Update app.py

app.py

@@ -11,70 +11,52 @@ from langchain.prompts import PromptTemplate
 from dotenv import load_dotenv
 from st_audiorec import st_audiorec
 import whisper
+from txtai.pipeline import Translation
 
 genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
-
 def get_pdf_text(pdf_docs):
-    text=""
+    text = ""
     for pdf in pdf_docs:
-        pdf_reader= PdfReader(pdf)
+        pdf_reader = PdfReader(pdf)
         for page in pdf_reader.pages:
-            text+= page.extract_text()
-    return  text
-
-
+            text += page.extract_text()
+    return text
 
 def get_text_chunks(text):
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
     chunks = text_splitter.split_text(text)
     return chunks
 
-
 def get_vector_store(text_chunks):
-    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
     vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
     vector_store.save_local("faiss_index")
 
-
 def get_conversational_chain():
-
     prompt_template = """
-    Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
-    provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
+    Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
     Context:\n {context}?\n
     Question: \n{question}\n
-    Answer:
-    """
-
-    model = ChatGoogleGenerativeAI(model="gemini-pro",
-                             temperature=0.1)
-
-    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
+    Answer: """
+    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.1)
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
     chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
-
     return chain
 
-
-
 def user_input(user_question):
-    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
-    
-    new_db = FAISS.load_local("faiss_index", embeddings,allow_dangerous_deserialization= True)
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
     docs = new_db.similarity_search(user_question)
-
     chain = get_conversational_chain()
-
-    
-    response = chain(
-        {"input_documents":docs, "question": user_question}
-        , return_only_outputs=True)
-
+    response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
     print(response)
-    st.write("Reply: ", response["output_text"])
-
 
+    # Create translation model
+    translate = Translation()
+    translation = translate(response["output_text"], "fa")
 
+    st.write("Reply: ", translation)
 
 def main():
     st.set_page_config("Chat PDF")
@@ -82,11 +64,9 @@ def main():
 
     # Audio recording
     wav_audio_data = st_audiorec()
-
     if wav_audio_data is not None:
         with open("query.wav", "wb") as f:
             f.write(wav_audio_data)
-
         model = whisper.load_model("large")
         result = model.transcribe("query.wav", language="en", fp16=False)
         user_question = result["text"]
@@ -103,6 +83,5 @@ def main():
                 get_vector_store(text_chunks)
             st.success("Done")
 
-
 if __name__ == "__main__":
     main()