Update app.py

app.py

@@ -1,63 +1,178 @@
-import streamlit as st
-
-from getpass import getpass
-GOOGLE_API_KEY = getpass("AIzaSyCL5TCEbUCfidn-f6cMSa7Lb6P9IyyLGUw")
-
-st.title("ITC Financial Analyzer")
-
-st.text_area(placeholder="Enter your query...")
-
-# Calling a Gemini LLM
-from langchain_google_genai import ChatGoogleGenerativeAI
-
-llm = ChatGoogleGenerativeAI(api_key = GOOGLE_API_KEY, model = "gemini-2.0-flash-exp")
-
-
-# Chat Prompt Template with MessagesPlaceholder
-from langchain_core.prompts import ChatMessagePromptTemplate, MessagesPlaceholder
-
-chat_prompt = ChatPromptTemplate.from_messages([
-    ("system",
-     """You are a domain-specific AI financial analyst focused on company-level performance evaluation.
-
-Your task is to analyze and respond to user financial queries *strictly based on the provided transcript data*: {context}.
-
-*Rules:*
-1. ONLY extract facts, figures, and insights that are explicitly available in the transcript.
-2. If data is *missing or partially available*, clearly state: "The required data is not available in the current transcript." Then provide a generic but relevant explanation based on standard financial principles.
-3. Maintain numerical accuracy and avoid interpretation beyond data boundaries.
-4. Prioritize answers relevant to *ITC Ltd.*, but keep response format adaptable to other firms and fiscal years.
-5. Clearly present year-wise or metric-wise insights using bullet points or structured formats if applicable.
-
-*Your goals:*
-- Ensure 100% fidelity to source transcript.
-- Do not assume or hallucinate missing numbers.
-- Use clear, reproducible reasoning steps (e.g., show which line items support your conclusion).
-- Output should be modular enough to scale across other companies and time periods.
-
-Respond only to this question from the user."""),
-    MessagesPlaceholder(variable_name = "chat_history"),
-
-    ("human", "{question}")
-])
-
-
-# String Output Parser
-from langchain_core.output_parsers import StrOutputParser
-parser = StrOutputParser()
-
-# Introducing Memory using Runnables
-from langchain_core.runnables import RunnableLambda, RunnableParallel, RunnablePassthrough
-
-memory_dict = {"history" : []}
-
-def get_history(input):
-    return memory_dict['history']
-
-runnable_memory = RunnableLambda(get_history)
-
-
-# Chaining with History
-chain = RunnablePassthrough.assign(chat_history = runnable_memory) | 
-
-
+import streamlit as st
+import zipfile
+
+from sentence_transformers import SentenceTransformer
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.schema.runnable import RunnableLambda, RunnablePassthrough
+from langchain.prompts.chat import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.messages import HumanMessage, AIMessage
+from langchain.schema.output_parser import StrOutputParser
+
+import base64
+
+st.set_page_config(page_title="ITC Financial Analysis with AI Scraping & LLM Integration", layout="centered")
+
+def set_background(local_image_path):
+    with open(local_image_path, "rb") as image_file:
+        encoded_string = base64.b64encode(image_file.read()).decode()
+    
+    bg_image_style = f"""
+    <style>
+        .stApp {{
+            background-image: url(data:image/{"png"};base64,{encoded_string});
+            background-size: cover;
+            background-repeat: no-repeat;
+            background-attachment: fixed;
+        }}
+    </style>
+    """
+    st.markdown(bg_image_style, unsafe_allow_html=True)
+
+set_background(r"Images/bkg2.jpg")
+
+# API key
+API_key = "AIzaSyCoFe_UHz_9zQzmB1KQgFp2A3G7m3KCe20"
+
+
+st.title("📊 ITC Financial Analysis with AI Scraping & LLM Integration")
+
+# Memory buffer for chat history
+memory_buffer = {"chat_history": []}
+
+# Minimalistic and background-matching style for the End Chat button
+end_chat_button = """
+<style>
+    .stButton>button {
+        background-color: rgba(255, 255, 255, 0.3);  /* Semi-transparent white */
+        color: white;
+        font-size: 16px;
+        border: 1px solid rgba(255, 255, 255, 0.5);
+        border-radius: 20px;
+        padding: 12px 24px;
+        cursor: pointer;
+        transition: background-color 0.3s ease, transform 0.2s ease;
+    }
+    .stButton>button:hover {
+        background-color: rgba(255, 255, 255, 0.6);  /* Lighter on hover */
+        transform: scale(1.05);  /* Subtle scaling effect */
