Update app.py

app.py

@@ -58,77 +58,77 @@ def main():
 
     
 
-    if st.button("Get Answer"):
-        if query:
-            # Load model, set prompts, create vector database, and retrieve answer
-            try:
-                start = timeit.default_timer()
-                config = {
-                'max_new_tokens': 1024,
-                'repetition_penalty': 1.1,
-                'temperature': 0.1,
-                'top_k': 50,
-                'top_p': 0.9,
-                'stream': True,
-                'threads': int(os.cpu_count() / 2)
-                }
-                    
-                llm = CTransformers(
-                model="TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF",
-                model_file="mistral-7b-instruct-v0.2.Q4_0.gguf",
-                model_type="mistral",
-                lib="avx2", #for CPU use
-                **config
-                )
-                    
-                print("LLM Initialized...")
-                    
-                    
+        if st.button("Get Answer"):
+            if query:
+                # Load model, set prompts, create vector database, and retrieve answer
+                try:
+                    start = timeit.default_timer()
+                    config = {
+                    'max_new_tokens': 1024,
+                    'repetition_penalty': 1.1,
+                    'temperature': 0.1,
+                    'top_k': 50,
+                    'top_p': 0.9,
+                    'stream': True,
+                    'threads': int(os.cpu_count() / 2)
+                    }
+                        
+                    llm = CTransformers(
+                    model="TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF",
+                    model_file="mistral-7b-instruct-v0.2.Q4_0.gguf",
+                    model_type="mistral",
+                    lib="avx2", #for CPU use
+                    **config
+                    )
+                        
+                    print("LLM Initialized...")
+                        
+                        
+                        
+                    model_name = "BAAI/bge-large-en"
+                    model_kwargs = {'device': 'cpu'}
+                    encode_kwargs = {'normalize_embeddings': False}
+                    embeddings = HuggingFaceBgeEmbeddings(
+                        model_name=model_name,
+                        model_kwargs=model_kwargs,
+                        encode_kwargs=encode_kwargs
+                    )
+    
+                    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+                    chunked_documents = text_splitter.split_documents(loaded_documents)
+                    retriever = FAISS.from_documents(docs, embeddings).as_retriever()
                     
-                model_name = "BAAI/bge-large-en"
-                model_kwargs = {'device': 'cpu'}
-                encode_kwargs = {'normalize_embeddings': False}
-                embeddings = HuggingFaceBgeEmbeddings(
-                    model_name=model_name,
-                    model_kwargs=model_kwargs,
-                    encode_kwargs=encode_kwargs
-                )
-
-                text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-                chunked_documents = text_splitter.split_documents(loaded_documents)
-                retriever = FAISS.from_documents(docs, embeddings).as_retriever()
-                
-                # Wrap retrievers in a Tool
-                tools.append(
-                    Tool(
-                        name="Comparison tool",
-                        description="useful when you want to answer questions about the uploaded documents",
-                        func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever),
+                    # Wrap retrievers in a Tool
+                    tools.append(
+                        Tool(
+                            name="Comparison tool",
+                            description="useful when you want to answer questions about the uploaded documents",
+                            func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever),
+                        )
                     )
-                )
-
-                agent = initialize_agent(
-                tools=tools,
-                llm=llm,
-                agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
-                verbose=True
-                )
-
-                response = agent.run(query)
-
-                end = timeit.default_timer()
-                st.write("Elapsed time:")
-                st.write(end - start)
-
-                st.write("Bot Response:")
-                st.write(response)
-
-
-                                         
-            except Exception as e:
-                st.error(f"An error occurred: {str(e)}")
-        else:
-            st.warning("Please enter a question.")
+    
+                    agent = initialize_agent(
+                    tools=tools,
+                    llm=llm,
+                    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+                    verbose=True
+                    )
+    
+                    response = agent.run(query)
+    
+                    end = timeit.default_timer()
+                    st.write("Elapsed time:")
+                    st.write(end - start)
+    
+                    st.write("Bot Response:")
+                    st.write(response)
+    
+    
+                                             
+                except Exception as e:
+                    st.error(f"An error occurred: {str(e)}")
+            else:
+                st.warning("Please enter a question.")
                     
 
 if __name__ == "__main__":