Create app.py

app.py

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+import os
+import streamlit as st
+import whisper
+from transformers import pipeline
+from gtts import gTTS
+import speech_recognition as sr
+import tempfile
+from langchain_community.vectorstores import FAISS
+from langchain.document_loaders import PyPDFLoader
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+
+# Initialize models
+whisper_model = whisper.load_model("base")  # Use the base model for faster performance
+translation_pipeline = pipeline(
+    "translation", model="Helsinki-NLP/opus-mt-ur-en-tiny", tokenizer="Helsinki-NLP/opus-mt-ur-en-tiny"
+)
+urdu_translation_pipeline = pipeline(
+    "translation", model="Helsinki-NLP/opus-mt-en-ur-tiny", tokenizer="Helsinki-NLP/opus-mt-en-ur-tiny"
+)
+embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-MiniLM-L6-v2")
+
+# Streamlit interface
+st.title("Real-Time Voice-to-Voice First Aid Chatbot")
+
+uploaded_file = st.file_uploader("Upload a PDF file for First Aid Knowledge", type=["pdf"])
+if uploaded_file:
+    st.write("Processing PDF...")
+    loader = PyPDFLoader(uploaded_file)
+    documents = loader.load()
+    
+    st.write("Creating vector database...")
+    vectorstore = FAISS.from_documents(documents, embedding_model)
+    st.write("Knowledge base ready.")
+    
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+    chain = ConversationalRetrievalChain.from_llm(
+        llm=None,  # Replace with a valid LLM integration like OpenAI or Groq client
+        retriever=vectorstore.as_retriever(),
+        memory=memory,
+    )
+
+if st.button("Start Chat"):
+    st.write("Listening... Speak now!")
+    recognizer = sr.Recognizer()
+    
+    with sr.Microphone() as source:
+        st.write("Adjusting for ambient noise, please wait...")
+        recognizer.adjust_for_ambient_noise(source)
+        st.write("You can now speak.")
+        
+        while True:
+            try:
+                st.write("Listening...")
+                audio = recognizer.listen(source)
+                st.write("Processing audio...")
+                
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
+                    temp_audio.write(audio.get_wav_data())
+                    temp_audio_path = temp_audio.name
+                
+                transcription = whisper_model.transcribe(temp_audio_path)["text"]
+                st.write(f"You said: {transcription}")
+                
+                translated_text = translation_pipeline(transcription)[0]["translation_text"]
+                st.write(f"Translated Text: {translated_text}")
+                
+                response = chain({"input": translated_text})["response"]
+                st.write(f"Response: {response}")
+                
+                urdu_response = urdu_translation_pipeline(response)[0]["translation_text"]
+                st.write(f"Response in Urdu: {urdu_response}")
+                
+                tts = gTTS(urdu_response, lang="ur")
+                response_audio_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3").name
+                tts.save(response_audio_path)
+                os.system(f"mpg123 {response_audio_path}")
+            
+            except Exception as e:
+                st.write(f"Error: {str(e)}")