Update app.py

app.py

@@ -1,158 +1,158 @@
-
-import logging
-from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
-from langchain_huggingface import HuggingFaceEmbeddings
-from sentence_transformers import SentenceTransformer
-
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from langchain.prompts import PromptTemplate
-from langchain_together import Together
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-import streamlit as st
-import os
-
-from dotenv import load_dotenv
-import warnings
-logging.basicConfig(level=logging.DEBUG)  # Logs at DEBUG level and above
-logger = logging.getLogger(__name__)
-
-logger.debug("Starting Streamlit app...")
-# Suppress PyTorch FutureWarning
-warnings.filterwarnings("ignore", message="You are using `torch.load` with `weights_only=False`")
-warnings.filterwarnings("ignore", message="Tried to instantiate class '__path__._path'")
-warnings.filterwarnings("ignore", category=FutureWarning)
-# Suppress generic DeprecationWarnings (including LangChain)
-warnings.filterwarnings("ignore", category=DeprecationWarning)
-
-
-
-load_dotenv()
-TOGETHER_AI_API = os.getenv("TOGETHER_AI")
-
-# Streamlit Page Config
-st.set_page_config(page_title="Law4her")
-col1, col2, col3 = st.columns([1, 4, 1])
-with col2:
-    st.image(
-        "https://res.cloudinary.com/dzzhbgbnp/image/upload/v1736073326/lawforher_logo1_yznqxr.png"
-    )
-
-st.markdown(
-    """
-    <style>
-   div.stButton > button:first-child {
-    background-color: #ffffff; /* White background */
-    color: #000000; /* Black text */
-    border: 1px solid #000000; /* Optional: Add a black border */
-}
-
-div.stButton > button:active {
-    background-color: #e0e0e0; /* Slightly darker white for active state */
-    color: #000000; /* Black text remains the same */
-}
-
-    div[data-testid="stStatusWidget"] div button {
-        display: none;
-    }
-    .reportview-container {
-        margin-top: -2em;
-    }
-    #MainMenu {visibility: hidden;}
-    .stDeployButton {display:none;}
-    footer {visibility: hidden;}
-    #stDecoration {display:none;}
-    button[title="View fullscreen"]{
-        visibility: hidden;}
-    </style>
-    """,
-    unsafe_allow_html=True,
-)
-
-# Reset Conversation
-def reset_conversation():
-    st.session_state.messages = [{"role": "assistant", "content": "Hi, how can I help you?"}]
-    st.session_state.memory.clear()
-
-# Initialize chat messages and memory
-if "messages" not in st.session_state:
-    st.session_state.messages = [{"role": "assistant", "content": "Hi, how can I help you?"}]
-
-if "memory" not in st.session_state:
-    st.session_state.memory = ConversationBufferMemory(
-        memory_key="chat_history",
-        return_messages=True
-    )
-
-# Load embeddings and vectorstore
-embeddings = HuggingFaceEmbeddings(
-    model_name="nomic-ai/nomic-embed-text-v1",
-    model_kwargs={"trust_remote_code": True, "revision": "289f532e14dbbbd5a04753fa58739e9ba766f3c7"},
-)
-
-# Enable dangerous deserialization (safe only if the file is trusted and created by you)
-db = FAISS.load_local("ipc_vector_db", embeddings, allow_dangerous_deserialization=True)
-db_retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 2, "max_length": 512})
-
-prompt_template = """<s>[INST]As a legal chatbot specializing in the Indian Penal Code, provide a concise and accurate answer based on the given context. Avoid unnecessary details or unrelated content. Only respond if the answer can be derived from the provided context; otherwise, say "The information is not available in the provided context." 
-CONTEXT: {context}
-CHAT HISTORY: {chat_history}
-QUESTION: {question}
-ANSWER:
-</s>[INST]
-"""
-
-prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question", "chat_history"])
-
-# Initialize the Together API
-llm = Together(
-    model="mistralai/Mistral-7B-Instruct-v0.2",
-    temperature=0.5,
-    max_tokens=1024,
-    together_api_key=TOGETHER_AI_API,
-)
-
-qa = ConversationalRetrievalChain.from_llm(
-    llm=llm,
-    memory=st.session_state.memory,
-    retriever=db_retriever,
-    combine_docs_chain_kwargs={"prompt": prompt},
-)
-
-# Display chat history
-for message in st.session_state.messages:
-    with st.chat_message(message.get("role")):
-        st.write(message.get("content"))
-
-# User input
-input_prompt = st.chat_input("Ask a legal question about the Indian Penal Code")
-
-if input_prompt:
-    with st.chat_message("user"):
-        st.write(input_prompt)
-
-    st.session_state.messages.append({"role": "user", "content": input_prompt})
-
-    with st.chat_message("assistant"):
-        with st.status("Thinking 💡...", expanded=True):
-            try:
-                # Pass the user question
-                result = qa.invoke(input=input_prompt)
-                full_response = result.get("answer", "")
-
-                # Ensure the answer is a string
-                if isinstance(full_response, list):
-                    full_response = " ".join(full_response)
-                elif not isinstance(full_response, str):
-                    full_response = str(full_response)
-
-                # Display the response
-                st.session_state.messages.append({"role": "assistant", "content": full_response})
-                st.write(full_response)
-
-            except Exception as e:
-                st.error(f"Error occurred: {e}")
-
-# Add reset button
-st.button("Reset All Chat 🗑", on_click=reset_conversation)
+
+import logging
+from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
+from langchain_huggingface import HuggingFaceEmbeddings
+from sentence_transformers import SentenceTransformer
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain.prompts import PromptTemplate
+from langchain_together import Together
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+import streamlit as st
+import os
+
+from dotenv import load_dotenv
+import warnings
+logging.basicConfig(level=logging.DEBUG)  # Logs at DEBUG level and above
+logger = logging.getLogger(__name__)
+
+logger.debug("Starting Streamlit app...")
