PesaQ-api

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kamau1 commited on Aug 24, 2023

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3d22ea4

1 Parent(s): f451dad

Update modules/app.py

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modules/app.py +71 -98

modules/app.py CHANGED Viewed

@@ -1,118 +1,91 @@
 '''
         Created By Lewis Kamau Kimaru
-        Sema fastapi backend
         August 2023
 '''
-from fastapi import FastAPI, HTTPException, Request
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import HTMLResponse
-import gradio as gr
-import ctranslate2
-import sentencepiece as spm
-import fasttext
-import uvicorn
 from pyngrok import ngrok
 import os
-#import nest_asyncio
-app = FastAPI()
 # Set your ngrok authtoken
-#ngrok.set_auth_token("2UAhCqf5zP0cCgJzeadNANkbIqx_7ZJvhkDSNWccqMX2hyxXP")
 #ngrok.set_auth_token("2S6xeFEoSVFWr2egtDRcqgeUtSx_2juefHFkEW6nGbpRHS37W")
 #ngrok.set_auth_token("2UAmdjHdAFV9x84TdyEknIfNhYk_4Ye8n4YK7ZhfCMob3yPBh")
 #ngrok.set_auth_token("2UAqm26HuWiWvQjzK58xYufSGpy_6tStKSyLLyR9f7pcezh6R")
 #ngrok.set_auth_token("2UGQqzZoI3bx7SSk8H4wuFC3iaC_2WniWyNAsW5fd2rFyKVq1")
-ngrok.set_auth_token("2UISOtStHwytO70NQK38dFhS1at_5opQaXnoQCKeyhEe4qfT2")
-fasttext.FastText.eprint = lambda x: None
-# Load the model and tokenizer ..... only once!
-beam_size = 1  # change to a smaller value for faster inference
-device = "cpu"  # or "cuda"
-# Language Prediction model
-print("\nimporting Language Prediction model")
-lang_model_file = "lid218e.bin"
-lang_model_full_path = os.path.join(os.path.dirname(__file__), lang_model_file)
-lang_model = fasttext.load_model(lang_model_full_path)
-# Load the source SentencePiece model
-print("\nimporting SentencePiece model")
-sp_model_file = "spm.model"
-sp_model_full_path = os.path.join(os.path.dirname(__file__), sp_model_file)
-sp = spm.SentencePieceProcessor()
-sp.load(sp_model_full_path)
-# Import The Translator model
-print("\nimporting Translator model")
-ct_model_file = "sematrans-3.3B"
-ct_model_full_path = os.path.join(os.path.dirname(__file__), ct_model_file)
-translator = ctranslate2.Translator(ct_model_full_path, device)
-print('\nDone importing models\n')
-def translate_text(userinput: str, target_lang: str):
-    source_sents = [userinput]
-    source_sents = [sent.strip() for sent in source_sents]
-    target_prefix = [[target_lang]] * len(source_sents)
-    # Predict the source language
-    predictions = lang_model.predict(source_sents[0], k=1)
-    source_lang = predictions[0][0].replace('__label__', '')
-    # Subword the source sentences
-    source_sents_subworded = sp.encode(source_sents, out_type=str)
-    source_sents_subworded = [[source_lang] + sent + ["</s>"] for sent in source_sents_subworded]
-    # Translate the source sentences
-    translations = translator.translate_batch(
-        source_sents_subworded,
-        batch_type="tokens",
-        max_batch_size=2024,
-        beam_size=beam_size,
-        target_prefix=target_prefix,
     )
-    translations = [translation[0]['tokens'] for translation in translations]
-    # Desubword the target sentences
-    translations_desubword = sp.decode(translations)
-    translations_desubword = [sent[len(target_lang):] for sent in translations_desubword]
-    # Return the source language and the translated text
-    return source_lang, translations_desubword
-@app.get("/")
-def read_root():
-    return {"message": "Welcome to the Sema Translation API! \nThis API was created by Lewsi Kamau Kimaru"}
-@app.post("/translate/")
-async def translate_endpoint(request: Request):
-    data = await request.json()
-    userinput = data.get("userinput")
-    target_lang = data.get("target_lang")
-    print(f"\n Target Language; {target_lang}, User Input: {userinput}\n")
-    if not userinput or not target_lang:
-        raise HTTPException(status_code=422, detail="Both 'userinput' and 'target_lang' are required.")
-    source_lang, translated_text = translate_text(userinput, target_lang)
-    print(f"\nsource_language: {source_lang}, Translated Text: {translated_text}\n\n")
-    return {
-        "source_language": source_lang,
-        "translated_text": translated_text[0],
-    }
 ngrok_tunnel = ngrok.connect(7860)
 public_url = ngrok_tunnel.public_url
 print('\nPublic URL✅:', public_url)
-#nest_asyncio.apply()
-print("\nAPI starting .......\n")
-#uvicorn.run(app, port=7860)

