Update app.py

app.py

@@ -1,54 +1,187 @@
-import gradio as gr
+from dataclasses import dataclass
+from typing import List, Tuple, Dict
 import os
-import asyncio
-from conver import ConversationConfig, URLToAudioConverter
-from dotenv import load_dotenv
+import re
+import httpx
+import json
+from openai import OpenAI
+import edge_tts
+import tempfile
+from pydub import AudioSegment
+import base64
+from pathlib import Path
+import shutil  # Importamos shutil para manejo de directorios
 
-load_dotenv()
+@dataclass
+class ConversationConfig:
+    max_words: int = 3000
+    prefix_url: str = "https://r.jina.ai/"
+    model_name: str = "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo"
 
-async def synthesize(article_url, text_input, language="en"):
-    if not article_url and not text_input:
-        return "Error: Ingresa una URL o texto", None
+class URLToAudioConverter:
+    def __init__(self, config: ConversationConfig, llm_api_key: str):
+        self.config = config
+        self.llm_client = OpenAI(api_key=llm_api_key, base_url="https://api.together.xyz/v1")
+        self.llm_out = None
 
-    try:
-        config = ConversationConfig()
-        converter = URLToAudioConverter(config, llm_api_key=os.environ.get("TOGETHER_API_KEY"))
+    def fetch_text(self, url: str) -> str:
+        if not url:
+            raise ValueError("URL cannot be empty")
+
+        full_url = f"{self.config.prefix_url}{url}"
+        try:
+            response = httpx.get(full_url, timeout=60.0)
+            response.raise_for_status()
+            return response.text
+        except httpx.HTTPError as e:
+            raise RuntimeError(f"Failed to fetch URL: {e}")
+
+    def extract_conversation(self, text: str) -> Dict:
+        if not text:
+            raise ValueError("Input text cannot be empty")
+
+        try:
+            # Prompt mejorado para obtener JSON consistente
+            prompt = (
+                f"{text}\nConvert the provided text into a short informative podcast conversation "
+                f"between two experts. Return ONLY a JSON object with the following structure:\n"
+                '{"conversation": [{"speaker": "Speaker1", "text": "..."}, {"speaker": "Speaker2", "text": "..."}]}'
+            )
+            
+            chat_completion = self.llm_client.chat.completions.create(
+                messages=[{"role": "user", "content": prompt}],
+                model=self.config.model_name,
+                response_format={"type": "json_object"}  # Fuerza formato JSON
+            )
+            
+            # Extracción robusta de JSON
+            response_content = chat_completion.choices[0].message.content
+            json_str = response_content.strip()
+            
+            # Limpieza de texto alrededor del JSON
+            if not json_str.startswith('{'):
+                start = json_str.find('{')
+                if start != -1:
+                    json_str = json_str[start:]
+            
+            if not json_str.endswith('}'):
+                end = json_str.rfind('}')
+                if end != -1:
+                    json_str = json_str[:end+1]
+            
+            return json.loads(json_str)
+        except Exception as e:
+            # Debug: Imprime la respuesta del modelo para diagnóstico
+            print(f"Error en extract_conversation: {str(e)}")
+            print(f"Respuesta del modelo: {response_content}")
+            raise RuntimeError(f"Failed to extract conversation: {str(e)}")
+
+    async def text_to_speech(self, conversation_json: Dict, voice_1: str, voice_2: str) -> Tuple[List[str], str]:
+        output_dir = Path(self._create_output_directory())
+        filenames = []
         
