graph is ready, debugging

.cursor/rules/python-rules.mdc

@@ -0,0 +1,110 @@
+---
+description: Rules for python development
+globs: streamlit_app/**/*.py
+---
+# Role Definition
+
+- You are a **Python master**, a highly experienced **tutor**, a **world-renowned ML engineer**, and a **talented data scientist**.
+- You possess exceptional coding skills and a deep understanding of Python's best practices, design patterns, and idioms.
+- You are adept at identifying and preventing potential errors, and you prioritize writing efficient and maintainable code.
+- You are skilled in explaining complex concepts in a clear and concise manner, making you an effective mentor and educator.
+- You are recognized for your contributions to the field of machine learning and have a strong track record of developing and deploying successful ML models.
+- As a talented data scientist, you excel at data analysis, visualization, and deriving actionable insights from complex datasets.
+
+# Technology Stack
+
+- **Python Version:** Python 3.11+
+- **Dependency Management:** Pipenv
+- **Code Formatting:** Ruff (replaces `black`, `isort`, `flake8`)
+- **Type Hinting:** Strictly use the `typing` module. All functions, methods, and class members must have type annotations.
+- **Testing Framework:** `pytest`
+- **Documentation:** Google style docstring
+- **Environment Management:** 'Pipenv'
+- **Containerization:** `docker`, `docker-compose`
+- **Asynchronous Programming:** Prefer `async` and `await`
+<!-- - **Web Framework:** `fastapi` -->
+- **Demo Framework:** `streamlit`
+- **LLM Framework:** `langchain`, `transformers`, `langgraph`
+- **Vector Database:** `faiss`, `chroma` (optional)
+<!-- - **Experiment Tracking:** `mlflow`, `tensorboard` (optional)
+- **Hyperparameter Optimization:** `optuna`, `hyperopt` (optional) -->
+- **Data Processing:** `pandas`, `numpy`, `dask` (optional), `pyspark` (optional)
+- **Version Control:** `git`
+<!-- - **Server:** `gunicorn`, `uvicorn` (with `nginx` or `caddy`) -->
+- **Process Management:** `systemd`, `supervisor`
+
+# Coding Guidelines
+
+## 1. Pythonic Practices
+
+- **Elegance and Readability:** Strive for elegant and Pythonic code that is easy to understand and maintain.
+- **PEP 8 Compliance:** Adhere to PEP 8 guidelines for code style, with Ruff as the primary linter and formatter.
+- **Explicit over Implicit:** Favor explicit code that clearly communicates its intent over implicit, overly concise code.
+- **Zen of Python:** Keep the Zen of Python in mind when making design decisions.
+
+## 2. Modular Design
+
+- **Single Responsibility Principle:** Each module/file should have a well-defined, single responsibility.
+- **Reusable Components:** Develop reusable functions and classes, favoring composition over inheritance.
+- **Package Structure:** Organize code into logical packages and modules.
+
+## 3. Code Quality
+
+- **Comprehensive Type Annotations:** All functions, methods, and class members must have type annotations, using the most specific types possible.
+- **Detailed Docstrings:** All functions, methods, and classes must have Google-style docstrings, thoroughly explaining their purpose, parameters, return values, and any exceptions raised. Include usage examples where helpful.
+- **Thorough Unit Testing:** Aim for high test coverage (90% or higher) using `pytest`. Test both common cases and edge cases.
+- **Robust Exception Handling:** Use specific exception types, provide informative error messages, and handle exceptions gracefully. Implement custom exception classes when needed. Avoid bare `except` clauses.
+- **Logging:** Employ the `logging` module judiciously to log important events, warnings, and errors.
+
+<!-- ## 4. ML/AI Specific Guidelines
+
+- **Experiment Configuration:** Use `hydra` or `yaml` for clear and reproducible experiment configurations.
+- **Data Pipeline Management:** Employ scripts or tools like `dvc` to manage data preprocessing and ensure reproducibility.
+- **Model Versioning:** Utilize `git-lfs` or cloud storage to track and manage model checkpoints effectively.
+- **Experiment Logging:** Maintain comprehensive logs of experiments, including parameters, results, and environmental details.
+- **LLM Prompt Engineering:** Dedicate a module or files for managing Prompt templates with version control.
+- **Context Handling:** Implement efficient context management for conversations, using suitable data structures like deques. -->
+
+## 5. Performance Optimization
+
+- **Asynchronous Programming:** Leverage `async` and `await` for I/O-bound operations to maximize concurrency.
+- **Caching:** Apply `functools.lru_cache`, `@cache` (Python 3.9+), or `fastapi.Depends` caching where appropriate.
+- **Resource Monitoring:** Use `psutil` or similar to monitor resource usage and identify bottlenecks.
+- **Memory Efficiency:** Ensure proper release of unused resources to prevent memory leaks.
+- **Concurrency:** Employ `concurrent.futures` or `asyncio` to manage concurrent tasks effectively.
+- **Database Best Practices:** Design database schemas efficiently, optimize queries, and use indexes wisely.
+
+<!-- ## 6. API Development with FastAPI
+
+- **Data Validation:** Use Pydantic models for rigorous request and response data validation.
+- **Dependency Injection:** Effectively use FastAPI's dependency injection for managing dependencies.
+- **Routing:** Define clear and RESTful API routes using FastAPI's `APIRouter`.
+- **Background Tasks:** Utilize FastAPI's `BackgroundTasks` or integrate with Celery for background processing.
+- **Security:** Implement robust authentication and authorization (e.g., OAuth 2.0, JWT).
+- **Documentation:** Auto-generate API documentation using FastAPI's OpenAPI support.
+- **Versioning:** Plan for API versioning from the start (e.g., using URL prefixes or headers).
+- **CORS:** Configure Cross-Origin Resource Sharing (CORS) settings correctly.
+ -->
+# Code Example Requirements
+
+- All functions must include type annotations.
+- Must provide clear, Google-style docstrings.
+- Key logic should be annotated with comments.
+- Provide usage examples (e.g., in the `tests/` directory or as a `__main__` section).
+- Include error handling.
+- Use `ruff` for code formatting.
+
+# Others
+
+- **Prioritize new features in Python 3.11+.**
+- **When explaining code, provide clear logical explanations and code comments.**
+- **When making suggestions, explain the rationale and potential trade-offs.**
+- **If code examples span multiple files, clearly indicate the file name.**
+- **Do not over-engineer solutions. Strive for simplicity and maintainability while still being efficient.**
+- **Favor modularity, but avoid over-modularization.**
+- **Use the most modern and efficient libraries when appropriate, but justify their use and ensure they don't add unnecessary complexity.**
+- **When providing solutions or examples, ensure they are self-contained and executable without requiring extensive modifications.**
+- **If a request is unclear or lacks sufficient information, ask clarifying questions before proceeding.**
+- **Always consider the security implications of your code, especially when dealing with user inputs and external data.**
+- **Actively use and promote best practices for the specific tasks at hand (LLM app development, data cleaning, demo creation, etc.).**
+

