updated pom file

app.py

@@ -20,160 +20,88 @@ import pandas as pd
 import numpy as np
 import pprint
 
-# from request
-#
-# url = 'https://www.example.com/some_path?some_key=some_value'
-# parsed_url = urlparse(url)
-# captured_value = parse_qs(parsed_url.query)['some_key'][0]
-#
-# print(captured_value)
-st.write(st.experimental_get_query_params()['pwd'][0])
-
-radioButtonList = ["E-commerce CSV (https://www.kaggle.com/datasets/mervemenekse/ecommerce-dataset)",
-"Upload my own CSV",
-"Upload my own PDF",
-"URL Chat with Google's Latest Earnings (https://abc.xyz/investor/)",
-"Enter my own URL"]
-
-# Add some designs to the radio buttons
-st.markdown("""
-<style>
-.stRadio {
-  padding: 10px;
-  border-radius: 5px;
-  background-color: #f5f5f5;
-}
-
-.stRadio input[type="radio"] {
-  position: absolute;
-  opacity: 0;
-  cursor: pointer;
-}
-
-.stRadio label {
-  display: flex;
-  justify-content: center;
-  align-items: center;
-  cursor: pointer;
-  font-size: 16px;
-  color: #333;
-}
-
-.stRadio label:hover {
-  color: #000;
-}
-
-.stRadio.st-selected input[type="radio"] ~ label {
-  color: #000;
-  background-color: #d9d9d9;
-}
-</style>
-""", unsafe_allow_html=True)
-
-genre = st.radio(
-    "Choose dataset to finetune", radioButtonList, index=0
-)
-
-# Initialize language model
-load_dotenv(find_dotenv()) # read local .env file
-api_key = st.secrets["PALM_API_KEY"] # put your API key here
-os.environ["GOOGLE_API_KEY"] = st.secrets["PALM_API_KEY"]
-palm.configure(api_key=api_key)
-llm = GooglePalm()
-llm.temperature = 0.1
-
-pdfCSVURLText = ""
-if genre==radioButtonList[0]:
-    pdfCSVURLText = "CSV"
-    exampleQuestion = "Question1: What was the most sold item? Question2: What was the most common payment?"
-    dataDF = pd.read_csv('EcommerceDataset.csv', encoding= 'unicode_escape')
-    # st.write('You selected comedy.')
-    # else:
-    # st.write(f'''Password streamlit app: {st.secrets["PSWD"]}''')
-elif genre==radioButtonList[1]:
-    pdfCSVURLText = "CSV"
-    exampleQuestion = "What are the data columns?"
-elif genre==radioButtonList[2]:
-    pdfCSVURLText = "PDF"
-    exampleQuestion = "Can you summarize the contents?"
-elif genre==radioButtonList[3]:
-    pdfCSVURLText = "URL"
-    exampleQuestion = "What is Google's latest earnings?"
-    urls = ['https://abc.xyz/investor/']
-    loader = [UnstructuredURLLoader(urls=urls)]
-    index = VectorstoreIndexCreator(
-            embedding=GooglePalmEmbeddings(),
-            text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)).from_loaders(loader)
-
-    chain = RetrievalQA.from_chain_type(llm=llm,
-                                chain_type="stuff",
-                                retriever=index.vectorstore.as_retriever(),
-                                input_key="question")
-elif genre==radioButtonList[4]:
-    pdfCSVURLText = "URL"
-    exampleQuestion = "Can you summarize the contents?"
