Update app.py

app.py

@@ -1,182 +1,182 @@
-import os
-import re
-import soundfile as sf
-import torch
-import torchaudio
-import torchaudio.transforms as T
-from datasets import load_dataset
-from transformers import WhisperForConditionalGeneration, WhisperProcessor, SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan, AutoModel
-from langchain.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import FAISS
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain, StuffDocumentsChain, RetrievalQA
-from langchain.llms import LlamaCpp
-import gradio as gr
-
-class PDFProcessor:
-    def __init__(self, pdf_path):
-        self.pdf_path = pdf_path
-
-    def load_and_split_pdf(self):
-        loader = PyPDFLoader(self.pdf_path)
-        documents = loader.load()
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=20)
-        docs = text_splitter.split_documents(documents)
-        return docs
-
-class FAISSManager:
-    def __init__(self):
-        self.vectorstore_cache = {}
-
-    def build_faiss_index(self, docs):
-        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-        vectorstore = FAISS.from_documents(docs, embeddings)
-        return vectorstore
-
-    def save_faiss_index(self, vectorstore, file_path):
-        vectorstore.save_local(file_path)
-        print(f"Vectorstore saved to {file_path}")
-
-    def load_faiss_index(self, file_path):
-        if not os.path.exists(f"{file_path}/index.faiss") or not os.path.exists(f"{file_path}/index.pkl"):
-            raise FileNotFoundError(f"Could not find FAISS index or metadata files in {file_path}")
-        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-        vectorstore = FAISS.load_local(file_path, embeddings, allow_dangerous_deserialization=True)
-        print(f"Vectorstore loaded from {file_path}")
-        return vectorstore
-
-    def build_faiss_index_with_cache_and_file(self, pdf_processor, vectorstore_path):
-        if os.path.exists(vectorstore_path):
-            print(f"Loading vectorstore from file {vectorstore_path}")
-            return self.load_faiss_index(vectorstore_path)
-
-        print(f"Building new vectorstore for {pdf_processor.pdf_path}")
-        docs = pdf_processor.load_and_split_pdf()
-        vectorstore = self.build_faiss_index(docs)
-        self.save_faiss_index(vectorstore, vectorstore_path)
-        return vectorstore
-
-class LLMChainFactory:
-    def __init__(self, prompt_template):
-        self.prompt_template = prompt_template
-
-    def create_llm_chain(self, llm, max_tokens=80):
-        prompt = PromptTemplate(template=self.prompt_template, input_variables=["documents", "question"])
-        llm_chain = LLMChain(llm=llm, prompt=prompt)
-        llm_chain.llm.max_tokens = max_tokens
-        combine_documents_chain = StuffDocumentsChain(
-            llm_chain=llm_chain,
-            document_variable_name="documents"
-        )
-        return combine_documents_chain
-
-class LLMManager:
-    def __init__(self, model_path):
-        self.llm = LlamaCpp(model_path=model_path)
-        self.llm.max_tokens = 80
-
-    def create_rag_chain(self, llm_chain_factory, vectorstore):
-        retriever = vectorstore.as_retriever()
-        combine_documents_chain = llm_chain_factory.create_llm_chain(self.llm)
-        qa_chain = RetrievalQA(combine_documents_chain=combine_documents_chain, retriever=retriever)
-        return qa_chain
-
-    def main_rag_pipeline(self, pdf_processor, query, vectorstore_manager, vectorstore_file):
-        vectorstore = vectorstore_manager.build_faiss_index_with_cache_and_file(pdf_processor, vectorstore_file)
-        llm_chain_factory = LLMChainFactory(prompt_template="""You are a helpful AI. Based on the context below, answer the question politely.
