Update scripts/services/services.py

scripts/services/services.py

@@ -1,89 +1,112 @@
-import wave
-import io
-
-import speech_recognition as sr
-from groq import Groq
-import numpy as np
-from scipy.io.wavfile import write
-import librosa
-
-from langchain_community.vectorstores import FAISS
-from langchain_huggingface import HuggingFaceEmbeddings
-
-import streamlit as st
-
-def downsample_audio(audio_data) : 
-
-    audio_data = audio_data.astype(np.float32) 
-
-    resampled_audio = librosa.resample(audio_data, orig_sr = 96000 , target_sr = 44000)
-
-    write('aud.wav' , 44000 , resampled_audio.astype(np.int16))
-
-
-def save_audio_from_bytes(audio_bytes) : 
-
-    audio_array = np.frombuffer(audio_bytes , dtype = np.int16)
-
-    write('audio.wav' , 96000 , audio_array)
-
-    downsample_audio(audio_array)
-
-def run_groq(prompt , model) :
-
-    if model == 'llama3-70b-8192' : prompt = prompt[: 8191]
-
-    client = Groq(api_key = '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')
-
-    chat_completion = client.chat.completions.create(
-    messages = [
-        {
-            'role' : 'user' ,
-            'content' : prompt
-        }
-    ] , model = model)
-
-    return chat_completion.choices[0].message.content
-
-def transcript() : 
-
-    recognizer = sr.Recognizer()
-
-    with sr.AudioFile('aud.wav') as source : audio = recognizer.record(source)
-        
-    text = recognizer.recognize_google(audio , language = 'es')
-
-    return text
-
-def run_rag(query , vc) : 
-
-    similar_docs = vc.similarity_search(query , k = 10)
-    context = '\n'.join([doc.page_content for doc in similar_docs])
-
-    st.sidebar.write(context)
-
-    prompt = f'''
-Use the following context as your learned knowledge, inside <context></context> XML tags.
-<context>
-    {context}
-</context>
-
-The context is taken from a set of pdfs 
-
-When answer to user:
-- If you don't know, just say that you don't know.
-- If you don't know when you are not sure, ask for clarification.
-Avoid mentioning that you obtained the information from the context.
-And answer according to the language of the user's question.
-Make your answers detailed
-
-- Return yout answer in Spanish
-
-Given the context information, answer the query.
-Query: {query}
-    '''
-
-    response = run_groq(prompt , 'llama3-70b-8192')
-
-    return response
-
+# Importing necessary libraries:
+import wave # Handles WAV audio files
+import io   # Input/Output operations, helps with working with data in memory
+
+# Libraries for speech recognition and language processing:
+import speech_recognition as sr # Speech recognition capabilities
+from groq import Groq # Groq client for interacting with Groq AI platform 
+import numpy as np # Powerful numerical computations (handling audio data)
+from scipy.io.wavfile import write # Writing audio files
+import librosa # Audio analysis (resampling audio)
+
+# Vectorstore and language model interactions:
+from langchain_community.vectorstores import FAISS # Efficient similarity search for documents
+from langchain_huggingface import HuggingFaceEmbeddings # Embeddings for language models
+import streamlit as st # Creating interactive web applications
+
+# Downsample audio from 96kHz to 44kHz (common for many applications)
+def downsample_audio(audio_data):
+    # Convert audio data to suitable format for librosa
+    audio_data = audio_data.astype(np.float32)
+
+    # Resample the audio, changing sample rate from 96000 to 44000
+    resampled_audio = librosa.resample(audio_data, orig_sr=96000, target_sr=44000)
+
+    # Save the resampled audio as a WAV file named 'aud.wav'
+    write('aud.wav', 44000, resampled_audio.astype(np.int16))
+
+# Save audio received as bytes (often from web interfaces) to a file
+def save_audio_from_bytes(audio_bytes):
+    # Convert audio bytes into a NumPy array (easier to work with)
+    audio_array = np.frombuffer(audio_bytes, dtype=np.int16)
+
+    # Save the original (high-sample-rate) audio as 'audio.wav'
+    write('audio.wav', 96000, audio_array)
+
+    # Downsample the audio for further processing
+    downsample_audio(audio_array)
+
+# Interact with Groq AI platform to get responses from a language model
+def run_groq(prompt, model):
+    # Truncate the prompt if using 'llama2-70b-chat' model
+    if model == 'llama3-70b-8192':
+        prompt = prompt[:8191]
+
+    # Create a Groq client (you'll need an API key)
+    client = Groq(api_key='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')
+
+    # Send the prompt to the specified language model on Groq
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {'role': 'user', 'content': prompt}
+        ],
+        model=model
+    )
+
+    # Return the generated response from the model
+    return chat_completion.choices[0].message.content
+
+# Transcribe audio from the 'aud.wav' file (assumes Spanish language)
+def transcript():
+    # Create a speech recognizer object
+    recognizer = sr.Recognizer()
+
+    # Open the audio file 'aud.wav'
+    with sr.AudioFile('aud.wav') as source:
+        # Read the audio data from the file
+        audio = recognizer.record(source)
+
+    # Use Google's speech recognition to transcribe the audio (Spanish)
+    text = recognizer.recognize_google(audio, language='es')
+
+    # Return the transcribed text
+    return text
+
+# Perform Retrieval Augmented Generation (RAG) to answer queries using context
+def run_rag(query, vc):  # 'vc' is assumed to be a Vectorstore
+    # Find similar documents in the Vectorstore based on the query
+    similar_docs = vc.similarity_search(query, k=10)  # Get top 10 similar docs
+
+    # Combine the content of these similar documents into a single context
+    context = '\n'.join([doc.page_content for doc in similar_docs])
+
+    # Display the context in the Streamlit sidebar
+    st.sidebar.write(context)
+
+    # Construct a prompt for the language model, including the context
+    prompt = f'''
+Use the following context as your learned knowledge, inside <context></context> XML tags.
+<context>
+    {context}
+</context>
+
+The context is taken from a set of pdfs 
+
+When answer to user:
+- If you don't know, just say that you don't know.
+- If you don't know when you are not sure, ask for clarification.
+Avoid mentioning that you obtained the information from the context.
+And answer according to the language of the user's question.
+Make your answers detailed
+
+- Return yout answer in Spanish
+
+Given the context information, answer the query.
+Query: {query}
+    '''
+
+    # Get a response from the 'llama2-70b-chat' model on Groq
+    response = run_groq(prompt, 'llama3-70b-8192')
+
+    # Return the generated response
+    return response