 '''
         Created By Lewis Kamau Kimaru
+        https://towardsdev.com/building-a-voice-assistant-using-openai-api-and-flask-by-chatgpt-9f90a430b242
+        https://github.com/prathmeshChaudhari05/Voice-Assistant-Flask
         August 2023
 '''
+from flask import Flask, render_template, request, redirect, url_for, send_from_directory
+from playsound import playsound
+import speech_recognition as sr
 from pyngrok import ngrok
+from gtts import gTTS
+import openai
 import os
+app = Flask(__name__)
 # Set your ngrok authtoken
+ngrok.set_auth_token("2UAhCqf5zP0cCgJzeadNANkbIqx_7ZJvhkDSNWccqMX2hyxXP")
 #ngrok.set_auth_token("2S6xeFEoSVFWr2egtDRcqgeUtSx_2juefHFkEW6nGbpRHS37W")
 #ngrok.set_auth_token("2UAmdjHdAFV9x84TdyEknIfNhYk_4Ye8n4YK7ZhfCMob3yPBh")
 #ngrok.set_auth_token("2UAqm26HuWiWvQjzK58xYufSGpy_6tStKSyLLyR9f7pcezh6R")
 #ngrok.set_auth_token("2UGQqzZoI3bx7SSk8H4wuFC3iaC_2WniWyNAsW5fd2rFyKVq1")
+#ngrok.set_auth_token("2UISOtStHwytO70NQK38dFhS1at_5opQaXnoQCKeyhEe4qfT2")
+# Set up OpenAI API credentials
+openai.api_key = 'YOUR_API_KEY'
+@app.route('/')
+def home():
+    html_content = """
+    <!DOCTYPE html>
+<html>
+  <head>
+    <title>Voice Assistant</title>
+  </head>
+  <body>
+    <h1>Voice Assistant</h1>
+    <form method="POST">
+      <button type="submit">Ask me something!</button>
+    </form>
+    <audio controls>
+      <source src="{{ url_for('static', filename='response.mp3') }}" type="audio/mpeg">
+    </audio>
+  </body>
+</html>
+    """
+    return html_content
+def handle_form():
+    r = sr.Recognizer()
+    with sr.Microphone() as source:
+        print("Listening...")
+        audio = r.listen(source)
+try:
+    result = r.recognize_google(audio)
+    print("result2:")
+    print(r.recognize_google(audio, show_all=True))
+    response = openai.Completion.create(
+        engine="davinci",
+        prompt=result,
+        max_tokens=60,
+        n=1,
+        stop=None,
+        temperature=0.5,
     )
+    if response.choices:
+        tts = gTTS(text=response.choices[0].text, lang='en')
+    else:
+        tts = gTTS(text="I'm sorry, I didn't understand what you said", lang='en')
+    filename = 'response.mp3'
+    tts.save(filename)
+    playsound(filename)
+    os.remove(filename)
+except sr.UnknownValueError:
+    print("Google Speech Recognition could not understand audio")
+except sr.RequestError as e:
+    print("Could not request results from Google Speech Recognition service; {0}".format(e))
+@app.route('/', methods=['POST'])
+def submit_textarea():
+    return handle_form()
 ngrok_tunnel = ngrok.connect(7860)
 public_url = ngrok_tunnel.public_url
 print('\nPublic URL✅:', public_url)
+print("\nFlask APP starting .......\n")