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| import streamlit as st | |
| import requests | |
| import base64 | |
| import os | |
| import llama_index | |
| from audio_recorder_streamlit import audio_recorder | |
| from openai import OpenAI | |
| from llama_index import VectorStoreIndex, SimpleDirectoryReader | |
| os.environ['OPENAI_API_KEY'] = os.getenv("apikey") | |
| API_KEY = os.getenv("apikey") | |
| def RAG(text): | |
| documents = SimpleDirectoryReader("db3").load_data() | |
| index = VectorStoreIndex.from_documents(documents) | |
| query_engine = index.as_query_engine() | |
| response = query_engine.query(text) | |
| # Extract the text from the response | |
| response_text = response.response if hasattr(response, 'response') else str(response) | |
| return response_text | |
| def linkRAGhindi(text): | |
| new_prompt="निम्नलिखित प्रश्न के लिए सबसे उपयुक्त वेबसाइट लिंक दें"+text | |
| documents = SimpleDirectoryReader("db1").load_data() | |
| index = VectorStoreIndex.from_documents(documents) | |
| query_engine = index.as_query_engine() | |
| response = query_engine.query(new_prompt) | |
| # Extract the text from the response | |
| response_text = response.response if hasattr(response, 'response') else str(response) | |
| return response_text | |
| def rechindi(text): | |
| new_prompt="निम्नलिखित प्रश्न के लिए सबसे उपयुक्त वेबसाइट लिंक दें"+text | |
| documents = SimpleDirectoryReader("db2").load_data() | |
| index = VectorStoreIndex.from_documents(documents) | |
| query_engine = index.as_query_engine() | |
| response = query_engine.query(new_prompt) | |
| # Extract the text from the response | |
| response_text = response.response if hasattr(response, 'response') else str(response) | |
| return response_text | |
| def linkRAGenglish(text): | |
| new_prompt="Give the most appropiate website link for the following question "+text | |
| documents = SimpleDirectoryReader("db1").load_data() | |
| index = VectorStoreIndex.from_documents(documents) | |
| query_engine = index.as_query_engine() | |
| response = query_engine.query(new_prompt) | |
| # Extract the text from the response | |
| response_text = response.response if hasattr(response, 'response') else str(response) | |
| return response_text | |
| def recenglish(text): | |
| new_prompt="Give the most intresting other website link for the following question "+text | |
| documents = SimpleDirectoryReader("db2").load_data() | |
| index = VectorStoreIndex.from_documents(documents) | |
| query_engine = index.as_query_engine() | |
| response = query_engine.query(new_prompt) | |
| # Extract the text from the response | |
| response_text = response.response if hasattr(response, 'response') else str(response) | |
| return response_text | |
| def transcribe_text_to_voice_english(audio_location): | |
| client = OpenAI(api_key=API_KEY) | |
| audio_file = open(audio_location, "rb") | |
| transcript = client.audio.transcriptions.create(model="whisper-1", file=audio_file) | |
| return transcript.text | |
| def transcribe_text_to_voice_hindi(audio_location): | |
| url = "https://api.runpod.ai/v2/faster-whisper/runsync" | |
| with open(audio_location, "rb") as audio_file: | |
| audio_base64 = base64.b64encode(audio_file.read()).decode('utf-8') | |
| payload = { | |
| "input": { | |
| "audio_base64": audio_base64, | |
| "model": "small", | |
| "transcription": "plain_text", | |
| "translate": True, | |
| "language": "hi", | |
| "temperature": 0, | |
| "best_of": 5, | |
| "beam_size": 5, | |
| "patience": 1, | |
| "suppress_tokens": "-1", | |
| "condition_on_previous_text": False, | |
| "temperature_increment_on_fallback": 0.2, | |
| "compression_ratio_threshold": 2.4, | |
| "logprob_threshold": -1, | |
| "no_speech_threshold": 0.6, | |
| "word_timestamps": False | |
| }, | |
| "enable_vad": False | |
| } | |
| headers = { | |
| "accept": "application/json", | |
| "content-type": "application/json", | |
| "authorization": "X01PG949AHTOVRYHLQZKSRIWN82UHBUU5JYLNAHM" | |
| } | |
| response = requests.post(url, json=payload, headers=headers) | |
| response_json = response.json() | |
| transcription = response_json["output"]["transcription"] | |
| translation = response_json["output"]["translation"].strip().split('\n')[-1].strip() | |
| return transcription, translation | |
| def recommendation(text): | |
| client = OpenAI(api_key=API_KEY) | |
| messages = [{"role": "user", "content": text}] | |
| response = client.chat.completions.create(model="gpt-3.5-turbo-1106", messages=messages) | |
| return response.choices[0].message.content | |
| def text_to_speech_ai(speech_file_path, api_response): | |
| client = OpenAI(api_key=API_KEY) | |