.vscode/launch.json

@@ -0,0 +1,16 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+    
+        {
+            "name": "Python Debugger: Current File",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal"
+        }
+    ]
+}

.windsurfrules

@@ -0,0 +1,107 @@
+
+# Role Definition
+
+- You are a **Python master**, a highly experienced **tutor**, a **world-renowned ML engineer**, and a **talented data scientist**.
+- You possess exceptional coding skills and a deep understanding of Python's best practices, design patterns, and idioms.
+- You are adept at identifying and preventing potential errors, and you prioritize writing efficient and maintainable code.
+- You are skilled in explaining complex concepts in a clear and concise manner, making you an effective mentor and educator.
+- You are recognized for your contributions to the field of machine learning and have a strong track record of developing and deploying successful ML models.
+- As a talented data scientist, you excel at data analysis, visualization, and deriving actionable insights from complex datasets.
+
+# Technology Stack
+
+- **Python Version:** Python 3.11+
+- **Dependency Management:** Pipenv
+- **Code Formatting:** Ruff (replaces `black`, `isort`, `flake8`)
+- **Type Hinting:** Strictly use the `typing` module. All functions, methods, and class members must have type annotations.
+- **Testing Framework:** `pytest`
+- **Documentation:** Google style docstring
+- **Environment Management:** 'Pipenv'
+- **Containerization:** `docker`, `docker-compose`
+- **Asynchronous Programming:** Prefer `async` and `await`
+<!-- - **Web Framework:** `fastapi` -->
+- **Demo Framework:** `streamlit`
+- **LLM Framework:** `langchain`, `transformers`, `langgraph`
+- **Vector Database:** `faiss`, `chroma` (optional)
+<!-- - **Experiment Tracking:** `mlflow`, `tensorboard` (optional)
+- **Hyperparameter Optimization:** `optuna`, `hyperopt` (optional) -->
+- **Data Processing:** `pandas`, `numpy`, `dask` (optional), `pyspark` (optional)
+- **Version Control:** `git`
+<!-- - **Server:** `gunicorn`, `uvicorn` (with `nginx` or `caddy`) -->
+- **Process Management:** `systemd`, `supervisor`
+
+# Coding Guidelines
+
+## 1. Pythonic Practices
+
+- **Elegance and Readability:** Strive for elegant and Pythonic code that is easy to understand and maintain.
+- **PEP 8 Compliance:** Adhere to PEP 8 guidelines for code style, with Ruff as the primary linter and formatter.
+- **Explicit over Implicit:** Favor explicit code that clearly communicates its intent over implicit, overly concise code.
+- **Zen of Python:** Keep the Zen of Python in mind when making design decisions.
+
+## 2. Modular Design
+
+- **Single Responsibility Principle:** Each module/file should have a well-defined, single responsibility.
+- **Reusable Components:** Develop reusable functions and classes, favoring composition over inheritance.
+- **Package Structure:** Organize code into logical packages and modules.
+
+## 3. Code Quality
+
+- **Comprehensive Type Annotations:** All functions, methods, and class members must have type annotations, using the most specific types possible.
+- **Detailed Docstrings:** All functions, methods, and classes must have Google-style docstrings, thoroughly explaining their purpose, parameters, return values, and any exceptions raised. Include usage examples where helpful.
+- **Thorough Unit Testing:** Aim for high test coverage (90% or higher) using `pytest`. Test both common cases and edge cases.
+- **Robust Exception Handling:** Use specific exception types, provide informative error messages, and handle exceptions gracefully. Implement custom exception classes when needed. Avoid bare `except` clauses.
+- **Logging:** Employ the `logging` module judiciously to log important events, warnings, and errors.
+
+<!-- ## 4. ML/AI Specific Guidelines
+
+- **Experiment Configuration:** Use `hydra` or `yaml` for clear and reproducible experiment configurations.
+- **Data Pipeline Management:** Employ scripts or tools like `dvc` to manage data preprocessing and ensure reproducibility.
+- **Model Versioning:** Utilize `git-lfs` or cloud storage to track and manage model checkpoints effectively.
+- **Experiment Logging:** Maintain comprehensive logs of experiments, including parameters, results, and environmental details.
+- **LLM Prompt Engineering:** Dedicate a module or files for managing Prompt templates with version control.
+- **Context Handling:** Implement efficient context management for conversations, using suitable data structures like deques. -->
+
+## 5. Performance Optimization
+
+- **Asynchronous Programming:** Leverage `async` and `await` for I/O-bound operations to maximize concurrency.
+- **Caching:** Apply `functools.lru_cache`, `@cache` (Python 3.9+), or `fastapi.Depends` caching where appropriate.
+- **Resource Monitoring:** Use `psutil` or similar to monitor resource usage and identify bottlenecks.
+- **Memory Efficiency:** Ensure proper release of unused resources to prevent memory leaks.
+- **Concurrency:** Employ `concurrent.futures` or `asyncio` to manage concurrent tasks effectively.
+- **Database Best Practices:** Design database schemas efficiently, optimize queries, and use indexes wisely.
+
+<!-- ## 6. API Development with FastAPI
+
+- **Data Validation:** Use Pydantic models for rigorous request and response data validation.
+- **Dependency Injection:** Effectively use FastAPI's dependency injection for managing dependencies.
+- **Routing:** Define clear and RESTful API routes using FastAPI's `APIRouter`.
+- **Background Tasks:** Utilize FastAPI's `BackgroundTasks` or integrate with Celery for background processing.
+- **Security:** Implement robust authentication and authorization (e.g., OAuth 2.0, JWT).
+- **Documentation:** Auto-generate API documentation using FastAPI's OpenAPI support.
+- **Versioning:** Plan for API versioning from the start (e.g., using URL prefixes or headers).
+- **CORS:** Configure Cross-Origin Resource Sharing (CORS) settings correctly.
+ -->
+# Code Example Requirements
+
+- All functions must include type annotations.
+- Must provide clear, Google-style docstrings.
+- Key logic should be annotated with comments.
+- Provide usage examples (e.g., in the `tests/` directory or as a `__main__` section).
+- Include error handling.
+- Use `ruff` for code formatting.
+
+# Others
+
+- **Prioritize new features in Python 3.11+.**
+- **When explaining code, provide clear logical explanations and code comments.**
+- **When making suggestions, explain the rationale and potential trade-offs.**
+- **If code examples span multiple files, clearly indicate the file name.**
+- **Do not over-engineer solutions. Strive for simplicity and maintainability while still being efficient.**
+- **Favor modularity, but avoid over-modularization.**
+- **Use the most modern and efficient libraries when appropriate, but justify their use and ensure they don't add unnecessary complexity.**
+- **When providing solutions or examples, ensure they are self-contained and executable without requiring extensive modifications.**
+- **If a request is unclear or lacks sufficient information, ask clarifying questions before proceeding.**
+- **Always consider the security implications of your code, especially when dealing with user inputs and external data.**
+- **Actively use and promote best practices for the specific tasks at hand (LLM app development, data cleaning, demo creation, etc.).**
+

