Upload app.py

app.py

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+import time
+import os
+import streamlit as st
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain.memory import ConversationBufferWindowMemory
+from langchain.chains import ConversationalRetrievalChain
+from langchain_together import Together
+
+from footer import footer
+
+# Set the Streamlit page configuration and theme
+st.set_page_config(page_title="BharatLAW", layout="centered")
+
+# Display the logo image
+col1, col2, col3 = st.columns([1, 30, 1])
+with col2:
+    st.image("images/banner.png", use_column_width=True)
+
+def hide_hamburger_menu():
+    st.markdown("""
+        <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+        </style>
+        """, unsafe_allow_html=True)
+
+hide_hamburger_menu()
+
+# Initialize session state for messages and memory
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+if "memory" not in st.session_state:
+    st.session_state.memory = ConversationBufferWindowMemory(k=2, memory_key="chat_history", return_messages=True)
+
+@st.cache_resource
+def load_embeddings():
+    """Load and cache the embeddings model."""
+    return HuggingFaceEmbeddings(model_name="law-ai/InLegalBERT")
+
+embeddings = load_embeddings()
+db = FAISS.load_local("ipc_embed_db", embeddings, allow_dangerous_deserialization=True)
+db_retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 3})
+
+prompt_template = """
+<s>[INST]
+As a legal chatbot specializing in the Indian Penal Code, you are tasked with providing highly accurate and contextually appropriate responses. Ensure your answers meet these criteria:
+- Respond in a bullet-point format to clearly delineate distinct aspects of the legal query.
+- Each point should accurately reflect the breadth of the legal provision in question, avoiding over-specificity unless directly relevant to the user's query.
+- Clarify the general applicability of the legal rules or sections mentioned, highlighting any common misconceptions or frequently misunderstood aspects.
+- Limit responses to essential information that directly addresses the user's question, providing concise yet comprehensive explanations.
+- Avoid assuming specific contexts or details not provided in the query, focusing on delivering universally applicable legal interpretations unless otherwise specified.
+- Conclude with a brief summary that captures the essence of the legal discussion and corrects any common misinterpretations related to the topic.
+
+CONTEXT: {context}
+CHAT HISTORY: {chat_history}
+QUESTION: {question}
+ANSWER:
+- [Detail the first key aspect of the law, ensuring it reflects general application]
+- [Provide a concise explanation of how the law is typically interpreted or applied]
+- [Correct a common misconception or clarify a frequently misunderstood aspect]
+- [Detail any exceptions to the general rule, if applicable]
+- [Include any additional relevant information that directly relates to the user's query]
+</s>[INST]
+"""
+
+
+
+prompt = PromptTemplate(template=prompt_template,
+                        input_variables=['context', 'question', 'chat_history'])
+
+api_key = os.getenv('TOGETHER_API_KEY')
+llm = Together(model="mistralai/Mixtral-8x22B-Instruct-v0.1", temperature=0.5, max_tokens=1024, together_api_key=api_key)
+
+qa = ConversationalRetrievalChain.from_llm(llm=llm, memory=st.session_state.memory, retriever=db_retriever, combine_docs_chain_kwargs={'prompt': prompt})
+
+def extract_answer(full_response):
+    """Extracts the answer from the LLM's full response by removing the instructional text."""
+    answer_start = full_response.find("Response:")
+    if answer_start != -1:
+        answer_start += len("Response:")
+        answer_end = len(full_response)
+        return full_response[answer_start:answer_end].strip()
+    return full_response
+
+def reset_conversation():
+    st.session_state.messages = []
+    st.session_state.memory.clear()
+
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+
+input_prompt = st.chat_input("Say something...")
+if input_prompt:
+    with st.chat_message("user"):
+        st.markdown(f"**You:** {input_prompt}")
+
+    st.session_state.messages.append({"role": "user", "content": input_prompt})
+    with st.chat_message("assistant"):
+        with st.spinner("Thinking 💡..."):
+            result = qa.invoke(input=input_prompt)
+            message_placeholder = st.empty()
+            answer = extract_answer(result["answer"])
+
+            # Initialize the response message
+            full_response = "⚠️ **_Gentle reminder: We generally ensure precise information, but do double-check._** \n\n\n"
+            for chunk in answer:
+                # Simulate typing by appending chunks of the response over time
+                full_response += chunk
+                time.sleep(0.02)  # Adjust the sleep time to control the "typing" speed
+                message_placeholder.markdown(full_response + " |", unsafe_allow_html=True)
+
+        st.session_state.messages.append({"role": "assistant", "content": answer})
+
+        if st.button('🗑️ Reset All Chat', on_click=reset_conversation):
+            st.experimental_rerun()
+
+
+
+# Define the CSS to style the footer
+footer()
+
+