Delete app1.py

app1.py

@@ -1,322 +0,0 @@
-import os
-import requests
-import streamlit as st
-import pickle
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-from langchain_groq import ChatGroq
-from langchain.document_loaders import PDFPlumberLoader
-from langchain_experimental.text_splitter import SemanticChunker
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_chroma import Chroma
-from langchain.chains import SequentialChain, LLMChain
-
-# Set API Keys
-os.environ["GROQ_API_KEY"] = st.secrets.get("GROQ_API_KEY", "")
-
-# Load LLM models
-llm_judge = ChatGroq(model="deepseek-r1-distill-llama-70b")
-rag_llm = ChatGroq(model="mixtral-8x7b-32768")
-
-llm_judge.verbose = True
-rag_llm.verbose = True
-
-VECTOR_DB_PATH = "/tmp/chroma_db"  
-CHUNKS_FILE = "/tmp/chunks.pkl"  
-
-# Session State Initialization
-if "vector_store" not in st.session_state:
-    st.session_state.vector_store = None
-if "documents" not in st.session_state:
-    st.session_state.documents = None
-if "pdf_path" not in st.session_state:
-    st.session_state.pdf_path = None  
-if "pdf_loaded" not in st.session_state:
-    st.session_state.pdf_loaded = False
-if "chunked" not in st.session_state:
-    st.session_state.chunked = False
-if "vector_created" not in st.session_state:
-    st.session_state.vector_created = False
-
-st.title("Blah-2")
-
-# Step 1: Choose PDF Source
-pdf_source = st.radio("Upload or provide a link to a PDF:", ["Enter a PDF URL", "Upload a PDF file"], index=0, horizontal=True)
-
-if pdf_source == "Upload a PDF file":
-    uploaded_file = st.file_uploader("Upload your PDF file", type="pdf")
-    if uploaded_file:
-        st.session_state.pdf_path = "temp.pdf"
-        with open(st.session_state.pdf_path, "wb") as f:
-            f.write(uploaded_file.getbuffer())
-        st.session_state.pdf_loaded = False
-        st.session_state.chunked = False
-        st.session_state.vector_created = False
-
-elif pdf_source == "Enter a PDF URL":
-    pdf_url = st.text_input("Enter PDF URL:", value="https://arxiv.org/pdf/2406.06998", key="pdf_url", on_change=lambda: st.session_state.update(trigger_download=True))
-
-    # Button OR Enter key will trigger download
-    if st.button("Download and Process PDF") or st.session_state.get("trigger_download", False):
-        with st.spinner("Downloading PDF..."):
-            try:
-                response = requests.get(pdf_url)
-                if response.status_code == 200:
-                    st.session_state.pdf_path = "temp.pdf"
-                    with open(st.session_state.pdf_path, "wb") as f:
-                        f.write(response.content)
-
-                    # Reset states
-                    st.session_state.pdf_loaded = False
-                    st.session_state.chunked = False
-                    st.session_state.vector_created = False
-                    st.session_state.trigger_download = False  # Reset trigger
-
-                    st.success("✅ PDF Downloaded Successfully!")
-                else:
-                    st.error("❌ Failed to download PDF. Check the URL.")
-            except Exception as e:
-                st.error(f"❌ Error downloading PDF: {e}")
-
-
-# Step 2: Load & Process PDF (Only Once)
-if st.session_state.pdf_path and not st.session_state.pdf_loaded:
-    with st.spinner("Loading PDF..."):
-        try:
-            loader = PDFPlumberLoader(st.session_state.pdf_path)
-            docs = loader.load()
-            st.session_state.documents = docs
-            st.session_state.pdf_loaded = True
-            st.success(f"✅ **PDF Loaded!** Total Pages: {len(docs)}")
-        except Exception as e:
-            st.error(f"❌ Error processing PDF: {e}")
-
-# Load Cached Chunks if Available
-def load_chunks():
-    if os.path.exists(CHUNKS_FILE):
-        with open(CHUNKS_FILE, "rb") as f:
-            return pickle.load(f)
-    return None
-
-if not st.session_state.chunked:  # Ensure chunking only happens once
-    cached_chunks = load_chunks()
-    if cached_chunks:
-        st.session_state.documents = cached_chunks
-        st.session_state.chunked = True
-
-# Step 3: Chunking (Only Happens Once)
-if st.session_state.pdf_loaded and not st.session_state.chunked:
-    with st.spinner("Chunking the document..."):
-        try:
-            model_name = "nomic-ai/modernbert-embed-base"
-            embedding_model = HuggingFaceEmbeddings(model_name=model_name, model_kwargs={'device': 'cpu'})
-            text_splitter = SemanticChunker(embedding_model)
-            
-            if st.session_state.documents:
-                documents = text_splitter.split_documents(st.session_state.documents)
-                st.session_state.documents = documents
-                st.session_state.chunked = True
-
-                # Save chunks for persistence
-                with open(CHUNKS_FILE, "wb") as f:
-                    pickle.dump(documents, f)
-
