Update app.py

app.py

@@ -1,47 +1,93 @@
 import streamlit as st
-from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
-from transformers import AutoModel
-from pinecone import Pinecone
-import os  # For environment variable access
-
-# Replace with your Space's environment variable name for API key
-API_KEY = os.environ.get("PINECONE_API_KEY")
-#pc = Pinecone(api_key=PINECONE_API_KEY)
-# Connect to Pinecone using the API key retrieved from the Space's environment variable
-#client = IndexClient(api_key=API_KEY)
-pc = Pinecone(api_key=API_KEY)
-
-# Load pre-trained model (replace with your chosen model)
-model =SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
-
-def process_and_search(query):
-    # Preprocess user input (example: tokenization, normalization)
-    #preprocessed_query = preprocess_query(query)  # Replace with your implementation
-
-    # Encode the preprocessed query using the pre-trained model
-    encoded_query = model.embed_query(query, return_tensors="pt")
-
-    # Perform vector search in Pinecone
-    results = pc.query(INDEX_NAME, encoded_query.cpu().numpy())
-
-    # Process search results (example: extract answers, format display)
-    processed_results = []
-    for result in results:
-        # Example processing: extract answer from metadata
-        answer = result["metadata"]["answer"]  # Adapt based on your data structure
-        processed_results.append(answer)
-
-    return processed_results
-st.title("Pinecone Search App")
-
-user_query = st.text_area("Enter your question:", height=100)
-
-if st.button("Search"):
-    if user_query:
-        # Process, search, and display results (call the process_and_search function)
-        answers = process_and_search(user_query)
-        st.write("Search Results:")
-        for answer in answers:
-            st.write(f"- {answer}")
+from utils import *
+import constants
+
+# Creating Session State Variable
+if 'HuggingFace_API_Key' not in st.session_state:
+    st.session_state['HuggingFace_API_Key'] =''
+if 'Pinecone_API_Key' not in st.session_state:
+    st.session_state['Pinecone_API_Key'] =''
+
+
+#
+st.title('🤖 AI Assistance For Website') 
+
+#********SIDE BAR Funtionality started*******
+
+# Sidebar to capture the API keys
+st.sidebar.title("😎🗝️")
+st.session_state['HuggingFace_API_Key']= st.sidebar.text_input("What's your HuggingFace API key?",type="password")
+st.session_state['Pinecone_API_Key']= st.sidebar.text_input("What's your Pinecone API key?",type="password")
+
+#Recent changes by langchain team, expects ""PINECONE_API_KEY" environment variable for Pinecone usage! So we are creating it here
+import os
+os.environ["PINECONE_API_KEY"] = st.session_state['Pinecone_API_Key']
+
+
+load_button = st.sidebar.button("Load data to Pinecone", key="load_button")
+
+#If the bove button is clicked, pushing the data to Pinecone...
+if load_button:
+    #Proceed only if API keys are provided
+    if st.session_state['HuggingFace_API_Key'] !="" and st.session_state['Pinecone_API_Key']!="" :
+
+        #Fetch data from site
+        site_data=get_website_data(constants.WEBSITE_URL)
+        st.write("Data pull done...")
+
+        #Split data into chunks
+        chunks_data=split_data(site_data)
+        st.write("Spliting data done...")
+
+        #Creating embeddings instance
+        embeddings=create_embeddings()
+        st.write("Embeddings instance creation done...")
+
+        #Push data to Pinecone
+        
+        push_to_pinecone(st.session_state['Pinecone_API_Key'],constants.PINECONE_ENVIRONMENT,constants.PINECONE_INDEX,embeddings,chunks_data)
+        st.write("Pushing data to Pinecone done...")
+
+        st.sidebar.success("Data pushed to Pinecone successfully!")
+    else:
+        st.sidebar.error("Ooopssss!!! Please provide API keys.....")
+
+#********SIDE BAR Funtionality ended*******
+
+#Captures User Inputs
+prompt = st.text_input('How can I help you my friend ❓',key="prompt")  # The box for the text prompt
+document_count = st.slider('No.Of links to return 🔗 - (0 LOW || 5 HIGH)', 0, 5, 2,step=1)
+
+submit = st.button("Search") 
+
+
+if submit:
+    #Proceed only if API keys are provided
+    if st.session_state['HuggingFace_API_Key'] !="" and st.session_state['Pinecone_API_Key']!="" :
+
+        #Creating embeddings instance
+        embeddings=create_embeddings()
+        st.write("Embeddings instance creation done...")
+
+        #Pull index data from Pinecone
+        index=pull_from_pinecone(st.session_state['Pinecone_API_Key'],constants.PINECONE_ENVIRONMENT,constants.PINECONE_INDEX,embeddings)
+        st.write("Pinecone index retrieval done...")
+
+        #Fetch relavant documents from Pinecone index
+        relavant_docs=get_similar_docs(index,prompt,document_count)
+        st.write(relavant_docs)
+
+        #Displaying search results
+        st.success("Please find the search results :")
+         #Displaying search results
+        st.write("search results list....")
+        for document in relavant_docs:
+            
+            st.write("👉**Result : "+ str(relavant_docs.index(document)+1)+"**")
+            st.write("**Info**: "+document.page_content)
+            st.write("**Link**: "+ document.metadata['source'])
+       
+
+
     else:
-        st.error("Please enter a question.")
+        st.sidebar.error("Ooopssss!!! Please provide API keys.....")