Update app.py

app.py

@@ -17,52 +17,50 @@ import time
 image = Image.open('Wipro logo.png')
 st.image(image)
 
-st.title("Wipro Impact | CSRD | Advisor")
+st.title("Wipro impact | The inquisitive sustainability leader:  Learn some of the best practices in sustainability from success stories of leading companies.. ")
 
-st.header("Enable clients to prepare for CSRD.. ")
 
+st.header("Welcome!. Today, What company's sustainability story is inspiring you ?.. ")
 
-genre = st.radio(
-    "Choose a CSRD focus area for AI question answering",
-    ('E1-Climate Change', 'E4-Biodiversity and ecosystem', 'S1-Own Workforce'), index=0)
 
-url = "https://www.efrag.org/Assets/Download?assetUrl=%2Fsites%2Fwebpublishing%2FSiteAssets%2F13%2520Draft%2520ESRS%2520S1%2520Own%2520workforce%2520November%25202022.pdf"
+myurl = st.text_input("Give the URL to find a sustainability or annual report", "Type URL of any company PDF document here")
 
-if genre == 'E1-Climate Change':
-  url = "https://www.efrag.org/Assets/Download?assetUrl=%2Fsites%2Fwebpublishing%2FSiteAssets%2F08%2520Draft%2520ESRS%2520E1%2520Climate%2520Change%2520November%25202022.pdf"
-  samplequestions = ["What are the climate related indicators?", "List all the disclosure requirments listed in page 3?",  "Policies related to climate change mitigation and adaptation?", 
-  "What should the company disclose regarding Actions and resources in relation to climate change policies?" , "How should the  GHG emission reduction targets be reported?" , 
-  "Total energy consumption from non-renewable sources for high climate impact sectors should be disaggregated as ?", 
-  "Total energy consumption from renewable sources should be disaggregated as ?" , 
-  "What should be disclosed on GHG removals and GHG mitigation projects financed through carbon credits ?" , 
-  "Is it wise to invest on carbon credits? ",
-  "What is Climate change adaptation? ", "What are Decarbonisation levers?"   ]
+if st.button("Load this report "):
+    loader1 = PyPDFLoader(myurl)
+    
+    index = VectorstoreIndexCreator(
+        # split the documents into chunks
+        text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0),
+        # select which embeddings we want to use
+        embedding=langchainembeddings,
+        # use Chroma as the vectorestore to index and search embeddings
+        vectorstore_cls=Chroma
+    ).from_loaders([loader1])
+    
+    st.write("loaded")
 
-if genre == 'E4-Biodiversity and ecosystem':
-  url = "https://www.efrag.org/Assets/Download?assetUrl=%2Fsites%2Fwebpublishing%2FSiteAssets%2F11%2520Draft%2520ESRS%2520E4%2520Biodiversity%2520and%2520ecosystems%2520November%25202022.pdf"
-  samplequestions = ["What are the Biodiversity related indicators?", "List all the disclosure requirments listed in page 3?"]
-if genre == 'S1-Own Workforce':
-  url = "https://www.efrag.org/Assets/Download?assetUrl=%2Fsites%2Fwebpublishing%2FSiteAssets%2F13%2520Draft%2520ESRS%2520S1%2520Own%2520workforce%2520November%25202022.pdf" 
-  samplequestions = ["What are the social related indicators?", "List all the disclosure requirments listed in page 3?"]
 
 
 
 
-sampleselectedquestion = st.selectbox(
-    'Just ask your question or start with a one of these example questions...',  samplequestions    )
-
-st.write(" :green[ Ask any thing on your mind...just type your question here...]")
-yourquestion = st.text_input('Your question', sampleselectedquestion)
-st.write('Your typed .. ', yourquestion)
-
+yourquestion = st.text_input('Ask your question on best practices', 'What is Wipro plans for Biodiversity in 2024?')
+st.write('Your input is ', yourquestion)
 
+aimethod = st.radio(
+    "Choose a AI brain",
+    ('GPT3', 'GPT3.5' ), index=1)
 
 
 os.environ['OPENAI_API_TYPE'] = 'azure'
 os.environ['OPENAI_API_VERSION'] = '2023-03-15-preview'
 
-llmgpt3 = AzureOpenAI(      deployment_name="testdavanci", model_name="text-davinci-003" )
-chain = load_qa_chain(llm=llmgpt3, chain_type="map_reduce")
+#llmgpt3 = AzureOpenAI(      deployment_name="testdavanci", model_name="text-davinci-003" )
+llmchatgpt = AzureOpenAI(     deployment_name="esujnand", model_name="gpt-35-turbo" )
+
+if st.button("Ask Best practices "):
+  answer = index.query(llm=llmchatgpt, question=query, chain_type="map_reduce")
+  st.write(answer)
+
 
 aimethod = st.radio(
     "Choose a AI brain or document comprehension method",
@@ -74,7 +72,7 @@ if aimethod ==  '2 minutes AI method map_reduce':
 if aimethod ==  '4 minutes AI method refine':
   mychain_type = "refine"
 
-chain = load_qa_chain(llm=llmgpt3, chain_type=mychain_type)
+ 
 
 
 loader1 = PyPDFLoader(url)