26,575 +36,112 @@ class PodcastGenerator:
     "topic": "AGI",
     "podcast": [
         {
-            "speaker": 2,
-            "line": "So, AGI, huh? Seems like everyone's talking about it these days."
-        },
-        {
-            "speaker": 1,
-            "line": "Yeah, it's definitely having a moment, isn't it?"
-        },
-        {
-            "speaker": 2,
-            "line": "It is and for good reason, right? I mean, you've been digging into this stuff, listening to the podcasts and everything. What really stood out to you? What got you hooked?"
-        },
-        {
-            "speaker": 1,
-            "line": "Honestly, it's the sheer scale of what AGI could do. We're talking about potentially reshaping well everything."
-        },
-        {
-            "speaker": 2,
-            "line": "No kidding, but let's be real. Sometimes it feels like every other headline is either hyping AGI up as this technological utopia or painting it as our inevitable robot overlords."
-        },
-        {
-            "speaker": 1,
-            "line": "It's easy to get lost in the noise, for sure."
-        },
-        {
-            "speaker": 2,
-            "line": "Exactly. So how about we try to cut through some of that, shall we?"
-        },
-        {
-            "speaker": 1,
-            "line": "Sounds like a plan."
-        },
-        {
-            "speaker": 2,
-            "line": "Okay, so first things first, AGI, what is it really? And I don't just mean some dictionary definition, we're talking about something way bigger than just a super smart computer, right?"
-        },
-        {
-            "speaker": 1,
-            "line": "Right, it's not just about more processing power or better algorithms, it's about a fundamental shift in how we think about intelligence itself."
-        },
-        {
-            "speaker": 2,
-            "line": "So like, instead of programming a machine for a specific task, we're talking about creating something that can learn and adapt like we do."
-        },
-        {
-            "speaker": 1,
-            "line": "Exactly, think of it this way: Right now, we've got AI that can beat a grandmaster at chess but ask that same AI to, say, write a poem or compose a symphony. No chance."
-        },
-        {
-            "speaker": 2,
-            "line": "Okay, I see. So, AGI is about bridging that gap, creating something that can move between those different realms of knowledge seamlessly."
-        },
-        {
-            "speaker": 1,
-            "line": "Precisely. It's about replicating that uniquely human ability to learn something new and apply that knowledge in completely different contexts and that's a tall order, let me tell you."
-        },
-        {
-            "speaker": 2,
-            "line": "I bet. I mean, think about how much we still don't even understand about our own brains."
-        },
-        {
-            "speaker": 1,
-            "line": "That's exactly it. We're essentially trying to reverse-engineer something we don't fully comprehend."
-        },
-        {
-            "speaker": 2,
-            "line": "And how are researchers even approaching that? What are some of the big ideas out there?"
-        },
-        {
-            "speaker": 1,
-            "line": "Well, there are a few different schools of thought. One is this idea of neuromorphic computing where they're literally trying to build computer chips that mimic the structure and function of the human brain."
-        },
-        {
-            "speaker": 2,
-            "line": "Wow, so like actually replicating the physical architecture of the brain. That's wild."
-        },
-        {
-            "speaker": 1,
-            "line": "It's pretty mind-blowing stuff and then you've got folks working on something called whole brain emulation."
-        },
-        {
-            "speaker": 2,
-            "line": "Okay, and what's that all about?"
-        },
-        {
-            "speaker": 1,
-            "line": "The basic idea there is to create a complete digital copy of a human brain down to the last neuron and synapse and run it on a sufficiently powerful computer simulation."
-        },
-        {
-            "speaker": 2,
-            "line": "Hold on, a digital copy of an entire brain, that sounds like something straight out of science fiction."
-        },
-        {
-            "speaker": 1,
-            "line": "It does, doesn't it? But it gives you an idea of the kind of ambition we're talking about here and the truth is we're still a long way off from truly achieving AGI, no matter which approach you look at."
-        },
-        {
-            "speaker": 2,
-            "line": "That makes sense but it's still exciting to think about the possibilities, even if they're a ways off."
-        },
-        {
-            "speaker": 1,
-            "line": "Absolutely and those possibilities are what really get people fired up about AGI, right? Yeah."
-        },
-        {
-            "speaker": 2,
-            "line": "For sure. In fact, I remember you mentioning something in that podcast about AGI's potential to revolutionize scientific research. Something about supercharging breakthroughs."
-        },
-        {
-            "speaker": 1,
-            "line": "Oh, absolutely. Imagine an AI that doesn't just crunch numbers but actually understands scientific data the way a human researcher does. We're talking about potential breakthroughs in everything from medicine and healthcare to material science and climate change."
-        },
-        {
-            "speaker": 2,
-            "line": "It's like giving scientists this incredibly powerful new tool to tackle some of the biggest challenges we face."
-        },
-        {