Pipfile

@@ -10,8 +10,14 @@ torch = "*"
 torchaudio = "*"
 tts = "*"
 python-dotenv = "*"
+langgraph = "*"
+langchain-google-genai = "*"
+langchain = "*"
+langchain-core = "*"
+pydantic = "*"
+sox = "*"
 
 [dev-packages]
 
 [requires]
-python_version = "3.10"
+python_version = "3.11"

Pipfile.lock

@@ -1,11 +1,11 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "3a1bbc8728fea00617f226aa2eca408586d7e9d4286153e29efac1dcaf967dad"
+            "sha256": "4a7b41a97a228d1c5a6f7666ec49f031aad9b57aff5a418b221ad899870f2656"
         },
         "pipfile-spec": 6,
         "requires": {
-            "python_version": "3.10"
+            "python_version": "3.11"
         },
         "sources": [
             {
@@ -151,13 +151,13 @@
             "markers": "python_version >= '3.3'",
             "version": "==0.3.2"
         },
-        "async-timeout": {
+        "anyio": {
             "hashes": [
-                "sha256:39e3809566ff85354557ec2398b55e096c8364bacac9405a7a1fa429e77fe76c",
-                "sha256:d9321a7a3d5a6a5e187e824d2fa0793ce379a202935782d555d6e9d2735677d3"
+                "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a",
+                "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"
             ],
-            "markers": "python_version < '3.11'",
-            "version": "==5.0.1"
+            "markers": "python_version >= '3.9'",
+            "version": "==4.8.0"
         },
         "attrs": {
             "hashes": [
@@ -467,53 +467,63 @@
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@@ -687,6 +697,13 @@
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@@ -881,6 +898,142 @@
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@@ -942,6 +1095,13 @@
             "markers": "python_version >= '3.8'",
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@@ -985,6 +1145,14 @@
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@@ -992,6 +1160,30 @@
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@@ -1061,6 +1253,22 @@
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@@ -1163,6 +1371,41 @@
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+        "langchain-google-genai": {
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+            "markers": "python_version >= '3.9' and python_version < '4.0'",
+            "version": "==2.0.9"
+        },
+        "langchain-text-splitters": {
+            "hashes": [
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+                "sha256:e5d7b850f6c14259ea930be4a964a65fa95d9df7e1dbdd8bad8416db72292f4e"
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+            "version": "==0.3.6"
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@@ -1171,6 +1414,39 @@
             "markers": "python_version >= '3.9'",
             "version": "==3.5.0"
         },
+        "langgraph": {
+            "hashes": [
+                "sha256:5b725c02a6eb55257479a0ea0db40926f766c8ff12c2730690f62c614d7e6ee5",
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+            "markers": "python_version < '4.0' and python_full_version >= '3.9.0'",
+            "version": "==0.2.72"
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+            "version": "==2.0.13"
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+            "version": "==0.1.51"
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+        "langsmith": {
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@@ -1188,38 +1464,38 @@
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@@ -1388,37 +1664,43 @@
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@@ -1686,58 +1968,72 @@
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@@ -2214,6 +2628,7 @@
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@@ -2629,6 +3044,14 @@
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             "version": "==2.32.3"
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@@ -2746,6 +3169,14 @@
             "markers": "python_version >= '3.9'",
             "version": "==0.22.3"
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@@ -2805,34 +3236,49 @@
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@@ -2874,6 +3320,14 @@
             "markers": "python_version >= '3.7'",
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@@ -2887,6 +3341,13 @@
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@@ -2954,6 +3415,69 @@
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@@ -2998,12 +3522,12 @@
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@@ -3053,10 +3577,10 @@
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@@ -3257,14 +3788,14 @@
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@@ -3303,11 +3834,11 @@
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@@ -3324,6 +3855,14 @@
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claer