-        # Voces humanizadas
-        voices = {
-            "en": ("en-US-AvaMultilingualNeural", "en-US-AndrewMultilingualNeural"),
-            "es": ("es-ES-AlvaroNeural", "es-ES-ElviraNeural")
-        }
-        voice1, voice2 = voices.get(language, voices["en"])
-
-        if text_input:
-            output_file, conversation = await converter.text_to_audio(text_input, voice1, voice2)
-        else:
-            output_file, conversation = await converter.url_to_audio(article_url, voice1, voice2)
+        try:
+            for i, turn in enumerate(conversation_json["conversation"]):
+                filename = output_dir / f"output_{i}.mp3"
+                voice = voice_1 if i % 2 == 0 else voice_2
+
+                tmp_path, error = await self._generate_audio(turn["text"], voice)
+                if error:
+                    raise RuntimeError(f"Text-to-speech failed: {error}")
+
+                os.rename(tmp_path, filename)
+                filenames.append(str(filename))
+
+            return filenames, str(output_dir)
+        except Exception as e:
+            raise RuntimeError(f"Failed to convert text to speech: {e}")
+
+    async def _generate_audio(self, text: str, voice: str, rate: int = 0, pitch: int = 0) -> Tuple[str, str]:
+        if not text.strip():
+            return None, "Text cannot be empty"
+        if not voice:
+            return None, "Voice cannot be empty"
+
+        voice_short_name = voice.split(" - ")[0]
+        rate_str = f"{rate:+d}%"
+        pitch_str = f"{pitch:+d}Hz"
+        communicate = edge_tts.Communicate(text, voice_short_name, rate=rate_str, pitch=pitch_str)
+
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
+            tmp_path = tmp_file.name
+            await communicate.save(tmp_path)
+
+        return tmp_path, None
+
+    def _create_output_directory(self) -> str:
+        random_bytes = os.urandom(8)
+        folder_name = base64.urlsafe_b64encode(random_bytes).decode("utf-8")
+        os.makedirs(folder_name, exist_ok=True)
+        return folder_name
+
+    def combine_audio_files(self, filenames: List[str], output_file: str) -> None:
+        if not filenames:
+            raise ValueError("No input files provided")
+
+        try:
+            combined = AudioSegment.empty()
+            for filename in filenames:
+                audio_segment = AudioSegment.from_file(filename, format="mp3")
+                combined += audio_segment
+
+            combined.export(output_file, format="mp3")
+
+            # Limpieza mejorada y robusta
+            dir_path = os.path.dirname(filenames[0])
             
-        return conversation, output_file
-    except Exception as e:
-        return f"Error: {str(e)}", None
-
-def synthesize_sync(article_url, text_input, language):
-    return asyncio.run(synthesize(article_url, text_input, language))
-
-with gr.Blocks(theme='gstaff/sketch') as demo:
-    gr.Markdown("# 🎙 Podcast Converter (Human Voices)")
-    with gr.Group():
-        text_url = gr.Textbox(label="URL (opcional)", placeholder="https://...")
-        text_input = gr.Textbox(label="O texto directo", lines=5)
-        language = gr.Dropdown(["en", "es"], label="Idioma", value="en")
-        btn = gr.Button("Generar Podcast", variant="primary")
-    
-    with gr.Row():
-        conv_display = gr.Textbox(label="Conversación", interactive=False, lines=10)
-        aud = gr.Audio(label="Audio Generado", interactive=False)
-    
-    btn.click(
-        synthesize_sync,
-        inputs=[text_url, text_input, language],
-        outputs=[conv_display, aud]
-    )
-
-demo.launch()
+            # Eliminar todos los archivos en el directorio
+            for file in os.listdir(dir_path):
+                file_path = os.path.join(dir_path, file)
+                if os.path.isfile(file_path):
+                    try:
+                        os.remove(file_path)
+                    except Exception as e:
+                        print(f"Warning: Could not remove file {file_path}: {str(e)}")
+            
+            # Intentar eliminar el directorio (no crítico si falla)
+            try:
+                os.rmdir(dir_path)
+            except OSError as e:
+                print(f"Info: Could not remove directory {dir_path}: {str(e)}")
+                # No es crítico, el espacio puede continuar
+                
+        except Exception as e:
+            raise RuntimeError(f"Failed to combine audio files: {e}")
+
+    async def url_to_audio(self, url: str, voice_1: str, voice_2: str) -> Tuple[str, str]:
+        text = self.fetch_text(url)
+
+        words = text.split()
+        if len(words) > self.config.max_words:
+            text = " ".join(words[:self.config.max_words])
+
+        conversation_json = self.extract_conversation(text)
+        conversation_text = "\n".join(
+            f"{turn['speaker']}: {turn['text']}" for turn in conversation_json["conversation"]
+        )
+        self.llm_out = conversation_json
+        audio_files, folder_name = await self.text_to_speech(
+            conversation_json, voice_1, voice_2
+        )
+
+        final_output = os.path.join(folder_name, "combined_output.mp3")
+        self.combine_audio_files(audio_files, final_output)
+        return final_output, conversation_text
+
+    async def text_to_audio(self, text: str, voice_1: str, voice_2: str) -> Tuple[str, str]:
+        """Método para procesar texto directo"""
+        conversation_json = self.extract_conversation(text)
+        conversation_text = "\n".join(
+            f"{turn['speaker']}: {turn['text']}" for turn in conversation_json["conversation"]
+        )
+        audio_files, folder_name = await self.text_to_speech(
+            conversation_json, voice_1, voice_2
+        )
+        final_output = os.path.join(folder_name, "combined_output.mp3")
+        self.combine_audio_files(audio_files, final_output)
+        return final_output, conversation_text