-            "speaker": 1,
-            "line": "Exactly, it could be a total game changer."
-        },
-        {
-            "speaker": 2,
-            "line": "Okay, but let's be real, every coin has two sides. What about the potential downsides of AGI? Because it can't all be sunshine and roses, right?"
-        },
-        {
-            "speaker": 1,
-            "line": "Right, there are definitely valid concerns. Probably the biggest one is the impact on the job market. As AGI gets more sophisticated, there's a real chance it could automate a lot of jobs that are currently done by humans."
-        },
-        {
-            "speaker": 2,
-            "line": "So we're not just talking about robots taking over factories but potentially things like, what, legal work, analysis, even creative fields?"
-        },
-        {
-            "speaker": 1,
-            "line": "Potentially, yes. And that raises a whole host of questions about what happens to those workers, how we retrain them, how we ensure that the benefits of AGI are shared equitably."
-        },
-        {
-            "speaker": 2,
-            "line": "Right, because it's not just about the technology itself, but how we choose to integrate it into society."
-        },
-        {
-            "speaker": 1,
-            "line": "Absolutely. We need to be having these conversations now about ethics, about regulation, about how to make sure AGI is developed and deployed responsibly."
-        },
-        {
-            "speaker": 2,
-            "line": "So it's less about preventing some kind of sci-fi robot apocalypse and more about making sure we're steering this technology in the right direction from the get-go."
-        },
-        {
-            "speaker": 1,
-            "line": "Exactly, AGI has the potential to be incredibly beneficial, but it's not going to magically solve all our problems. It's on us to make sure we're using it for good."
-        },
-        {
-            "speaker": 2,
-            "line": "It's like you said earlier, it's about shaping the future of intelligence."
-        },
-        {
-            "speaker": 1,
-            "line": "I like that. It really is."
+            "speaker": "Alex",
+            "text": "Hallo und willkommen zur heutigen Folge über AGI."
         },
         {
-            "speaker": 2,
-            "line": "And honestly, that's a responsibility that extends beyond just the researchers and the policymakers."
-        },
-        {
-            "speaker": 1,
-            "line": "100%"
-        },
-        {
-            "speaker": 2,
-            "line": "So to everyone listening out there I'll leave you with this. As AGI continues to develop, what role do you want to play in shaping its future?"
-        },
-        {
-            "speaker": 1,
-            "line": "That's a question worth pondering."
-        },
-        {
-            "speaker": 2,
-            "line": "It certainly is and on that note, we'll wrap up this deep dive. Thanks for listening, everyone."
-        },
-        {
-            "speaker": 1,
-            "line": "Peace."
+            "speaker": "Ben",
+            "text": "Danke Alex! Wir sprechen heute über Artificial General Intelligence und ihre möglichen Auswirkungen."
         }
     ]
 }
-        """
-
-        if language == "Auto Detect":
-            language_instruction = "- The podcast MUST be in the same language as the user input."
-        else:
-            language_instruction = f"- The podcast MUST be in {language} language"
-
-        system_prompt = f"""
-You are a professional podcast generator. Your task is to generate a professional podcast script based on the user input.
-{language_instruction}
-- The podcast should have 2 speakers.
-- The podcast should be long.
-- Do not use names for the speakers.
-- The podcast should be interesting, lively, and engaging, and hook the listener from the start.
-- The input text might be disorganized or unformatted, originating from sources like PDFs or text files. Ignore any formatting inconsistencies or irrelevant details; your task is to distill the essential points, identify key definitions, and highlight intriguing facts that would be suitable for discussion in a podcast.
-- The script must be in JSON format.
-Follow this example structure:
-{example}
 """
-        user_prompt = ""
-        if prompt and file_obj:
-            user_prompt = f"Please generate a podcast script based on the uploaded file following user input:\n{prompt}"
-        elif prompt:
-            user_prompt = f"Please generate a podcast script based on the following user input:\n{prompt}"
-        else:
-            user_prompt = "Please generate a podcast script based on the uploaded file."
-
-        messages = []
-        
-        # If file is provided, add it to the messages
-        if file_obj:
-            file_data = await self._read_file_bytes(file_obj)
-            mime_type = self._get_mime_type(file_obj.name)
-            
-            messages.append(
-                types.Content(
-                    role="user",
-                    parts=[
-                        types.Part.from_bytes(
-                            data=file_data,
-                            mime_type=mime_type,
-                        )
-                    ],
-                )
-            )
-        
-        # Add text prompt
-        messages.append(
-            types.Content(
-                role="user",
-                parts=[
-                    types.Part.from_text(text=user_prompt)
-                ],
-            )
+        full_prompt = (
+            f"Lies den Inhalt und fasse ihn in einem kurzen Podcast-Drehbuch in folgender JSON-Struktur zusammen:\n"
+            f"{example}\n\n"
+            f"Inhalt:\n{prompt}"
         )
 