-        Context: {documents}
-        Question: {question}
-        Answer:""")
-        rag_chain = self.create_rag_chain(llm_chain_factory, vectorstore)
-        result = rag_chain.run(query)
-        return result
-
-class WhisperManager:
-    def __init__(self):
-        self.model_id = "openai/whisper-small"
-        self.whisper_model = WhisperForConditionalGeneration.from_pretrained(self.model_id)
-        self.whisper_processor = WhisperProcessor.from_pretrained(self.model_id)
-        self.forced_decoder_ids = self.whisper_processor.get_decoder_prompt_ids(language="english", task="transcribe")
-
-    def transcribe_speech(self, filepath):
-        if not os.path.isfile(filepath):
-            raise ValueError(f"Invalid file path: {filepath}")
-        waveform, sample_rate = torchaudio.load(filepath)
-        target_sample_rate = 16000
-        if sample_rate != target_sample_rate:
-            resampler = T.Resample(orig_freq=sample_rate, new_freq=target_sample_rate)
-            waveform = resampler(waveform)
-        input_features = self.whisper_processor(waveform.squeeze(), sampling_rate=target_sample_rate, return_tensors="pt").input_features
-        generated_ids = self.whisper_model.generate(input_features, forced_decoder_ids=self.forced_decoder_ids)
-        transcribed_text = self.whisper_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-        cleaned_text = re.sub(r"<[^>]*>", "", transcribed_text).strip()
-        return cleaned_text
-
-class SpeechT5Manager:
-    def __init__(self):
-        self.SpeechT5_processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
-        self.SpeechT5_model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
-        self.vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
-        self.speaker_embedding_model = AutoModel.from_pretrained("microsoft/speecht5_vc")
-        embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
-        self.pretrained_speaker_embeddings = torch.tensor(embeddings_dataset[7000]["xvector"]).unsqueeze(0)
-
-    def text_to_speech(self, text, output_file="output_speechT5.wav"):
-        inputs = self.SpeechT5_processor(text=[text], return_tensors="pt")
-        speech = self.SpeechT5_model.generate_speech(inputs["input_ids"], self.pretrained_speaker_embeddings, vocoder=self.vocoder)
-        sf.write(output_file, speech.numpy(), 16000)
-        return output_file
-
-# --- Gradio Interface ---
-def asr_to_text(audio_file):
-    transcribed_text = whisper_manager.transcribe_speech(audio_file)
-    return transcribed_text
-
-def process_with_llm_and_tts(transcribed_text):
-    response_text = llm_manager.main_rag_pipeline(pdf_processor, transcribed_text, vectorstore_manager, vectorstore_file)
-    audio_output = speech_manager.text_to_speech(response_text)
-    return response_text, audio_output
-
-# Instantiate Managers
-pdf_processor = PDFProcessor('./files/LawsoftheGame2024_25.pdf')
-vectorstore_manager = FAISSManager()
-llm_manager = LLMManager(model_path="./files/mistral-7b-instruct-v0.2.Q2_K.gguf")
-whisper_manager = WhisperManager()
-speech_manager = SpeechT5Manager()
-vectorstore_file = "./vectorstore_faiss"
-
-# Define Gradio Interface
-with gr.Blocks() as demo:
-    gr.Markdown("<h1 style='text-align: center;'>:robot: RAG Powered Voice Assistant :robot:</h1>")
-    gr.Markdown("<h1 style='text-align: center;'>Ask me anything about the rules of Football!</h1>")
-
-    # Step 1: Audio input and ASR output
-    with gr.Row():
-        audio_input = gr.Audio(type="filepath", label="Speak your question")
-        asr_output = gr.Textbox(label="ASR Output (Edit if necessary)", interactive=True)
-
-    # Button to process audio (ASR)
-    asr_button = gr.Button("1 - Transform Voice to Text")
-
-    # Step 2: LLM Response and TTS output
-    with gr.Row():
-        llm_response = gr.Textbox(label="LLM Response")
-        tts_audio_output = gr.Audio(label="TTS Audio")
-
-    # Button to process text with LLM
-    llm_button = gr.Button("2 - Submit Question")
-
-    # When ASR button is clicked, the audio is transcribed
-    asr_button.click(fn=asr_to_text, inputs=audio_input, outputs=asr_output)
