Update app.py

app.py

@@ -1,126 +1,30 @@
-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("https://github.com/Nike-one/BharatLAW/blob/master/images/banner.png?raw=true", 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)
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
+# Load the tokenizer and model from local files
 @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()
-
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("./", config="config.json")
+    model = AutoModelForCausalLM.from_pretrained("./")
+    return tokenizer, model
+
+# Initialize the model and tokenizer
+tokenizer, model = load_model()
+
+# Set up Streamlit page configuration
+st.set_page_config(page_title="Legal AI Chatbot", layout="centered")
+
+st.title("Legal AI Chatbot")
+st.write("This chatbot provides responses based on a legal language model.")
+
+# User input
+user_input = st.text_input("Enter your query:")
+
+if user_input:
+    # Tokenize and generate response
+    inputs = tokenizer.encode(user_input, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=150, num_return_sequences=1)
+    
+    # Decode and display the output
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    st.text_area("Response:", response, height=200)