-        client = genai.Client(api_key=api_key)
-
-        safety_settings = [
-            {
-                "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
-                "threshold": "BLOCK_NONE"
-            },
-            {
-                "category": "HARM_CATEGORY_HARASSMENT",
-                "threshold": "BLOCK_NONE"
-            },
-            {
-                "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
-                "threshold": "BLOCK_NONE"
-            },
-            {
-                "category": "HARM_CATEGORY_HATE_SPEECH",
-                "threshold": "BLOCK_NONE"
-            }
-        ]
+        inputs = tokenizer(full_prompt, return_tensors="pt").to(model.device)
+        output = model.generate(**inputs, max_new_tokens=1024, do_sample=True)
+        script_text = tokenizer.decode(output[0], skip_special_tokens=True)
 
         try:
-            if progress:
-                progress(0.3, "Generating podcast script...")
-                
-            # Add timeout to the API call
-            response = await asyncio.wait_for(
-                client.aio.models.generate_content(
-                    model="gemini-2.0-flash",
-                    contents=messages,
-                    config=types.GenerateContentConfig(
-                        temperature=1,
-                        response_mime_type="application/json",
-                        safety_settings=[
-                            types.SafetySetting(
-                                category=safety_setting["category"],
-                                threshold=safety_setting["threshold"]
-                            ) for safety_setting in safety_settings
-                        ],
-                        system_instruction=system_prompt
-                    )
-                ),
-                timeout=60  # 60 seconds timeout
-            )
-        except asyncio.TimeoutError:
-            raise Exception("The script generation request timed out. Please try again later.")
-        except Exception as e:
-            if "API key not valid" in str(e):
-                raise Exception("Invalid API key. Please provide a valid Gemini API key.")
-            elif "rate limit" in str(e).lower():
-                raise Exception("Rate limit exceeded for the API key. Please try again later or provide your own Gemini API key.")
-            else:
-                raise Exception(f"Failed to generate podcast script: {e}")
+            podcast_json = json.loads(script_text)
+        except json.JSONDecodeError:
+            return {"error": "Fehler beim Parsen der Antwort: Kein gültiges JSON erkannt."}
 
-        print(f"Generated podcast script:\n{response.text}")
-        
-        if progress:
-            progress(0.4, "Script generated successfully!")
-            
-        return json.loads(response.text)
-    
-    async def _read_file_bytes(self, file_obj) -> bytes:
-        """Read file bytes from a file object"""
-        # Check file size before reading
-        if hasattr(file_obj, 'size'):
-            file_size = file_obj.size
-        else:
-            file_size = os.path.getsize(file_obj.name)
-            
-        if file_size > MAX_FILE_SIZE_BYTES:
-            raise Exception(f"File size exceeds the {MAX_FILE_SIZE_MB}MB limit. Please upload a smaller file.")
-            
-        if hasattr(file_obj, 'read'):
-            return file_obj.read()
-        else:
-            async with aiofiles.open(file_obj.name, 'rb') as f:
-                return await f.read()
-    
-    def _get_mime_type(self, filename: str) -> str:
-        """Determine MIME type based on file extension"""
-        ext = os.path.splitext(filename)[1].lower()
-        if ext == '.pdf':
-            return "application/pdf"
-        elif ext == '.txt':
-            return "text/plain"
-        else:
-            # Fallback to the default mime type detector
-            mime_type, _ = mimetypes.guess_type(filename)
-            return mime_type or "application/octet-stream"
+        return podcast_json
 