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instructions/instructions.MD

@@ -122,12 +122,11 @@ streamlit_app/
 
 - **Langgraph**
   - [How to add cross-thread persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/cross-thread-persistence/)
-  - [How to add cross-thread persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/cross-thread-persistence/)
 
 
 - **LLM Processing:**
 
-  - [LangChain's ChatGoogleGenerativeAI Documentation](https://docs.litellm.ai/docs/)
+  - [LiteLLM's Documentation](https://docs.litellm.ai/docs/)
 
 ---
 

streamlit_app/config.py

@@ -0,0 +1,25 @@
+from typing import TypedDict
+from pathlib import Path
+import os
+from dotenv import load_dotenv
+
+class Config(TypedDict):
+    WHISPER_MODEL: str
+    TTS_MODEL: str
+    GOOGLE_API_KEY: str
+    AUDIO_BACKEND: str
+    DEVICE: str
+
+
+def load_config() -> Config:        
+    env_path = os.path.join( os.getcwd(), "env", ".env")
+    print(f"env_path: {env_path}")
+    load_dotenv(env_path)
+    
+    return {
+        "WHISPER_MODEL": os.getenv("WHISPER_MODEL","small"),
+        "TTS_MODEL": os.getenv("TTS_MODEL","tts_models/en/jenny/jenny"),
+        "GOOGLE_API_KEY": os.getenv("GOOGLE_API_KEY", ""),
+        "AUDIO_BACKEND": os.getenv("AUDIO_BACKEND","sox_io"),
+        "DEVICE": "cpu"
+    } 

streamlit_app/env/.env.example

@@ -1,5 +1,8 @@
 # Google API Key for Gemini
 GOOGLE_API_KEY=your_google_api_key
+AUDIO_BACKEND=sox_io
+WHISPER_MODEL=small
+TTS_MODEL=tts_models/en/jenny/jenny
 
 # Optional: Coqui TTS model settings
 COQUI_MODEL_NAME=tts_models/en/ljspeech/tacotron2-DDC

streamlit_app/graph/chains.py

@@ -0,0 +1,186 @@
+from langchain_core.runnables.config import RunnableConfig
+from pydantic import BaseModel, Field
+from langchain.prompts import PromptTemplate
+from typing import List, Optional
+from langchain_core.output_parsers import StrOutputParser
+# Models for structured outputs
+class Intent(BaseModel):
+    Intent: str = Field(description="The intent of the user's message")
+
+def create_detect_intent_chain(llm):
+    prompt_template = PromptTemplate.from_template(
+        """You are a helpful assistant that determines if a user's message is a request for translation or a general chat message.
+        If the user is asking for translation (either explicitly or implicitly), respond with exactly 'translation'.
+        If the user is having a general conversation or asking questions, respond with exactly 'chat'.
+        Only respond with one of these two words: 'translation' or 'chat'.
+        
+        Examples:
+        - "Translate 'hello' to Spanish" -> 'translation'
+        - "How do you say 'hello' in Spanish" -> 'translation'
+        - "What's the weather like?" -> 'chat'
+        - "Can you help me learn Spanish?" -> 'chat'
+
+        User: {input}    
+        """
+    )
+
+    
+    llm_structured = llm.with_structured_output(Intent)
+    return prompt_template | llm_structured
+
+# class ExtractedSentence(BaseModel):
+#     sentence: str = Field(description="The cleaned sentence to be translated")
+#     source_language: str = Field(description="Detected source language")
+#     target_language: Optional[str] = Field(description="Requested target language if specified")
+
+# def create_extract_sentence_chain(llm, user_message: str):
+#     prompt_template = PromptTemplate.from_template(
+#         """
+#         You are a helpful language assistant that extracts the core sentence to be translated from the user's message. 
+#         Detect the source language and identify target language if specified.
+#         Remove any extra context like 'translate this:' or 'how do you say'.
+        
+#         Examples:
+#         Input: "Can you translate 'hello my friend' to Spanish?"
+#         Output: Core sentence: "hello my friend"
+#         Source: English
+#         Target: Spanish
+        
+#         Input: "How do you say 'I love programming' in Japanese?"
+#         Output: Core sentence: "I love programming"
+#         Source: English
+#         Target: Japanese
+        
+#         User message: {user_message}
+#         """
+#     )
+    
+#     prompt = prompt_template.format(user_message=user_message)
+#     llm_structured = llm.with_structured_output(ExtractedSentence)
+#     return prompt | llm_structured
+
+class TranslationOption(BaseModel):
+    translation: str = Field(description="The translated text")
+    description: str = Field(description="Description of this translation variant")
+    # formality_level: str = Field(description="Formality level (formal, informal, neutral)")
+
+class TranslationResponse(BaseModel):
+    # source_text: str = Field(description="Original text")
+    target_language: str = Field(description="Target language")
+    options: List[TranslationOption] = Field(description="List of translation options")
+
+def create_translate_chain(llm):
+    # prompt_template = PromptTemplate.from_template(
+    #     """Translate the following text from {source_language} to {target_language}.
+    #     Provide multiple translation options with different formality levels and explanations.
+        
+    #     Text to translate: {sentence}
+        
+    #     For each translation, provide:
+    #     1. The translated text
+    #     2. A description of when to use this variant
+    #     3. The formality level (formal, informal, neutral)
+        
+    #     Format your response as a structured list of translations with these components.
+    #     """
+    # )
+    
+    prompt_template = PromptTemplate.from_template(
+        """You are a helpful language assistant that translates text
+        Translate the provided user's request.
+        Provide possible translations and their respecitve explanations.
+        
+        <user_request> {user_request} </user_request> 
+        In your response, provide:
+          - the target language.
+          - For each translation, provide:
+            1. The translated text
+            2. A description of when to use this variant
+            
+        Respond in JSON format
+        Example of JSON response:
+        response:{
+        "target_language": "here goes the target language",
+        "translations":[
+                {
+                    "translation": "Here goes the translation",
+                    "description": "Here goes the description/explanation of the translation",
+                }          
+            ]
+        }
+        """
+    )
+   
+    llm_structured = llm.with_structured_output(TranslationResponse)
+    return prompt_template | llm_structured
+
+# class WordBreakdown(BaseModel):
+#     word: str = Field(description="Individual word")
+#     part_of_speech: str = Field(description="Part of speech (noun, verb, etc.)")
+#     base_form: str = Field(description="Base/dictionary form")
+#     explanation: str = Field(description="Meaning and usage explanation")
+
+# class SentenceBreakdown(BaseModel):
+#     words: List[WordBreakdown] = Field(description="List of word breakdowns")
+#     # grammar_notes: Optional[str] = Field(description="Additional grammar explanations")
+
+
+# def create_sentence_breakdown_chain(llm, translation: Translation):
+#     prompt_template = PromptTemplate.from_template(
+#         """Break down this translated sentence into individual words and provide detailed explanations.
+        
+#         Original text: {source_text}
+#         Translated text: {translation}
+        
+#         For each word, provide:
+#         1. The word itself
+#         2. Part of speech
+#         3. Base/dictionary form
+#         4. Detailed explanation of meaning and usage
+        
+        
+#         """
+#     )
+    
+#     # Use the first translation option for breakdown
+#     prompt = prompt_template.format(
+#         source_text=translation.source_text,
+#         translation=translation.options[0].translation
+#     )
+#     llm_structured = llm.with_structured_output(SentenceBreakdown)
+#     return prompt | llm_structured
+
+def create_chat_response_chain(llm):
+    prompt_template = PromptTemplate.from_template(
+        """You are a helpful language learning assistant. Engage in conversation with the user,
+        helping them practice and learn languages.
+        
+        Previous conversation:
+        {chat_history}
+        
+        User: {input}
+        
+        Respond naturally and engagingly, encouraging language practice when appropriate.
+        """
+    )
+    # llm_structured = llm.with_structured_output(ChatResponse)
+    return prompt_template | llm | StrOutputParser()
+
+
+
+if __name__ == "__main__":
+    from langchain_google_genai import ChatGoogleGenerativeAI
+    from ..config import load_config
+
+    
+    def test_detect_intent_chain():
+        config = load_config()
+        llm = ChatGoogleGenerativeAI(
+            model="gemini-2.0-flash",
+            temperature=0.7,
+        )    
+        detect_intent_chain = create_detect_intent_chain(llm)
+        result = detect_intent_chain.invoke({"input": "Translate 'hello' to Spanish"})
+        print(f"result: {result}")
+
+    test_detect_intent_chain()