+# Suppress PyTorch FutureWarning
+warnings.filterwarnings("ignore", message="You are using `torch.load` with `weights_only=False`")
+warnings.filterwarnings("ignore", message="Tried to instantiate class '__path__._path'")
+warnings.filterwarnings("ignore", category=FutureWarning)
+# Suppress generic DeprecationWarnings (including LangChain)
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+
+
+load_dotenv()
+TOGETHER_AI_API = os.getenv("TOGETHER_AI")
+
+# Streamlit Page Config
+st.set_page_config(page_title="Law4her")
+col1, col2, col3 = st.columns([1, 4, 1])
+with col2:
+    st.image(
+        "https://res.cloudinary.com/dzzhbgbnp/image/upload/v1736073326/lawforher_logo1_yznqxr.png"
+    )
+
+st.markdown(
+    """
+    <style>
+   div.stButton > button:first-child {
+    background-color: #ffffff; /* White background */
+    color: #000000; /* Black text */
+    border: 1px solid #000000; /* Optional: Add a black border */
+}
+
+div.stButton > button:active {
+    background-color: #e0e0e0; /* Slightly darker white for active state */
+    color: #000000; /* Black text remains the same */
+}
+
+    div[data-testid="stStatusWidget"] div button {
+        display: none;
+    }
+    .reportview-container {
+        margin-top: -2em;
+    }
+    #MainMenu {visibility: hidden;}
+    .stDeployButton {display:none;}
+    footer {visibility: hidden;}
+    #stDecoration {display:none;}
+    button[title="View fullscreen"]{
+        visibility: hidden;}
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+# Reset Conversation
+def reset_conversation():
+    st.session_state.messages = [{"role": "assistant", "content": "Hi, how can I help you?"}]
+    st.session_state.memory.clear()
+
+# Initialize chat messages and memory
+if "messages" not in st.session_state:
+    st.session_state.messages = [{"role": "assistant", "content": "Hi, how can I help you?"}]
+
+if "memory" not in st.session_state:
+    st.session_state.memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        return_messages=True
+    )
+
+# Load embeddings and vectorstore
+embeddings = HuggingFaceEmbeddings(
+    model_name="nomic-ai/nomic-embed-text-v1",
+    model_kwargs={"trust_remote_code": True, "revision": "289f532e14dbbbd5a04753fa58739e9ba766f3c7"},
+)
+
+# Enable dangerous deserialization (safe only if the file is trusted and created by you)
+db = FAISS.load_local("ipc_vector_db", embeddings, allow_dangerous_deserialization=True)
+db_retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 2, "max_length": 512})
+
+prompt_template = """<s>[INST]As a legal chatbot specializing in the Indian Penal Code, provide a concise and accurate answer based on the given context. Avoid unnecessary details or unrelated content. Only respond if the answer can be derived from the provided context; otherwise, say "The information is not available in the provided context." Do not create your own questions and answers.  
+CONTEXT: {context}  
+CHAT HISTORY: {chat_history}  
+QUESTION: {question}  
+ANSWER:  
+</s>[INST]
+"""
+
+prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question", "chat_history"])
+
+# Initialize the Together API
+llm = Together(
+    model="mistralai/Mistral-7B-Instruct-v0.2",
+    temperature=0.5,
+    max_tokens=1024,
+    together_api_key=TOGETHER_AI_API,
+)
+
+qa = ConversationalRetrievalChain.from_llm(
+    llm=llm,
+    memory=st.session_state.memory,
+    retriever=db_retriever,
+    combine_docs_chain_kwargs={"prompt": prompt},
+)
+
+# Display chat history
+for message in st.session_state.messages:
+    with st.chat_message(message.get("role")):
+        st.write(message.get("content"))
+
+# User input
+input_prompt = st.chat_input("Ask a legal question about the Indian Penal Code")
+
+if input_prompt:
+    with st.chat_message("user"):
+        st.write(input_prompt)
+
+    st.session_state.messages.append({"role": "user", "content": input_prompt})
+
+    with st.chat_message("assistant"):
+        with st.status("Thinking 💡...", expanded=True):
+            try:
+                # Pass the user question
+                result = qa.invoke(input=input_prompt)
+                full_response = result.get("answer", "")
+
+                # Ensure the answer is a string
+                if isinstance(full_response, list):
+                    full_response = " ".join(full_response)
+                elif not isinstance(full_response, str):
+                    full_response = str(full_response)
+
+                # Display the response
+                st.session_state.messages.append({"role": "assistant", "content": full_response})
+                st.write(full_response)
+
+            except Exception as e:
+                st.error(f"Error occurred: {e}")
+
+# Add reset button
+st.button("Reset All Chat 🗑", on_click=reset_conversation)