-    async def tts_generate(self, text: str, speaker: int, speaker1: str, speaker2: str) -> str:
-        voice = speaker1 if speaker == 1 else speaker2
-        speech = edge_tts.Communicate(text, voice)
-        
-        temp_filename = f"temp_{uuid.uuid4()}.wav"
-        try:
-            # Add timeout to TTS generation
-            await asyncio.wait_for(speech.save(temp_filename), timeout=30)  # 30 seconds timeout
-            return temp_filename
-        except asyncio.TimeoutError:
-            if os.path.exists(temp_filename):
-                os.remove(temp_filename)
-            raise Exception("Text-to-speech generation timed out. Please try with a shorter text.")
-        except Exception as e:
-            if os.path.exists(temp_filename):
-                os.remove(temp_filename)
-            raise e
+    async def text_to_audio(self, segments: List[Dict], language: str, progress=None) -> str:
+        audio_segments = []
+        temp_files = []
 
-    async def combine_audio_files(self, audio_files: List[str], progress=None) -> str:
-        if progress:
-            progress(0.9, "Combining audio files...")
-            
-        combined_audio = AudioSegment.empty()
-        for audio_file in audio_files:
-            combined_audio += AudioSegment.from_file(audio_file)
-            os.remove(audio_file)  # Clean up temporary files
+        for idx, segment in enumerate(segments):
+            speaker = segment.get("speaker", "Sprecher")
+            text = segment.get("text", "")
+            tts_text = f"{speaker} sagt: {text}"
 
-        output_filename = f"output_{uuid.uuid4()}.wav"
-        combined_audio.export(output_filename, format="wav")
-        
-        if progress:
-            progress(1.0, "Podcast generated successfully!")
-            
-        return output_filename
+            filename = f"temp_{uuid.uuid4()}.mp3"
+            communicate = edge_tts.Communicate(tts_text, "de-DE-KasperNeural")
+            await communicate.save(filename)
+
+            audio = AudioSegment.from_file(filename, format="mp3")
+            audio_segments.append(audio)
+            temp_files.append(filename)
 
-    async def generate_podcast(self, input_text: str, language: str, speaker1: str, speaker2: str, api_key: str, file_obj=None, progress=None) -> str:
-        try:
             if progress:
-                progress(0.1, "Starting podcast generation...")
-                
-            # Set overall timeout for the entire process
-            return await asyncio.wait_for(
-                self._generate_podcast_internal(input_text, language, speaker1, speaker2, api_key, file_obj, progress),
-                timeout=600  # 10 minutes total timeout
-            )
-        except asyncio.TimeoutError:
-            raise Exception("The podcast generation process timed out. Please try with shorter text or try again later.")
-        except Exception as e:
-            raise Exception(f"Error generating podcast: {str(e)}")
-    
-    async def _generate_podcast_internal(self, input_text: str, language: str, speaker1: str, speaker2: str, api_key: str, file_obj=None, progress=None) -> str:
-        if progress:
-            progress(0.2, "Generating podcast script...")
-            
-        podcast_json = await self.generate_script(input_text, language, api_key, file_obj, progress)
-        
-        if progress:
-            progress(0.5, "Converting text to speech...")
-        
-        # Process TTS in batches for concurrent processing
-        audio_files = []
-        total_lines = len(podcast_json['podcast'])
-        
-        # Define batch size to control concurrency
-        batch_size = 10  # Adjust based on system resources
-        
-        # Process in batches
-        for batch_start in range(0, total_lines, batch_size):
-            batch_end = min(batch_start + batch_size, total_lines)
-            batch = podcast_json['podcast'][batch_start:batch_end]
-            
-            # Create tasks for concurrent processing
-            tts_tasks = []
-            for item in batch:
-                tts_task = self.tts_generate(item['line'], item['speaker'], speaker1, speaker2)
-                tts_tasks.append(tts_task)
-            
-            try:
-                # Process batch concurrently
-                batch_results = await asyncio.gather(*tts_tasks, return_exceptions=True)
-                
-                # Check for exceptions and handle results
-                for i, result in enumerate(batch_results):
-                    if isinstance(result, Exception):
-                        # Clean up any files already created
-                        for file in audio_files:
-                            if os.path.exists(file):
-                                os.remove(file)
-                        raise Exception(f"Error generating speech: {str(result)}")
-                    else:
-                        audio_files.append(result)
-                        
-                # Update progress
-                if progress:
-                    current_progress = 0.5 + (0.4 * (batch_end / total_lines))
-                    progress(current_progress, f"Processed {batch_end}/{total_lines} speech segments...")
-            
-            except Exception as e:
-                # Clean up any files already created
-                for file in audio_files:
-                    if os.path.exists(file):
-                        os.remove(file)
-                raise Exception(f"Error in batch TTS generation: {str(e)}")
-        
-        combined_audio = await self.combine_audio_files(audio_files, progress)
-        return combined_audio
+                progress((idx + 1) / len(segments), f"Segment {idx+1} erstellt...")
 