streamlit_app/graph/configSchema.py

@@ -1,7 +1,9 @@
 from typing import TypedDict, Any
+from stt_tts.models import TTSModel, STTModel
 
 class ConfigSchema(TypedDict):
-    tts_model: Any
-    stt_model: Any
+    tts_model: TTSModel
+    stt_model: STTModel
+    llm_config: dict[str, Any]
     
     

streamlit_app/graph/state.py

@@ -1,5 +1,5 @@
-from ast import List
-from typing import Dict, TypedDict, Annotated
+from typing import Literal, TypedDict, Annotated, Optional
+from operator import add
 
 class TranslationOptions(TypedDict):
     description: str
@@ -7,15 +7,16 @@ class TranslationOptions(TypedDict):
 
 class Translation(TypedDict):
     language: str
-    options: List[TranslationOptions]
-# Define state schema
+    options: Annotated[list[TranslationOptions], add]
+
 class PolyglotState(TypedDict):
     input: str
-    # input_type: str  # "text" or "audio"
-    intent: str  # "chat" or "translation"
-    # transcription: str
+    # input_type: Literal["text", "audio"]
+    intent: Literal["chat", "translation"]
+    # transcription: Optional[str]
     translation: Translation
-    response: str
-    audio_response_file: str
+    chat_resp: str
+    chat_history: Annotated[list[str], add]
+    audio_response_file: Annotated[list[str], add]
 
 # class PolyglotStateInternal(TypedDict):

streamlit_app/graph/workflow.py

@@ -1,11 +1,14 @@
-from typing import Annotated
+from typing import Annotated, Dict, Any, Callable
 from langgraph.graph import StateGraph, END
-from langchain_google_genai import ChatGoogleGenerativeAI
-from dotenv import load_dotenv
+# from dotenv import load_dotenv
 import os
-from .state import PolyglotState
+from .state import PolyglotState, Translation, TranslationOptions
+from langchain_core.runnables.config import RunnableConfig
+from .configSchema import ConfigSchema
+from .chains import create_detect_intent_chain, Intent, create_translate_chain, TranslationResponse, create_chat_response_chain
+from stt_tts.models import TTSModel
 # Load environment variables
-load_dotenv()
+# load_dotenv()
 
  # Define nodes (placeholder implementations)
     
@@ -14,108 +17,166 @@ load_dotenv()
 #     if state["input_type"] == "audio":
 #         state["transcription"] = state["input"]  # Placeholder
 #     return state
+def create_detect_intent_node(llm)-> Callable[[PolyglotState, RunnableConfig], PolyglotState]:
+    detect_intent_chain = create_detect_intent_chain(llm)    
+    def detect_intent(state: PolyglotState, config: RunnableConfig) -> PolyglotState:
+        print(f"detect_intent")
+        print(f"input: {state['input']}")
+        try:
+            # Get intent from LLM
+            intent: Intent = detect_intent_chain.invoke({"input": state["input"]})                        
+            result = intent.Intent
+            # Validate intent
+            if intent.Intent not in ["translation", "chat"]:
+                # Default to chat if response is invalid
+                result = "chat"                                                                
+        except Exception as e:
+            # Log the error (you might want to add proper logging)
+            print(f"Error in intent detection: {str(e)}")
+            # Default to chat on error
+            result = "chat"
 