-                st.success(f"✅ **Document Chunked!** Total Chunks: {len(documents)}")
-        except Exception as e:
-            st.error(f"❌ Error chunking document: {e}")
-
-# Step 4: Setup Vectorstore 
-def load_vector_store():
-    return Chroma(persist_directory=VECTOR_DB_PATH, collection_name="deepseek_collection", embedding_function=HuggingFaceEmbeddings(model_name="nomic-ai/modernbert-embed-base"))
-
-if st.session_state.chunked and not st.session_state.vector_created:
-    with st.spinner("Creating vector store..."):
-        try:
-            if st.session_state.vector_store is None:  # Prevent unnecessary reloading
-                st.session_state.vector_store = load_vector_store()
-
-            if len(st.session_state.vector_store.get()["documents"]) == 0:  # Prevent duplicate insertions
-                st.session_state.vector_store.add_documents(st.session_state.documents)
-
-            num_documents = len(st.session_state.vector_store.get()["documents"])
-            st.session_state.vector_created = True
-            st.success(f"✅ **Vector Store Created!** Total documents stored: {num_documents}")
-        except Exception as e:
-            st.error(f"❌ Error creating vector store: {e}")
-
-# Debugging Logs
-#st.write("📄 **PDF Loaded:**", st.session_state.pdf_loaded)
-#st.write("🔹 **Chunked:**", st.session_state.chunked)
-#st.write("📂 **Vector Store Created:**", st.session_state.vector_created)
-
-
-# ----------------- Query Input -----------------
-query = None  
-
-# Check if a valid PDF URL has been entered (but not processed yet)
-pdf_url_entered = bool(st.session_state.get("pdf_url"))  # Checks if text is in the input box
-
-# No PDF Provided Yet
-if not st.session_state.pdf_path and not pdf_url_entered:
-    st.info("📥 **Please upload a PDF or enter a valid URL to proceed.**")
-
-# PDF URL Exists but Not Processed Yet (Only show if URL exists but hasn't been downloaded)
-elif pdf_url_entered and not st.session_state.pdf_loaded:
-    st.warning("⚠️ **PDF URL detected! Click 'Download and Process PDF' to proceed.**")
-
-# Processing in Progress 
-elif st.session_state.get("trigger_download", False) and (
-    not st.session_state.pdf_loaded or not st.session_state.chunked or not st.session_state.vector_created
-):
-    st.info("⏳ **Processing your document... Please wait.**")
-
-# ✅ Step 4: Processing Complete, Ready for Questions
-elif st.session_state.pdf_loaded and st.session_state.chunked and st.session_state.vector_created:
-    st.success("🎉 **Processing complete! You can now ask questions.**")
-    query = st.text_input("🔍 **Ask a question about the document:**")
-    
-if query:
-    with st.spinner("🔄 Retrieving relevant context..."):
-        retriever = st.session_state.vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 3})
-        contexts = retriever.invoke(query)
-        # Debugging: Check what was retrieved
-        st.write("Retrieved Contexts:", contexts)
-        st.write("Number of Contexts:", len(contexts))
-        
-        context = [d.page_content for d in contexts]
-        # Debugging: Check extracted context
-        st.write("Extracted Context (page_content):", context)
-        st.write("Number of Extracted Contexts:", len(context))
-        
-        relevancy_prompt = """You are an expert judge tasked with evaluating whether the  EACH OF THE CONTEXT provided in the CONTEXT LIST is self sufficient to answer the QUERY asked.
-        Analyze the provided QUERY AND  CONTEXT to determine if each Ccontent in the CONTEXT LIST contains Relevant information to answer the QUERY.
-        
-        Guidelines:
-        1. The content must not introduce new information beyond what's provided in the QUERY.
-        2. Pay close attention to the subject of statements. Ensure that attributes, actions, or dates are correctly associated with the right entities (e.g., a person vs. a TV show they star in).
-        3. Be vigilant for subtle misattributions or conflations of information, even if the date or other details are correct.
-        4. Check that the content in the CONTEXT LIST doesn't oversimplify or generalize information in a way that changes the meaning of the QUERY.
-        
-        Analyze the text thoroughly and assign a relevancy score 0 or 1 where:
-        - 0: The content has all the necessary information to answer the QUERY
-        - 1: The content does not has the necessary information to answer the QUERY
-        
-        ```
-        EXAMPLE:
-        INPUT (for context only, not to be used for faithfulness evaluation):
-        What is the capital of France?
-        
-        CONTEXT:
-        ['France is a country in Western Europe. Its capital is Paris, which is known for landmarks like the Eiffel Tower.',
-        'Mr. Naveen patnaik has been the chief minister of Odisha for consequetive 5 terms']
-        
-        OUTPUT:
-        The Context has sufficient information to answer the query.