-async def process_input(input_text: str, input_file, language: str, speaker1: str, speaker2: str, api_key: str = "", progress=None) -> str:
-    start_time = time.time()
+        podcast = sum(audio_segments[1:], audio_segments[0])
+        final_filename = f"podcast_{uuid.uuid4()}.mp3"
+        podcast.export(final_filename, format="mp3")
 
-    voice_names = {
-        "Andrew - English (United States)": "en-US-AndrewMultilingualNeural",
-        "Ava - English (United States)": "en-US-AvaMultilingualNeural",
-        "Brian - English (United States)": "en-US-BrianMultilingualNeural",
-        "Emma - English (United States)": "en-US-EmmaMultilingualNeural",
-        "Florian - German (Germany)": "de-DE-FlorianMultilingualNeural",
-        "Seraphina - German (Germany)": "de-DE-SeraphinaMultilingualNeural",
-        "Remy - French (France)": "fr-FR-RemyMultilingualNeural",
-        "Vivienne - French (France)": "fr-FR-VivienneMultilingualNeural"
-    }
+        for f in temp_files:
+            os.remove(f)
 
-    speaker1 = voice_names[speaker1]
-    speaker2 = voice_names[speaker2]
+        return final_filename
 
-    try:
-        if progress:
-            progress(0.05, "Processing input...")
 
-        if not api_key:
-            api_key = os.getenv("GENAI_API_KEY")
-            if not api_key:
-                raise Exception("No API key provided. Please provide a Gemini API key.")
+async def generate_podcast(prompt, language, api_key, file, progress=gr.Progress(track_tqdm=True)):
+    gen = PodcastGenerator()
 
-        podcast_generator = PodcastGenerator()
-        podcast = await podcast_generator.generate_podcast(input_text, language, speaker1, speaker2, api_key, input_file, progress)
+    text = ""
+    if file is not None:
+        if file.size > MAX_FILE_SIZE_BYTES:
+            return "Datei zu groß. Maximal erlaubt sind 20 MB.", None
 
-        end_time = time.time()
-        print(f"Total podcast generation time: {end_time - start_time:.2f} seconds")
-        return podcast
-        
-    except Exception as e:
-        # Ensure we show a user-friendly error
-        error_msg = str(e)
-        if "rate limit" in error_msg.lower():
-            raise Exception("Rate limit exceeded. Please try again later or use your own API key.")
-        elif "timeout" in error_msg.lower():
-            raise Exception("The request timed out. This could be due to server load or the length of your input. Please try again with shorter text.")
-        else:
-            raise Exception(f"Error: {error_msg}")
+        mime_type, _ = mimetypes.guess_type(file.name)
+        if not mime_type or not mime_type.endswith("pdf"):
+            return "Nur PDF-Dateien sind erlaubt.", None
 
-# Gradio UI
-def generate_podcast_gradio(input_text, input_file, language, speaker1, speaker2, api_key, progress=gr.Progress()):
-    # Handle the file if uploaded
-    file_obj = None
-    if input_file is not None:
-        file_obj = input_file
-        
-    # Use the progress function from Gradio
-    def progress_callback(value, text):
-        progress(value, text)
+        async with aiofiles.open(file.name, mode="rb") as f:
+            pdf_data = await f.read()
+            import fitz  # PyMuPDF
+            with fitz.open(stream=pdf_data, filetype="pdf") as doc:
+                text = "\n".join([page.get_text() for page in doc])
+    else:
+        text = prompt
 
-    # Run the async function in the event loop
-    result = asyncio.run(process_input(
-        input_text, 
-        file_obj, 
-        language, 
-        speaker1, 
-        speaker2, 
-        api_key,
-        progress_callback
-    ))
-    
-    return result
+    progress(0.1, "Erzeuge Skript...")
+    script = await gen.generate_script(text, language, api_key, file_obj=file, progress=progress)
 