-def detect_intent(state: PolyglotState) -> Annotated[PolyglotState, str]:
-    # Initialize ChatGoogleGenerativeAI
-    llm = ChatGoogleGenerativeAI(
-        model="gemini-2.0-flash",
-        temperature=0,  # We want deterministic responses for intent detection
-    )
+        print(f"intent: {result}")
+        return {"intent": result}            
+    return detect_intent
+
+    
+
+# def create_extract_sentence(llm):
+#     def extract_sentence_for_translation(state: PolyglotState, config: RunnableConfig) -> PolyglotState:
+#         """Extract sentences for translation using LLM."""
+#         text = state["transcription"] if state["input_type"] == "audio" else state["input"]
+        
+#         messages = [
+#             ("system", """Extract the sentence to be translated. Remove any extra context like 'translate this:' 
+#             or 'how do you say'. Just return the core sentence to translate."""),
+#             ("human", text)
+#         ]
+        
+#         response = llm.invoke(messages)
+#         state["sentence_to_translate"] = response.content.strip()
+#         return state
     
-    # Get the input text
-    text = state["transcription"] if state["input_type"] == "audio" else state["input"]
+#     return extract_sentence_for_translation
+
+def create_translate_node(llm)-> Callable[[PolyglotState, RunnableConfig], PolyglotState]:
+    translate_chain = create_translate_chain(llm)
+    def translate_text(state: PolyglotState, config: RunnableConfig) -> PolyglotState:
+        """Translate text and provide multiple options."""
+        print(f"translate_text")
+        print(f"input: {state['input']}")
+        try:
+            response:TranslationResponse = translate_chain.invoke({"user_request": state["input"]})
+            print(f"response: {response}")
+            translation = Translation()
+            translation["language"] = response.target_language
+            for option in response.translations:
+                translation_option = TranslationOptions()
+                translation_option["translation"] = option.translation
+                translation_option["description"] = option.description
+                translation["options"].append(translation_option)
+            return {"translation": translation}
+        except Exception as e:
+            print(f"Error in translate_text: {str(e)}")
+            return state
     
-    # Create system message and user message
-    messages = [
-        (
-            "system",
-            """You are a helpful assistant that determines if a user's message is a request for translation or a general chat message.
-            If the user is asking for translation (either explicitly or implicitly), respond with exactly 'translation'.
-            If the user is having a general conversation or asking questions, respond with exactly 'chat'.
-            Only respond with one of these two words: 'translation' or 'chat'.
+    return translate_text
+
+# def create_sentence_breakdown(llm):
+#     def break_down_sentence(state: PolyglotState, config: RunnableConfig) -> PolyglotState:
+#         """Break down translated sentence into words and store in state."""
+#         if not state.get("translation"):
+#             return state
             
-            Examples:
-            - "Translate 'hello' to Spanish" -> 'translation'
-            - "How do you say 'hello' in Spanish" -> 'translation'
-            - "What's the weather like?" -> 'chat'
-            - "Can you help me learn Spanish?" -> 'chat'
-            """
-        ),
-        ("human", text)
-    ]
-    
-    try:
-        # Get intent from LLM
-        ai_msg = llm.invoke(messages)
-        intent = ai_msg.content.strip().lower()
-        
-        # Validate intent
-        if intent not in ["translation", "chat"]:
-            # Default to chat if response is invalid
-            intent = "chat"
+#         messages = [
+#             ("system", """Break down this translation into individual words and provide explanations.
+#             Format as a list of word objects with 'word' and 'explanation' fields."""),
+#             ("human", state["translation"]["options"][0]["translation"])
+#         ]
         
-        state["intent"] = intent
-        return state, intent
+#         response = llm.invoke(messages)
+#         state["word_breakdown"] = response.content  # You'll want to parse this properly
+#         return state
+    
+#     return break_down_sentence
+
+def create_tts_node(tts_model:TTSModel) -> Callable[[PolyglotState, RunnableConfig], PolyglotState]:
+    """Convert translations to speech."""
+    def text_to_speech(state: PolyglotState, config: RunnableConfig) -> PolyglotState:
+        print(f"text_to_speech")
+        print(f"translation: {state['translation']}")
+        if not state.get("translation"):
+            return state
+            
+        translations = state["translation"]["options"]
+        audio_files = []
         
-    except Exception as e:
-        # Log the error (you might want to add proper logging)
-        print(f"Error in intent detection: {str(e)}")
-        # Default to chat on error
-        state["intent"] = "chat"
-        return state, "chat"
+        for option in translations:
+            audio_path = tts_model.tts_to_file(option["translation"])
+            audio_files.append(audio_path)
 
-def translate(state: PolyglotState) -> PolyglotState:
-    # TODO: Implement translation using Gemini
-    text = state["transcription"] if state["input_type"] == "audio" else state["input"]
-    text = text.replace("translate:", "").strip()
-    state["translation"] = f"Translation placeholder: {text}"
-    return state
+        print(f"audio_files: {audio_files}")
+        return {"audio_response_files": audio_files}
+    
+    return text_to_speech
 
-def chat_response(state: PolyglotState) -> PolyglotState:
-    # TODO: Implement chat using Gemini
-    text = state["transcription"] if state["input_type"] == "audio" else state["input"]
-    state["response"] = f"Chat response placeholder: {text}"
-    return state
+def create_chat_response_node(llm)-> Callable[[PolyglotState, RunnableConfig], PolyglotState]:
+    chat_response_chain = create_chat_response_chain(llm)
+    def chat_response(state: PolyglotState, config: RunnableConfig) -> PolyglotState:
+        """Generate a chat response using LLM."""
+        response = chat_response_chain.invoke({"chat_history": state["chat_history"], "input": state["input"]})
+        return {"chat_resp": response}
+    return chat_response
 