+    }
+    .stButton>button:active {
+        transform: scale(0.98);  /* Slight shrink on click */
+    }
+</style>
+"""
+st.markdown(end_chat_button, unsafe_allow_html=True)
+
+
+# End chat button
+if st.button("End Chat 🛑"):
+    memory_buffer["chat_history"] = []
+
+# Load Chroma vector DB from zip
+with zipfile.ZipFile('chroma_db_32_backup.zip', 'r') as zip_ref:
+    zip_ref.extractall('chroma_db')
+
+# Embeddings and vector store
+embedding = HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2')
+vectorstore = Chroma(persist_directory='chroma_db', embedding_function=embedding)
+mmr_retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={"k": 3, "lambda_mult": 1})
+
+# Helper functions
+def format_docs(docs):
+    return "\n\n".join(doc.page_content for doc in docs)
+
+def get_docs_and_context(question):
+    docs = mmr_retriever.get_relevant_documents(question)
+    return {"question": question, "docs": docs, "context": format_docs(docs)}
+
+# Chain parts
+parallel_chain = RunnableLambda(lambda x: {
+    "question": x["input"],
+    **get_docs_and_context(x["input"])
+})
+
+chat_prompt = ChatPromptTemplate.from_messages([
+    ("system",
+     """
+     You are a domain-specific AI financial analyst focused on company-level performance evaluation.
+
+     Your task is to analyze and respond to user financial queries *strictly based on the provided transcript data*: {context}.
+
+     Rules:
+     1. ONLY extract facts, figures, and insights that are explicitly available in the transcript.
+     2. If data is *missing or partially available*, clearly state: "The required data is not available in the current transcript." Then provide a generic but relevant explanation based on standard financial principles.
+     3. Maintain numerical accuracy and avoid interpretation beyond data boundaries.
+     4. Prioritize answers relevant to *ITC Ltd.*, but keep response format adaptable to other firms and fiscal years.
+     5. Clearly present year-wise or metric-wise insights using bullet points or structured formats if applicable.
+
+     Your goals:
+     - Ensure 100% fidelity to source transcript.
+     - Do not assume or hallucinate missing numbers.
+     - Use clear, reproducible reasoning steps (e.g., show which line items support your conclusion).
+     - Output should be modular enough to scale across other companies and time periods.
+
+     Respond only to this question from the user.
+     """),
+    MessagesPlaceholder(variable_name="chat_history", optional=True),
+    ("human", "{input}")
+])
+
+llm = ChatGoogleGenerativeAI(api_key=API_key, model="gemini-2.0-flash-exp", temperature=1)
+parser = StrOutputParser()
+
+# Chat memory logic
+def get_history_from_buffer(_):
+    return memory_buffer['chat_history']
+
+runnable_get_history_from_buffer = RunnableLambda(get_history_from_buffer)
+
+main_chain = (
+    parallel_chain |
+    RunnableLambda(lambda x: {
+        "llm_input": {"input": x["question"], "context": x["context"]},
+        "docs": x["docs"]
+    }) |
+    RunnableLambda(lambda x: {
+        "result": (chat_prompt | llm | parser).invoke(x["llm_input"]),
+        "source_documents": x["docs"]
+    })
+)
+
+chain = RunnablePassthrough.assign(chat_history=runnable_get_history_from_buffer) | main_chain
+
+# Display previous chat messages
+for msg in memory_buffer["chat_history"]:
+    role = "user" if isinstance(msg, HumanMessage) else "assistant"
+    with st.chat_message(role):
+        st.markdown(msg.content)
+
+# Chat input
+user_input = st.chat_input("Ask a question about ITC's financials...")
+
+if user_input:
+    # Show user message
+    with st.chat_message("user"):
+        st.markdown(user_input)
+
+    # Add user input to memory
+    memory_buffer["chat_history"].append(HumanMessage(content=user_input))
+
+    # Call the chain
+    output = chain.invoke({"input": user_input})
+    ai_response = output["result"]
+
+    # Add AI response to memory
+    memory_buffer["chat_history"].append(AIMessage(content=ai_response))
+
+    # Show AI response
+    with st.chat_message("assistant"):
+        st.markdown(ai_response)
+
+        # Show sources
+        if output.get("source_documents"):
+            st.markdown("**Sources:**")
+            for doc in output["source_documents"]:
+                st.markdown(f"- {doc.metadata.get('source', 'Unknown document')}")