-
-    # When LLM button is clicked, the text is processed with the LLM and converted to speech
-    llm_button.click(fn=process_with_llm_and_tts, inputs=asr_output, outputs=[llm_response, tts_audio_output])
-
-    # Disclaimer
-    gr.Markdown(
-        "<p style='text-align: center; color: gray;'>Disclaimer: This application was developed solely for educational purposes to demonstrate AI capabilities and should not be used as a source of information or for any other purpose.</p>"
-    )
-
+import os
+import re
+import soundfile as sf
+import torch
+import torchaudio
+import torchaudio.transforms as T
+from datasets import load_dataset
+from transformers import WhisperForConditionalGeneration, WhisperProcessor, SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan, AutoModel
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain, StuffDocumentsChain, RetrievalQA
+from langchain.llms import LlamaCpp
+import gradio as gr
+
+class PDFProcessor:
+    def __init__(self, pdf_path):
+        self.pdf_path = pdf_path
+
+    def load_and_split_pdf(self):
+        loader = PyPDFLoader(self.pdf_path)
+        documents = loader.load()
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=20)
+        docs = text_splitter.split_documents(documents)
+        return docs
+
+class FAISSManager:
+    def __init__(self):
+        self.vectorstore_cache = {}
+
+    def build_faiss_index(self, docs):
+        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+        vectorstore = FAISS.from_documents(docs, embeddings)
+        return vectorstore
+
+    def save_faiss_index(self, vectorstore, file_path):
+        vectorstore.save_local(file_path)
+        print(f"Vectorstore saved to {file_path}")
+
+    def load_faiss_index(self, file_path):
+        if not os.path.exists(f"{file_path}/index.faiss") or not os.path.exists(f"{file_path}/index.pkl"):
+            raise FileNotFoundError(f"Could not find FAISS index or metadata files in {file_path}")
+        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+        vectorstore = FAISS.load_local(file_path, embeddings, allow_dangerous_deserialization=True)
+        print(f"Vectorstore loaded from {file_path}")
+        return vectorstore
+
+    def build_faiss_index_with_cache_and_file(self, pdf_processor, vectorstore_path):
+        if os.path.exists(vectorstore_path):
+            print(f"Loading vectorstore from file {vectorstore_path}")
+            return self.load_faiss_index(vectorstore_path)
+
+        print(f"Building new vectorstore for {pdf_processor.pdf_path}")
+        docs = pdf_processor.load_and_split_pdf()
+        vectorstore = self.build_faiss_index(docs)
+        self.save_faiss_index(vectorstore, vectorstore_path)
+        return vectorstore
+
+class LLMChainFactory:
+    def __init__(self, prompt_template):
+        self.prompt_template = prompt_template
+
+    def create_llm_chain(self, llm, max_tokens=80):
+        prompt = PromptTemplate(template=self.prompt_template, input_variables=["documents", "question"])
+        llm_chain = LLMChain(llm=llm, prompt=prompt)
+        llm_chain.llm.max_tokens = max_tokens
+        combine_documents_chain = StuffDocumentsChain(
+            llm_chain=llm_chain,
+            document_variable_name="documents"
+        )
+        return combine_documents_chain
+
+class LLMManager:
+    def __init__(self, model_path):
+        self.llm = LlamaCpp(model_path=model_path)
+        self.llm.max_tokens = 80
+
+    def create_rag_chain(self, llm_chain_factory, vectorstore):
+        retriever = vectorstore.as_retriever()
+        combine_documents_chain = llm_chain_factory.create_llm_chain(self.llm)
+        qa_chain = RetrievalQA(combine_documents_chain=combine_documents_chain, retriever=retriever)
+        return qa_chain
+
+    def main_rag_pipeline(self, pdf_processor, query, vectorstore_manager, vectorstore_file):
+        vectorstore = vectorstore_manager.build_faiss_index_with_cache_and_file(pdf_processor, vectorstore_file)
+        llm_chain_factory = LLMChainFactory(prompt_template="""You are a helpful AI. Based on the context below, answer the question politely.