-
-isCustomURL = genre==radioButtonList[4]
-urlInput = st.text_input('Enter your own URL', '', placeholder="Type your URL here (e.g. https://abc.xyz/investor/)", disabled=not isCustomURL)
-
-isCustomPDF = genre==radioButtonList[1] or genre==radioButtonList[2]
-uploaded_file = st.file_uploader(f"Upload your own {pdfCSVURLText} here", type=pdfCSVURLText.lower(), disabled=not isCustomPDF)
-uploadedFilename = ""
-if uploaded_file is not None:
-    with NamedTemporaryFile(dir='.', suffix=f'.{pdfCSVURLText.lower()}') as f:
-        f.write(uploaded_file.getbuffer())
-        uploadedFilename = f.name
-        if genre==radioButtonList[1]: # Custom CSV Upload
-            dataDF = pd.read_csv(uploadedFilename, encoding= 'unicode_escape')
-        elif genre==radioButtonList[2]: # Custom PDF Upload
-            pdf_loaders = [UnstructuredPDFLoader(uploadedFilename)]
-            pdf_index = VectorstoreIndexCreator(
-                    embedding=GooglePalmEmbeddings(),
-                    text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)).from_loaders(pdf_loaders)
-            pdf_chain = RetrievalQA.from_chain_type(llm=llm,
-                                        chain_type="stuff",
-                                        retriever=pdf_index.vectorstore.as_retriever(),
-                                        input_key="question")
-
-enableChatBox = False
-if genre==radioButtonList[0]: # E-commerce CSV
-    enableChatBox = True
-elif genre==radioButtonList[1]: # Custom CSV Upload
-    enableChatBox = uploadedFilename[-4:]==".csv"
-elif genre==radioButtonList[2]: # Custom PDF Upload
-    enableChatBox = uploadedFilename[-4:]==".pdf"
-elif genre==radioButtonList[3]: # Google Alphabet URL Earnings Report
-    enableChatBox = True
-elif genre==radioButtonList[4]: # Custom URL
-    enableChatBox = True
-
-chatTextStr = st.text_input(f'Ask me anything about this {pdfCSVURLText}', '', placeholder=f"Type here (e.g. {exampleQuestion})", disabled=not enableChatBox)
-chatWithPDFButton = "CLICK HERE TO START CHATTING"
-if st.button(chatWithPDFButton, disabled=not enableChatBox and not chatTextStr): #  Button Cliked
-
-
-    if genre==radioButtonList[0]: # E-commerce CSV
-        # Initializing the agent
-        agent = create_pandas_dataframe_agent(llm, dataDF, verbose=False)
-        answer = agent.run(chatTextStr)
-        st.write(answer)
-
-    elif genre==radioButtonList[1]: # Custom CSV Upload
-        # Initializing the agent
-        agent = create_pandas_dataframe_agent(llm, dataDF, verbose=False)
-        answer = agent.run(chatTextStr)
-        st.write(answer)
-
-    elif genre==radioButtonList[2]: # Custom PDF Upload
-        pdf_answer = pdf_chain.run(chatTextStr)
-        st.write(pdf_answer)
-
-    elif genre==radioButtonList[3]: # Google Alphabet URL Earnings Report
-        answer = chain.run(chatTextStr)
-        st.write(answer)
-
-    elif genre==radioButtonList[4]: # Custom URL
-        urls = [urlInput]
+isPswdValid = False
+try:
+    pswdVal = st.experimental_get_query_params()['pwd'][0]
+    if pswdVal==st.secrets["PSWD"]:
+        isPswdValid = True
+except:
+    pass
+
+if not isPswdValid:
+    st.write("Invalid Password")
+else:
+    radioButtonList = ["E-commerce CSV (https://www.kaggle.com/datasets/mervemenekse/ecommerce-dataset)",
+    "Upload my own CSV",
+    "Upload my own PDF",
+    "URL Chat with Google's Latest Earnings (https://abc.xyz/investor/)",
+    "Enter my own URL"]
+
+    # Add some designs to the radio buttons
+    st.markdown("""
+    <style>
+    .stRadio {
+      padding: 10px;
+      border-radius: 5px;
+      background-color: #f5f5f5;
+    }
+
+    .stRadio input[type="radio"] {
+      position: absolute;
+      opacity: 0;
+      cursor: pointer;
+    }
+
+    .stRadio label {
+      display: flex;
+      justify-content: center;
+      align-items: center;
+      cursor: pointer;
+      font-size: 16px;
+      color: #333;
+    }
+
+    .stRadio label:hover {
+      color: #000;
+    }
+
+    .stRadio.st-selected input[type="radio"] ~ label {
+      color: #000;
+      background-color: #d9d9d9;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+    genre = st.radio(
+        "Choose dataset to finetune", radioButtonList, index=0
+    )
+
+    # Initialize language model
+    load_dotenv(find_dotenv()) # read local .env file
+    api_key = st.secrets["PALM_API_KEY"] # put your API key here
+    os.environ["GOOGLE_API_KEY"] = st.secrets["PALM_API_KEY"]
+    palm.configure(api_key=api_key)
+    llm = GooglePalm()
+    llm.temperature = 0.1
+
+    pdfCSVURLText = ""
+    if genre==radioButtonList[0]:
+        pdfCSVURLText = "CSV"
+        exampleQuestion = "Question1: What was the most sold item? Question2: What was the most common payment?"
+        dataDF = pd.read_csv('EcommerceDataset.csv', encoding= 'unicode_escape')
+        # st.write('You selected comedy.')
+        # else:
+        # st.write(f'''Password streamlit app: {st.secrets["PSWD"]}''')
+    elif genre==radioButtonList[1]:
+        pdfCSVURLText = "CSV"
+        exampleQuestion = "What are the data columns?"
+    elif genre==radioButtonList[2]:
+        pdfCSVURLText = "PDF"
+        exampleQuestion = "Can you summarize the contents?"