-        
-        RESPONSE:
-        {{"score":0}}
-        ```
-        
-        CONTENT LIST:
-        {context}
-        
-        QUERY:
-        {retriever_query}
-        Provide your verdict in JSON format  with a single key 'score' and no preamble or explanation:
-        [{{"content:1,"score": <your score either 0 or 1>,"Reasoning":<why you have chose the score as 0 or 1>}},
-        {{"content:2,"score": <your score either 0 or 1>,"Reasoning":<why you have chose the score as 0 or 1>}},
-        ...]
-        """
-        
-        context_relevancy_checker_prompt = PromptTemplate(input_variables=["retriever_query","context"],template=relevancy_prompt)
-
-        relevant_prompt = PromptTemplate(
-            input_variables=["relevancy_response"],
-            template="""
-            Your main task is to analyze the json structure as a part of the Relevancy Response.
-            Review the Relevancy Response and do the following:-
-            (1) Look at the Json Structure content
-            (2) Analyze the 'score' key in the Json Structure content.
-            (3) pick the value of 'content' key against those 'score' key value which has 0.
-            
-            Relevancy Response:
-            {relevancy_response}
-            
-            Provide your verdict in JSON format  with a single key 'content number' and no preamble or explanation:
-            [{{"content":<content number>}}]
-            """
-        )
-        
-        context_prompt = PromptTemplate(
-            input_variables=["context_number"],
-            template="""
-            Your main task is to analyze the json structure as a part of the Context Number Response and the list of Contexts provided in the 'Content List' and perform the following steps:-
-            (1) Look at the output from the Relevant Context Picker Agent.
-            (2) Analyze the 'content' key in the Json Structure format({{"content":<<content_number>>}}).
-            (3) Retrieve the value of 'content' key and pick up the context corresponding to that element from the Content List provided.
-            (4) Pass the retrieved context for each corresponing element number referred in the 'Context Number Response'
-            
-            Context Number Response:
-            {context_number}
-            
-            Content List:
-            {context}
-            
-            Provide your verdict in JSON format  with a two key 'relevant_content' and 'context_number' no preamble or explanation:
-            [{{"context_number":<content1>,"relevant_content":<content corresponing to that element 1 in the Content List>}},
-            {{"context_number":<content4>,"relevant_content":<content corresponing to that element 4 in the Content List>}},
-            ...
-            ]
-            """
-        )
-
-        rag_prompt = """ You are a helpful assistant very profiient in formulating clear and meaningful answers from the context provided.Based on the CONTEXT Provided ,Please formulate
-        a clear concise and meaningful answer for the QUERY asked.Please refrain from making up your own answer in case the COTEXT provided is not sufficient to answer the QUERY.In such a situation please respond as 'I do not know'.
-        
-        QUERY:
-        {query}
-        
-        CONTEXT
-        {context}
-        
-        ANSWER:
-        """
-
-        context_relevancy_evaluation_chain = LLMChain(llm=llm_judge, prompt=context_relevancy_checker_prompt, output_key="relevancy_response")
-
-        response_crisis = context_relevancy_evaluation_chain.invoke({"context":context,"retriever_query":query})
-
-        pick_relevant_context_chain = LLMChain(llm=llm_judge, prompt=relevant_prompt, output_key="context_number")
-
-        relevant_response = pick_relevant_context_chain.invoke({"relevancy_response":response_crisis['relevancy_response']})
-
-        relevant_contexts_chain = LLMChain(llm=llm_judge, prompt=context_prompt, output_key="relevant_contexts")
-
-        contexts = relevant_contexts_chain.invoke({"context_number":relevant_response['context_number'],"context":context})
-
-        final_prompt = PromptTemplate(input_variables=["query","context"],template=rag_prompt)
-
-        response_chain = LLMChain(llm=rag_llm,prompt=final_prompt,output_key="final_response")
-
-        response = response_chain.invoke({"query":query,"context":contexts['relevant_contexts']})
-
-        # Orchestrate using SequentialChain
-        context_management_chain = SequentialChain(
-            chains=[context_relevancy_evaluation_chain ,pick_relevant_context_chain, relevant_contexts_chain,response_chain],
-            input_variables=["context","retriever_query","query"],
-            output_variables=["relevancy_response", "context_number","relevant_contexts","final_response"]
-        )
-
-        final_output = context_management_chain({"context":context,"retriever_query":query,"query":query})
-
-        st.subheader('final_output["relevancy_response"]')
-        st.write(final_output["relevancy_response"] )
-
-        st.subheader('final_output["context_number"]')
-        st.write(final_output["context_number"])
-
-        st.subheader('final_output["relevant_contexts"]')
-        st.write(final_output["relevant_contexts"])
-
-        st.subheader('final_output["final_response"]')
-        st.write(final_output["final_response"])