+        Context: {documents}
+        Question: {question}
+        Answer:""")
+        rag_chain = self.create_rag_chain(llm_chain_factory, vectorstore)
+        result = rag_chain.run(query)
+        return result
+
+class WhisperManager:
+    def __init__(self):
+        self.model_id = "openai/whisper-small"
+        self.whisper_model = WhisperForConditionalGeneration.from_pretrained(self.model_id)
+        self.whisper_processor = WhisperProcessor.from_pretrained(self.model_id)
+        self.forced_decoder_ids = self.whisper_processor.get_decoder_prompt_ids(language="english", task="transcribe")
+
+    def transcribe_speech(self, filepath):
+        if not os.path.isfile(filepath):
+            raise ValueError(f"Invalid file path: {filepath}")
+        waveform, sample_rate = torchaudio.load(filepath)
+        target_sample_rate = 16000
+        if sample_rate != target_sample_rate:
+            resampler = T.Resample(orig_freq=sample_rate, new_freq=target_sample_rate)
+            waveform = resampler(waveform)
+        input_features = self.whisper_processor(waveform.squeeze(), sampling_rate=target_sample_rate, return_tensors="pt").input_features
+        generated_ids = self.whisper_model.generate(input_features, forced_decoder_ids=self.forced_decoder_ids)
+        transcribed_text = self.whisper_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        cleaned_text = re.sub(r"<[^>]*>", "", transcribed_text).strip()
+        return cleaned_text
+
+class SpeechT5Manager:
+    def __init__(self):
+        self.SpeechT5_processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+        self.SpeechT5_model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
+        self.vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+        self.speaker_embedding_model = AutoModel.from_pretrained("microsoft/speecht5_vc")
+        embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+        self.pretrained_speaker_embeddings = torch.tensor(embeddings_dataset[7000]["xvector"]).unsqueeze(0)
+
+    def text_to_speech(self, text, output_file="output_speechT5.wav"):
+        inputs = self.SpeechT5_processor(text=[text], return_tensors="pt")
+        speech = self.SpeechT5_model.generate_speech(inputs["input_ids"], self.pretrained_speaker_embeddings, vocoder=self.vocoder)
+        sf.write(output_file, speech.numpy(), 16000)
+        return output_file
+
+# --- Gradio Interface ---
+def asr_to_text(audio_file):
+    transcribed_text = whisper_manager.transcribe_speech(audio_file)
+    return transcribed_text
+
+def process_with_llm_and_tts(transcribed_text):
+    response_text = llm_manager.main_rag_pipeline(pdf_processor, transcribed_text, vectorstore_manager, vectorstore_file)
+    audio_output = speech_manager.text_to_speech(response_text)
+    return response_text, audio_output
+
+# Instantiate Managers
+pdf_processor = PDFProcessor('./files/LawsoftheGame2024_25.pdf')
+vectorstore_manager = FAISSManager()
+llm_manager = LLMManager(model_path="./files/mistral-7b-instruct-v0.2.Q2_K.gguf")
+whisper_manager = WhisperManager()
+speech_manager = SpeechT5Manager()
+vectorstore_file = "./vectorstore_faiss"
+
+# Define Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("<h1 style='text-align: center;'>RAG Powered Voice Assistant</h1>") #removed emojis
+    gr.Markdown("<h1 style='text-align: center;'>Ask me anything about the rules of Football!</h1>")
+
+    # Step 1: Audio input and ASR output
+    with gr.Row():
+        audio_input = gr.Audio(type="filepath", label="Speak your question")
+        asr_output = gr.Textbox(label="ASR Output (Edit if necessary)", interactive=True)
+
+    # Button to process audio (ASR)
+    asr_button = gr.Button("1 - Transform Voice to Text")
+
+    # Step 2: LLM Response and TTS output
+    with gr.Row():
+        llm_response = gr.Textbox(label="LLM Response")
+        tts_audio_output = gr.Audio(label="TTS Audio")
+
+    # Button to process text with LLM
+    llm_button = gr.Button("2 - Submit Question")
+
+    # When ASR button is clicked, the audio is transcribed
+    asr_button.click(fn=asr_to_text, inputs=audio_input, outputs=asr_output)
+
+    # When LLM button is clicked, the text is processed with the LLM and converted to speech
+    llm_button.click(fn=process_with_llm_and_tts, inputs=asr_output, outputs=[llm_response, tts_audio_output])
+
+    # Disclaimer
+    gr.Markdown(
+        "<p style='text-align: center; color: gray;'>Disclaimer: This application was developed solely for educational purposes to demonstrate AI capabilities and should not be used as a source of information or for any other purpose.</p>"
+    )
+
 demo.launch(debug=True)