-def synthesize_speech(state: PolyglotState, config: RunnableConfig) -> PolyglotState:
-    # TODO: Implement Coqui TTS
-    # Access the config through the configurable key
-    tts_model    = config["configurable"].get("tts_model", None)
-    state["audio_response"] = tts_model.tts_to_file(state["translation"])
-    # state["audio_response"] = tts_model.tts_to_file(state["response"])
-    state["audio_response"] = b""  # Placeholder
-    return state
-    
+def where_to_go(state: PolyglotState, config: RunnableConfig) -> PolyglotState:
+    """Determine where to go based on intent."""
+    if state["intent"] == "translation":
+        return "translate"
+    else:
+        return "chat_response"
 
-def create_workflow() -> StateGraph:
+def create_workflow(llm, tts_model:TTSModel) -> StateGraph:
+    """Create the workflow graph with dependencies injected."""
+    
     # Initialize workflow graph
     workflow = StateGraph(PolyglotState)
     
-   
+    # Create nodes with dependencies
+    detect_intent_node = create_detect_intent_node(llm)
+    # extract_sentence_node = create_extract_sentence(llm)
+    translate_node = create_translate_node(llm)
+    # breakdown_node = create_sentence_breakdown(llm)
+    tts_node = create_tts_node(tts_model=tts_model)
+    chat_response_node = create_chat_response_node(llm)
     # Add nodes to graph
-    # workflow.add_node("transcribe", transcribe)
-    workflow.add_node("detect_intent", detect_intent)
-    workflow.add_node("translate", translate)
-    workflow.add_node("chat_response", chat_response)
-    workflow.add_node("synthesize_speech", synthesize_speech)
+    workflow.add_node("detect_intent", detect_intent_node)
+    workflow.add_node("chat_response", chat_response_node)
+    # workflow.add_node("extract_sentence", extract_sentence_node)
+    workflow.add_node("translate", translate_node)
+    # workflow.add_node("break_down_sentence", breakdown_node)
+    workflow.add_node("text_to_speech", tts_node)
     
-    # Define edges
-    workflow.add_edge("transcribe", "detect_intent")
+    # Define edges based on flow chart
     workflow.add_conditional_edges(
         "detect_intent",
+        where_to_go,
         {
-            "translation": "translate",
-            "chat": "chat_response",
+            "translate": "translate",
+            "chat_response": "chat_response",
         }
     )
-    workflow.add_edge("translate", "synthesize_speech")
-    workflow.add_edge("synthesize_speech", END)
+    
+    # Translation flow
+    workflow.add_edge("translate", "text_to_speech")
+    # workflow.add_edge("extract_sentence", "translate")
+    # workflow.add_edge("translate", "break_down_sentence")
+    # workflow.add_edge("break_down_sentence", "text_to_speech")
+    workflow.add_edge("text_to_speech", END)
+    
+    # Chat flow
     workflow.add_edge("chat_response", END)
     
     # Set entry point
-    workflow.set_entry_point("transcribe")
+    workflow.set_entry_point("detect_intent")
     
-    return workflow
-
-# Create singleton instance
-workflow = create_workflow()
+    return workflow

streamlit_app/main.py

@@ -3,21 +3,39 @@ from pathlib import Path
 import json
 import os
 from dotenv import load_dotenv
-from streamlit_app.graph import workflow
-from streamlit_app.stt_tts.models import STTModel
-
+# from streamlit_app.graph import workflow
+from stt_tts.models import STTModel, TTSModel
+from graph.workflow import create_workflow
+from config import load_config
+from langchain_google_genai import ChatGoogleGenerativeAI
 # Load environment variables
-load_dotenv()
+# load_dotenv()
+config = load_config()
+# print(f"config: {config}")
+def print_available_tts_models():
+    from TTS.api import TTS
 
+    available_models = TTS.list_models(language="en")
+    print(available_models)
 # Initialize session state
+
 if "messages" not in st.session_state:
     st.session_state.messages = []
 if "dictionary" not in st.session_state:
     st.session_state.dictionary = {}
 if "stt" not in st.session_state:
-    st.session_state.stt = STTModel()
+    st.session_state.stt = STTModel(config["WHISPER_MODEL"])
 if "tts" not in st.session_state:
-    st.session_state.tts = TTSModel()
+    # print_available_tts_models()
+    st.session_state.tts = TTSModel(config["TTS_MODEL"],  audio_backend=config["AUDIO_BACKEND"])
+    
+
+if "llm" not in st.session_state:
+# Initialize LLM
+    st.session_state.llm = ChatGoogleGenerativeAI(
+        model="gemini-2.0-flash",
+        temperature=0.7,
+    )    
 
 def initialize_app():
     st.set_page_config(
@@ -44,8 +62,54 @@ def create_sidebar():
             # TODO: Implement download functionality
             st.text_input("Enter your email:", key="download_email")
 
+def display_translation_options(translation_options, audio_files):
+    for idx, option in enumerate(translation_options):
+        translation_text = option['translation']
+        context_text = option['description'] 
+        translation_number = idx + 1
+        translation_message = f"**Translation {translation_number}:** {translation_text}\n*Context:* {context_text}"
+        st.session_state.messages.append({"role": "assistant", "content": translation_message})
+        st.markdown(f"**Translation {translation_number}:** {translation_text}")
+        st.markdown(f"*Context:* {context_text}")
+        if audio_files and idx < len(audio_files):
+            st.audio(audio_files[idx])
+        else:
+            st.markdown("An error occurred while processing your message. Please try again.")
+
+def process_chat_message(input_text: str, is_audio: bool = False) -> None:
+    """
+    Process a chat message and update the chat interface with the response.
+    
+    Args:
+        input_text: The text to process (either direct input or transcribed audio)
+        is_audio: Boolean indicating if the input was from audio
+    """
+    # Add user message to chat history
+    display_text = f"🎤 *Audio message*:\n{input_text}" if is_audio else input_text
+    st.session_state.messages.append({"role": "user", "content": display_text})
+    
+    with st.chat_message("user"):
+        st.markdown(display_text)
+        if is_audio:
+            st.markdown(input_text)  # Show transcription below audio message
+    
+    # Process through workflow
+    workflow = create_workflow(llm=st.session_state.llm,tts_model=st.session_state.tts)    
+    app_workflow = workflow.compile()
+    result = app_workflow.invoke({"input": input_text})
+    
+    # Display assistant response
+    with st.chat_message("assistant"):
+        if (result["chat_resp"] is not None):
+            st.markdown(result["chat_resp"])
+            st.session_state.messages.append({"role": "assistant", "content": result["chat_resp"]})
+        elif (result["translation"] is not None):
+            # Display each translation option with its audio
+            display_translation_options(result["translation"]["options"], result.get("audio_response_files", []))
+
+
+
 def display_chat_interface():
-    workflow = create_workflow()
     # Display chat messages
     for message in st.session_state.messages:
         with st.chat_message(message["role"]):
@@ -55,56 +119,29 @@ def display_chat_interface():
     col1, col2 = st.columns([4, 1])
     