+    elif genre==radioButtonList[3]:
+        pdfCSVURLText = "URL"
+        exampleQuestion = "What is Google's latest earnings?"
+        urls = ['https://abc.xyz/investor/']
         loader = [UnstructuredURLLoader(urls=urls)]
         index = VectorstoreIndexCreator(
                 embedding=GooglePalmEmbeddings(),
@@ -183,5 +111,79 @@ if st.button(chatWithPDFButton, disabled=not enableChatBox and not chatTextStr):
                                     chain_type="stuff",
                                     retriever=index.vectorstore.as_retriever(),
                                     input_key="question")
-        answer = chain.run(chatTextStr)
-        st.write(answer)
+    elif genre==radioButtonList[4]:
+        pdfCSVURLText = "URL"
+        exampleQuestion = "Can you summarize the contents?"
+
+    isCustomURL = genre==radioButtonList[4]
+    urlInput = st.text_input('Enter your own URL', '', placeholder="Type your URL here (e.g. https://abc.xyz/investor/)", disabled=not isCustomURL)
+
+    isCustomPDF = genre==radioButtonList[1] or genre==radioButtonList[2]
+    uploaded_file = st.file_uploader(f"Upload your own {pdfCSVURLText} here", type=pdfCSVURLText.lower(), disabled=not isCustomPDF)
+    uploadedFilename = ""
+    if uploaded_file is not None:
+        with NamedTemporaryFile(dir='.', suffix=f'.{pdfCSVURLText.lower()}') as f:
+            f.write(uploaded_file.getbuffer())
+            uploadedFilename = f.name
+            if genre==radioButtonList[1]: # Custom CSV Upload
+                dataDF = pd.read_csv(uploadedFilename, encoding= 'unicode_escape')
+            elif genre==radioButtonList[2]: # Custom PDF Upload
+                pdf_loaders = [UnstructuredPDFLoader(uploadedFilename)]
+                pdf_index = VectorstoreIndexCreator(
+                        embedding=GooglePalmEmbeddings(),
+                        text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)).from_loaders(pdf_loaders)
+                pdf_chain = RetrievalQA.from_chain_type(llm=llm,
+                                            chain_type="stuff",
+                                            retriever=pdf_index.vectorstore.as_retriever(),
+                                            input_key="question")
+
+    enableChatBox = False
+    if genre==radioButtonList[0]: # E-commerce CSV
+        enableChatBox = True
+    elif genre==radioButtonList[1]: # Custom CSV Upload
+        enableChatBox = uploadedFilename[-4:]==".csv"
+    elif genre==radioButtonList[2]: # Custom PDF Upload
+        enableChatBox = uploadedFilename[-4:]==".pdf"
+    elif genre==radioButtonList[3]: # Google Alphabet URL Earnings Report
+        enableChatBox = True
+    elif genre==radioButtonList[4]: # Custom URL
+        enableChatBox = True
+
+    chatTextStr = st.text_input(f'Ask me anything about this {pdfCSVURLText}', '', placeholder=f"Type here (e.g. {exampleQuestion})", disabled=not enableChatBox)
+    chatWithPDFButton = "CLICK HERE TO START CHATTING"
+    if st.button(chatWithPDFButton, disabled=not enableChatBox and not chatTextStr): #  Button Cliked
+
+
+        if genre==radioButtonList[0]: # E-commerce CSV
+            # Initializing the agent
+            agent = create_pandas_dataframe_agent(llm, dataDF, verbose=False)
+            answer = agent.run(chatTextStr)
+            st.write(answer)
+
+        elif genre==radioButtonList[1]: # Custom CSV Upload
+            # Initializing the agent
+            agent = create_pandas_dataframe_agent(llm, dataDF, verbose=False)
+            answer = agent.run(chatTextStr)
+            st.write(answer)
+
+        elif genre==radioButtonList[2]: # Custom PDF Upload
+            pdf_answer = pdf_chain.run(chatTextStr)
+            st.write(pdf_answer)
+
+        elif genre==radioButtonList[3]: # Google Alphabet URL Earnings Report
+            answer = chain.run(chatTextStr)
+            st.write(answer)
+
+        elif genre==radioButtonList[4]: # Custom URL
+            urls = [urlInput]
+            loader = [UnstructuredURLLoader(urls=urls)]
+            index = VectorstoreIndexCreator(
+                    embedding=GooglePalmEmbeddings(),
+                    text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)).from_loaders(loader)
+
+            chain = RetrievalQA.from_chain_type(llm=llm,
+                                        chain_type="stuff",
+                                        retriever=index.vectorstore.as_retriever(),
+                                        input_key="question")
+            answer = chain.run(chatTextStr)
+            st.write(answer)