Update app.py

app.py

@@ -23,8 +23,6 @@ import requests
 import json
 import os
 
-
-
 # set this key as an environment variable
 os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets['huggingface_token']
 
@@ -36,29 +34,27 @@ Public_Url = 'https://jikoni-tmodel.hf.space' #endpoint
 
 def translate(userinput, target_lang, source_lang=None):
     if source_lang:
-       url = f"{Public_Url}/translate_enter/"
-       data = {
-           "userinput": userinput,
-           "source_lang": source_lang,
-           "target_lang": target_lang,
+        url = f"{Public_Url}/translate_enter/"
+        data = {
+            "userinput": userinput,
+            "source_lang": source_lang,
+            "target_lang": target_lang,
         }
-       response = requests.post(url, json=data)
-       result = response.json()
-       print(type(result))
-       source_lange = source_lang
-       translation = result['translated_text']
-       
+        response = requests.post(url, json=data)
+        result = response.json()
+        print(type(result))
+        source_lange = source_lang
+        translation = result['translated_text']
     else:
-      url = f"{Public_Url}/translate_detect/"
-      data = {
-        "userinput": userinput,
-        "target_lang": target_lang,
-      }
-
-      response = requests.post(url, json=data)
-      result = response.json()
-      source_lange = result['source_language']
-      translation = result['translated_text']
+        url = f"{Public_Url}/translate_detect/"
+        data = {
+            "userinput": userinput,
+            "target_lang": target_lang,
+        }
+        response = requests.post(url, json=data)
+        result = response.json()
+        source_lange = result['source_language']
+        translation = result['translated_text']
     return source_lange, translation
 
 def get_pdf_text(pdf : Union[str, bytes, bytearray]) -> str:
@@ -70,7 +66,6 @@ def get_pdf_text(pdf : Union[str, bytes, bytearray]) -> str:
             pdf_text += text
     return text
 
-
 def get_text_chunks(text:str) ->list:
     text_splitter = CharacterTextSplitter(
         separator="\n", chunk_size=1500, chunk_overlap=300, length_function=len
@@ -78,7 +73,6 @@ def get_text_chunks(text:str) ->list:
     chunks = text_splitter.split_text(text)
     return chunks
 
-
 def get_vectorstore(text_chunks : list) -> FAISS:
     model = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
     encode_kwargs = {
@@ -90,13 +84,11 @@ def get_vectorstore(text_chunks : list) -> FAISS:
     vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
     return vectorstore
 
-
 def get_conversation_chain(vectorstore:FAISS) -> ConversationalRetrievalChain:
     llm = HuggingFaceHub(
-        #repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
         repo_id="TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF",
         task="text-generation",
-        model_kwargs={"temperature": 0.5, "max_length": 1048},
+        model_kwargs={"temperature": 0.5, "max_length": 1048}
     )
 
     memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
@@ -105,7 +97,6 @@ def get_conversation_chain(vectorstore:FAISS) -> ConversationalRetrievalChain:
     )
     return conversation_chain
 
-
 st.markdown (""" 
   <style> div.stSpinner > div {
     text-align:center; 
@@ -151,7 +142,6 @@ def main():
             st.session_state.conversation = get_conversation_chain(vectorstore)
             st.info("done")
             
-    #user_question = st.text_input("chat with your pdf ...")
     # show user input
     if "messages" not in st.session_state:
         st.session_state.messages = []
@@ -179,6 +169,5 @@ def main():
     # Signature
     st.markdown(footer, unsafe_allow_html=True)
 
-
 if __name__ == '__main__':
     main()