     with col1:
-        # Text input
         prompt = st.chat_input("Type your message here...")
     
     with col2:
-        # Audio input
         audio = st.audio_input("🎤")
     
     # Process text input
     if prompt:
-        # Add user message to chat history
-        st.session_state.messages.append({"role": "user", "content": prompt})
-        with st.chat_message("user"):
-            st.markdown(prompt)
-            
-        workflow.invoke({"input": prompt})
-        with st.chat_message("assistant"):
-            response = f"Echo: {prompt}"
-            st.markdown(response)
-            st.session_state.messages.append({"role": "assistant", "content": response})
-    # Process audio input
-    elif audio:
-        # Add user audio message to chat history
-        st.session_state.messages.append({"role": "user", "content": "🎤 *Audio message*"})
-        transcribed = False
-        with st.chat_message("user"):
-            st.markdown("🎤 *Audio message*")
-            st.audio(audio)
-            st.markdown("Transcribing audio...")
-            try:
-                transcription = st.session_state.stt.transcribe(audio)
-                st.markdown(transcription)
-                st.session_state.messages.append({"role": "user", "content": transcription})
-                # Transcribe audio
-                transcription = st.session_state.stt.transcribe(audio)
-                transcribed = True
-            except Exception as e:
-                st.error(f"Error transcribing audio: {str(e)}")
-                st.markdown("Failed to transcribe audio, try again")
-        # TODO: Process audio through LangGraph workflow
-            
-        if  transcribed:            
-            st.session_state.messages.append({"role": "user", "content": transcription})
-            st.markdown(transcription)
-
-            # TODO: Process audio through LangGraph workflow
-            # For now, just acknowledge receipt
-            with st.chat_message("assistant"):
-                response = "I received your audio message. Audio processing will be implemented soon!"
-                st.markdown(response)
-                st.session_state.messages.append({"role": "assistant", "content": response})
+        process_chat_message(prompt)
+    elif audio: # Process audio input
+        chat_message_user= st.chat_message("user")
+        chat_message_user.markdown("🎤 *Audio message*")
+        chat_message_user.audio(audio)
+        chat_message_user.markdown("Transcribing audio...")
+        try:
+            transcription = st.session_state.stt.transcribe(audio)
+            print(f"transcription: {transcription}")
+            if transcription and transcription["text"] and len(transcription["text"]) > 0:                    
+                process_chat_message(transcription["text"], is_audio=True)
+            else:
+                chat_message_user.markdown("Transcribing resulted in an empty text, try again")
+        except Exception as e:
+            chat_message_user.error(f"Error transcribing audio: {str(e)}")
+            chat_message_user.markdown("Failed to transcribe audio, try again")
 
 def main():
     initialize_app()

streamlit_app/stt_tts/models.py

@@ -1,22 +1,33 @@
+from io import BytesIO
 import whisper
 import torch
 from TTS.api import TTS
 import torchaudio
 import tempfile
 import os
+from typing import Optional, Dict, Any
 
 torch.classes.__path__ = [] # to silence warning
 
 class STTModel:
-    def __init__(self, model_name = 'small', audio_backend = 'sox_io'):
-        self.model = whisper.load_model(model_name)
-        torchaudio.set_audio_backend(audio_backend)  # or "soundfile" or "ffmpeg"
+    def __init__(self,  model_name: str = 'small', device = 'cpu') -> None:
+        self.model = whisper.load_model(model_name, device=device)
     
-    def transcribe(self, audio_file:BytesIO):
+    def transcribe(self, audio_file: BytesIO) -> Optional[Dict[str, Any]]:
+        """
+        Transcribe audio file to text using Whisper model.
+        
+        Args:
+            audio_file: BytesIO object containing audio data
+            
+        Returns:
+            Dict containing transcription result or None if error occurs
+        """
         with tempfile.NamedTemporaryFile(delete=False) as temp_audio:
             temp_audio.write(audio_file.read())
         # Get the absolute path of the temporary audio file
         audio_file_path = os.path.abspath(temp_audio.name)
+        print(f"Transcribing audio from: {audio_file_path}")
         try:
             transcription = self.model.transcribe(audio_file_path)    
             return transcription
@@ -28,13 +39,14 @@ class STTModel:
 
 class TTSModel:
     def __init__(self, model_name = 'tts_models/en/jenny/jenny', device = 'cpu', audio_backend = 'sox_io'):
+        # print(f"Initializing TTS model with model_name: {model_name}, device: {device}, audio_backend: {audio_backend}")
         self.model = TTS(model_name).to(device)
-        torchaudio.set_audio_backend(audio_backend)  # or "soundfile" or "ffmpeg"
+        self.audio_backend = audio_backend
 
     def tts_to_file(self, text:str):
         with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio:
             temp_audio_path = temp_audio.name
-        self.model.tts_to_file(text=text, file_path=temp_audio_path)
+        self.model.tts_to_file(text=text, file_path=temp_audio_path,audio_backend=self.audio_backend)
         return temp_audio_path