Delete delete.py

delete.py

@@ -1,174 +0,0 @@
-import gradio as gr
-import os
-import warnings
-import asyncio
-from melo.api import TTS
-from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Document, Settings
-from llama_index.llms.cerebras import Cerebras
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-from groq import Groq
-import io
-import nltk
-
-nltk.download('averaged_perceptron_tagger_eng')
-
-# Suppress warnings
-warnings.filterwarnings("ignore", message=".*clean_up_tokenization_spaces.*")
-
-# Global variables
-index = None
-query_engine = None
-
-# Inisialisasi MeloTTS untuk TTS
-device = 'cpu'  # Atur menjadi 'cuda' jika GPU tersedia
-language = 'EN'  # Bahasa default
-model = TTS(language=language, device=device)
-
-# Load Cerebras API key from Hugging Face secrets
-api_key = os.getenv("CEREBRAS_API_KEY")
-if not api_key:
-    raise ValueError("CEREBRAS_API_KEY is not set in Hugging Face Secrets.")
-else:
-    print("Cerebras API key loaded successfully.")
-
-# Initialize Cerebras LLM and embedding model
-os.environ["CEREBRAS_API_KEY"] = api_key
-llm = Cerebras(model="llama-3.3-70b", api_key=os.environ["CEREBRAS_API_KEY"])  # Change model to Llama3.1-70b from Cerebras
-Settings.llm = llm  # Ensure Cerebras is the LLM being used
-embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-mpnet-base-v2")
-
-# Initialize Groq client for Whisper Large V3
-groq_api_key = os.getenv("GROQ_API_KEY")
-if not groq_api_key:
-    raise ValueError("GROQ_API_KEY is not set.")
-else:
-    print("Groq API key loaded successfully.")
-client = Groq(api_key=groq_api_key)  # Groq client initialization
-
-# Function for audio transcription and translation (Whisper Large V3 from Groq)
-def transcribe_or_translate_audio(audio_file, translate=False):
-    """
-    Transcribes or translates audio using Whisper Large V3 via Groq API.
-    """
-    try:
-        with open(audio_file, "rb") as file:
-            if translate:
-                result = client.audio.translations.create(
-                    file=(audio_file, file.read()),
-                    model="whisper-large-v3",  # Use Groq Whisper Large V3
-                    response_format="json",
-                    temperature=0.0
-                )
-                return result.text
-            else:
-                result = client.audio.transcriptions.create(
-                    file=(audio_file, file.read()),
-                    model="whisper-large-v3",  # Use Groq Whisper Large V3
-                    response_format="json",
-                    temperature=0.0
-                )
-                return result.text
-    except Exception as e:
-        return f"Error processing audio: {str(e)}"
-
-# Function to load documents and create index
-def load_documents(file_objs):
-    global index, query_engine
-    try:
-        if not file_objs:
-            return "Error: No files selected."
-
-        documents = []
-        document_names = []
-        for file_obj in file_objs:
-            file_name = os.path.basename(file_obj.name)
-            document_names.append(file_name)
-            loaded_docs = SimpleDirectoryReader(input_files=[file_obj.name]).load_data()
-            for doc in loaded_docs:
-                doc.metadata["source"] = file_name
-                documents.append(doc)
-
-        if not documents:
-            return "No documents found in the selected files."
-
-        index = VectorStoreIndex.from_documents(documents, llm=llm, embed_model=embed_model)
-        query_engine = index.as_query_engine()
-
-        return f"Successfully loaded {len(documents)} documents from the files: {', '.join(document_names)}"
-    except Exception as e:
-        return f"Error loading documents: {str(e)}"
-
-async def perform_rag(query, history, audio_file=None, translate_audio=False):
-    global query_engine
-    if query_engine is None:
-        return history + [("Please load documents first.", None)], None  # Tambahkan None untuk output audio
-
-    try:
-        # Handle audio input jika diberikan
-        if audio_file:
-            transcription = transcribe_or_translate_audio(audio_file, translate=translate_audio)
-            query = f"{query} {transcription}".strip()
-
-        response = await asyncio.to_thread(query_engine.query, query)
-        answer = str(response)  # Dapatkan jawaban dari respons
-
-        # Jika dokumen relevan tersedia, tambahkan sumber tanpa label "Sources"
-        if hasattr(response, "get_documents"):
-            relevant_docs = response.get_documents()
-            if relevant_docs:
-                sources = "\n\n".join([f"{doc.metadata.get('source', 'No source available')}" for doc in relevant_docs])
-            else:
-                sources = ""
-        else:
-            sources = ""
-
-        # Gabungkan jawaban dengan sumber (jika ada) tanpa label tambahan
-        final_result = f"{answer}\n\n{sources}".strip()
-
-        # **Generate audio menggunakan MeloTTS**
-        output_audio_path = "output.wav"
-        model.tts_to_file(answer, model.hps.data.spk2id['EN-US'], output_audio_path, speed=1.0)
-
-        # Kembalikan history yang diperbarui dan file audio
-        return history + [(query, final_result)], output_audio_path
-    except Exception as e:
-        return history + [(query, f"Error processing query: {str(e)}")], None
-
-# Function to clear the session and reset variables
-def clear_all():
-    global index, query_engine
-    index = None
-    query_engine = None
-    return None, "", [], ""  # Reset file input, load output, chatbot, and message input to default states
-
-# Create the Gradio interface
-with gr.Blocks(theme=gr.themes.Base(primary_hue="teal", secondary_hue="teal", neutral_hue="slate")) as demo:
-    gr.Markdown("# RAG Multi-file Chat Application with Speech-to-Text and Text-to-Speech")
-    
-    with gr.Row():
-        file_input = gr.File(label="Select files to load", file_count="multiple")
-        load_btn = gr.Button("Load Documents")
-        load_output = gr.Textbox(label="Load Status")
-
-    msg = gr.Textbox(label="Enter your question")
-    audio_input = gr.Audio(type="filepath", label="Upload Audio")
-    translate_checkbox = gr.Checkbox(label="Translate Audio to English Text", value=False)
-    chatbot = gr.Chatbot()
-    audio_output = gr.Audio(label="Response Audio", type="filepath")  # Tambahkan output audio
-    clear = gr.Button("Clear")
-
-    # Set up event handlers
-    load_btn.click(load_documents, inputs=[file_input], outputs=[load_output])
-
-    # Event handler untuk input teks (proses teks)
-    msg.submit(perform_rag, inputs=[msg, chatbot], outputs=[chatbot, audio_output])  # Tambahkan audio_output
-
-    # Event handler untuk input audio (proses audio)
-    audio_input.change(perform_rag, inputs=[msg, chatbot, audio_input, translate_checkbox], outputs=[chatbot, audio_output])  # Tambahkan audio_output
-
-    clear.click(clear_all, outputs=[file_input, load_output, chatbot, msg], queue=False)
-
-# Run the app
-if __name__ == "__main__":
-    demo.queue()
-    demo.launch()