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| import streamlit as st | |
| import speech_recognition as sr | |
| from langchain.llms import HuggingFaceHub | |
| from langchain.prompts import PromptTemplate | |
| from langchain.chains import LLMChain | |
| # Initialize Hugging Face model (you can use other models based on your needs) | |
| llm = HuggingFaceHub( | |
| repo_id="gpt2", # You can replace with other models on Hugging Face | |
| model_kwargs={"temperature": 0.7} | |
| ) | |
| # Define the prompt template for translation or text generation | |
| prompt_template = "Translate the following text to Urdu: {text}" | |
| prompt = PromptTemplate(input_variables=["text"], template=prompt_template) | |
| # Set up the chain | |
| llm_chain = LLMChain(prompt=prompt, llm=llm) | |
| # Function to process text using LangChain | |
| def process_text(input_text): | |
| result = llm_chain.run(input_text) | |
| return result | |
| # Function to recognize speech input | |
| def recognize_speech(): | |
| recognizer = sr.Recognizer() | |
| with sr.Microphone() as source: | |
| st.write("Please speak now...") | |
| audio = recognizer.listen(source) | |
| try: | |
| text = recognizer.recognize_google(audio, language="en-US") | |
| st.write(f"Recognized Text: {text}") | |
| return text | |
| except sr.UnknownValueError: | |
| st.error("Sorry, I could not understand the audio.") | |
| return "" | |
| except sr.RequestError: | |
| st.error("Could not request results from Google Speech Recognition service.") | |
| return "" | |
| # Streamlit UI | |
| st.title("Voice and Text Input for English and Urdu") | |
| st.write("You can either type your text or speak to input in English or Urdu!") | |
| # Choose language input type | |
| input_type = st.selectbox("Select Input Type", ["Text", "Voice"]) | |
| # Process based on input type | |
| if input_type == "Text": | |
| text_input = st.text_area("Enter text:") | |
| if st.button("Submit"): | |
| if text_input: | |
| result = process_text(text_input) | |
| st.write(f"Processed Result: {result}") | |
| else: | |
| st.error("Please enter some text.") | |
| elif input_type == "Voice": | |
| if st.button("Start Recording"): | |
| speech_text = recognize_speech() | |
| if speech_text: | |
| result = process_text(speech_text) | |
| st.write(f"Processed Result: {result}") | |