-def main():
-    # Define language options
-    language_options = [
-        "Auto Detect",
-        "Afrikaans", "Albanian", "Amharic", "Arabic", "Armenian", "Azerbaijani",
-        "Bahasa Indonesian", "Bangla", "Basque", "Bengali", "Bosnian", "Bulgarian",
-        "Burmese", "Catalan", "Chinese Cantonese", "Chinese Mandarin",
-        "Chinese Taiwanese", "Croatian", "Czech", "Danish", "Dutch", "English",
-        "Estonian", "Filipino", "Finnish", "French", "Galician", "Georgian",
-        "German", "Greek", "Hebrew", "Hindi", "Hungarian", "Icelandic", "Irish",
-        "Italian", "Japanese", "Javanese", "Kannada", "Kazakh", "Khmer", "Korean",
-        "Lao", "Latvian", "Lithuanian", "Macedonian", "Malay", "Malayalam",
-        "Maltese", "Mongolian", "Nepali", "Norwegian Bokmål", "Pashto", "Persian",
-        "Polish", "Portuguese", "Romanian", "Russian", "Serbian", "Sinhala",
-        "Slovak", "Slovene", "Somali", "Spanish", "Sundanese", "Swahili",
-        "Swedish", "Tamil", "Telugu", "Thai", "Turkish", "Ukrainian", "Urdu",
-        "Uzbek", "Vietnamese", "Welsh", "Zulu"
-    ]
-    
-    # Define voice options
-    voice_options = [
-        "Andrew - English (United States)",
-        "Ava - English (United States)",
-        "Brian - English (United States)",
-        "Emma - English (United States)",
-        "Florian - German (Germany)",
-        "Seraphina - German (Germany)",
-        "Remy - French (France)",
-        "Vivienne - French (France)"
-    ]
-    
-    # Create Gradio interface
-    with gr.Blocks(title="PodcastGen 🎙️") as demo:
-        gr.Markdown("# PodcastGen 🎙️")
-        gr.Markdown("Generate a 2-speaker podcast from text input or documents!")
-        
-        with gr.Row():
-            with gr.Column(scale=2):
-                input_text = gr.Textbox(label="Input Text", lines=10, placeholder="Enter text for podcast generation...")
-            
-            with gr.Column(scale=1):
-                input_file = gr.File(label="Or Upload a PDF or TXT file", file_types=[".pdf", ".txt"])
-        
-        with gr.Row():
-            with gr.Column():
-                api_key = gr.Textbox(label="Your Gemini API Key (Optional)", placeholder="Enter API key here if you're getting rate limited", type="password")
-                language = gr.Dropdown(label="Language", choices=language_options, value="Auto Detect")
+    if "error" in script:
+        return script["error"], None
 
-            with gr.Column():
-                speaker1 = gr.Dropdown(label="Speaker 1 Voice", choices=voice_options, value="Andrew - English (United States)")
-                speaker2 = gr.Dropdown(label="Speaker 2 Voice", choices=voice_options, value="Ava - English (United States)")
-        
-        generate_btn = gr.Button("Generate Podcast", variant="primary")
-        
-        with gr.Row():
-            output_audio = gr.Audio(label="Generated Podcast", type="filepath", format="wav")
-            
-        generate_btn.click(
-            fn=generate_podcast_gradio,
-            inputs=[input_text, input_file, language, speaker1, speaker2, api_key],
-            outputs=[output_audio]
-        )
-    
-    demo.launch()
+    progress(0.5, "Erzeuge Audio...")
+    podcast_path = await gen.text_to_audio(script["podcast"], language, progress=progress)
+
+    progress(1.0, "Fertig!")
+    return "Podcast erfolgreich erstellt!", podcast_path
+
+
+demo = gr.Interface(
+    fn=generate_podcast,
+    inputs=[
+        gr.Textbox(label="Thema oder Text", lines=5, placeholder="Worum soll es im Podcast gehen?"),
+        gr.Radio(["de"], label="Sprache", value="de"),
+        gr.Textbox(label="API Key (nicht benötigt für dieses Modell)", type="password", placeholder=""),
+        gr.File(label="Optional: PDF-Datei hochladen", file_types=[".pdf"])
+    ],
+    outputs=[
+        gr.Textbox(label="Status"),
+        gr.Audio(label="Erstellter Podcast", type="filepath")
+    ],
+    title="Podcast Generator (German-Gemma)",
+    description="Erstelle Podcasts aus Text oder PDFs mithilfe eines KI-Modells. Nutzt das Modell 'tabularisai/german-gemma-3-1b-it'.",
+    allow_flagging="never"
+)
 
 if __name__ == "__main__":
-    main()
+    demo.launch()