| response = client.audio.speech.create(model="tts-1",voice="nova",input=api_response) | |
| response.stream_to_file(speech_file_path) | |
| st.title("🚀 SHRESHTH 💬 Bhuvan Assistant") | |
| # Radio wheel for language selection | |
| language = st.radio("Language/भाषा",["English", "हिंदी"]) | |
| # Displaying description based on selected language | |
| if language == "English": | |
| mode = st.radio("Select Mode Of Input", ["Voice","Text"]) | |
| st.write("Smart - Helpful - Robust - Effortless - System for Text-to-speech and Human-like Assistance") | |
| if mode == "Voice" or mode == "आवाज": | |
| st.write("Click on the voice recorder and let me know how I can help you today with your Queries Regarding Bhuvan!") | |
| audio_bytes = audio_recorder( | |
| text="", | |
| recording_color="#e8b62c", | |
| neutral_color="#6aa36f", | |
| icon_name="microphone", | |
| icon_size="2x", | |
| ) | |
| if audio_bytes: | |
| # Save the Recorded File | |
| audio_location = "audio_file.wav" | |
| with open(audio_location, "wb") as f: | |
| f.write(audio_bytes) | |
| if language == "English": | |
| text=transcribe_text_to_voice_english(audio_location) | |
| st.write(text) | |
| else: | |
| text,trans=transcribe_text_to_voice_hindi(audio_location) | |
| st.write(text) | |
| link_response = linkRAGenglish(text) | |
| st.write("SHRESHTH:", link_response) | |
| api_response = RAG(text) | |
| st.write("SHRESHTH:", api_response) | |
| speech_file_path = 'audio_response.mp3' | |
| text_to_speech_ai(speech_file_path, api_response) | |
| st.audio(speech_file_path) | |
| recctext="recommend top three other websites that could interest the user depending on this link and answer : " + link_response + api_response | |
| recc=linkRAGenglish(recctext) | |
| st.write("SHRESHTH:", recc) | |
| else: | |
| # Text input option | |
| text_input = st.text_area("Enter your text here and press Enter", "") | |
| if st.button("Submit"): | |
| # Process the entered text | |
| link_response = linkRAGenglish(text_input) | |
| st.write("SHRESHTH:", link_response) | |
| api_response = RAG(text_input) | |
| st.write("SHRESHTH:", api_response) | |
| # Read out the text response using tts | |
| speech_file_path = 'audio_response.mp3' | |
| text_to_speech_ai(speech_file_path, api_response) | |
| st.audio(speech_file_path) | |
| recctext="recommend top three other websites that could interest the user depending on this link and answer : " + link_response + api_response | |
| recc=linkRAGenglish(recctext) | |
| st.write("SHRESHTH:", recc) | |
| else: | |
| mode = st.radio("इनपुट मोड का चयन करें", ["आवाज", "टेक्स्ट"]) | |
| st.write("स्मार्ट - सहायक - मजबूत - प्रयासहीन - पाठ-से-बोल के लिए एक सिस्टम और मानव जैसी सहायता") | |
| if mode == "Voice" or mode == "आवाज" or mode == "ভয়েস": | |
| st.write("आवाज रेकॉर्डर पर क्लिक करें और मुझसे यह बताएं कि आज आपकी भुवन से संबंधित सवालों में मैं आपकी कैसे मदद कर सकता हूँ!") | |
| audio_bytes = audio_recorder( | |
| text="", | |
| recording_color="#e8b62c", | |
| neutral_color="#6aa36f", | |
| icon_name="microphone", | |
| icon_size="2x", | |
| ) | |
| if audio_bytes: | |
| # Save the Recorded File | |
| audio_location = "audio_file.wav" | |
| with open(audio_location, "wb") as f: | |
| f.write(audio_bytes) | |
| if language == "English": | |
| text=transcribe_text_to_voice_english(audio_location) | |
| st.write(text) | |
| else: | |
| text,trans=transcribe_text_to_voice_hindi(audio_location) | |
| st.write(text) | |
| link_response = linkRAGhindi(text) | |
| st.write("श्रेष्ठ:", link_response) | |
| api_response = RAG(text) | |
| st.write("श्रेष्ठ:", api_response) | |
| # Read out the text response using tts | |
| speech_file_path = 'audio_response.mp3' | |
| text_to_speech_ai(speech_file_path, api_response) | |
| st.audio(speech_file_path) | |
| recctext="recommend top three other websites that could interest the user depending on this link and answer : " + link_response + api_response | |
| recc=rechindi(recctext) | |
| st.write("श्रेष्ठ:", recc) | |
| else: | |
| # Text input option | |
| text_input = st.text_area("आप यहाँ अपना टेक्स्ट दर्ज करें और एंटर दबाएं", "") | |
| if st.button("एंटर"): | |
| # Process the entered text | |
| link_response = linkRAGhindi(text_input) | |
| st.write("श्रेष्ठ:", link_response) | |
| api_response = RAG(text_input) | |
| st.write("श्रेष्ठ:", api_response) | |
| # Read out the text response using tts | |
| speech_file_path = 'audio_response.mp3' | |
| text_to_speech_ai(speech_file_path, api_response) | |
| st.audio(speech_file_path) | |
| recctext="recommend top three other websites that could interest the user depending on this link and answer : " + link_response + api_response | |
| recc=rechindi(recctext) | |
| st.write("श्रेष्ठ:", recc) | |