Update app.py

app.py

@@ -1,63 +1,144 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+from dataclasses import dataclass
+from typing import Literal
+import streamlit as st
+import os
+from llamaapi import LlamaAPI
+from langchain_experimental.llms import ChatLlamaAPI
+from langchain.embeddings import HuggingFaceEmbeddings
+import pinecone
+from langchain.vectorstores import Pinecone
+from langchain.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
+import streamlit.components.v1 as components
+from langchain_groq import ChatGroq
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ChatMessageHistory, ConversationBufferMemory
+import time
+
+HUGGINGFACEHUB_API_TOKEN = st.secrets['HUGGINGFACEHUB_API_TOKEN']
+
+@dataclass
+class Message:
+    """Class for keeping track of a chat message."""
+    origin: Literal["👤 Human", "👨🏻‍⚖️ Ai"]
+    message: str
+
+
+def download_hugging_face_embeddings():
+    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
+    return embeddings
+
+
+def initialize_session_state():
+    if "history" not in st.session_state:
+        st.session_state.history = []
+    if "conversation" not in st.session_state:
+        llama = LlamaAPI(st.secrets["LlamaAPI"])
+        model = ChatLlamaAPI(client=llama)
+        chat = ChatGroq(temperature=0.5, groq_api_key=st.secrets["Groq_api"], model_name="mixtral-8x7b-32768")
+
+        embeddings = download_hugging_face_embeddings()
+
+        # Initializing the Pinecone
+        pinecone.init(
+            api_key=st.secrets["PINECONE_API_KEY"],  # find at app.pinecone.io
+            environment=st.secrets["PINECONE_API_ENV"]  # next to api key in console
+        )
+        index_name = "legal-advisor"  # put in the name of your pinecone index here
+
+        docsearch = Pinecone.from_existing_index(index_name, embeddings)
+
+        prompt_template = """
+            You are a trained bot to guide people about Indian Law. You will answer user's query with your knowledge and the context provided. 
+            If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
+            Do not say thank you and tell you are an AI Assistant and be open about everything.
+            Use the following pieces of context to answer the users question.
+            Context: {context}
+            Question: {question}
+            Only return the helpful answer below and nothing else.
+            Helpful answer:
+            """
+
+        PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+        
+        #chain_type_kwargs = {"prompt": PROMPT}
+        message_history = ChatMessageHistory()
+        memory = ConversationBufferMemory(
+            memory_key="chat_history",
+            output_key="answer",
+            chat_memory=message_history,
+            return_messages=True,
+            )
+        retrieval_chain = ConversationalRetrievalChain.from_llm(llm=chat,
+                                                      chain_type="stuff",
+                                                      retriever=docsearch.as_retriever(
+                                                          search_kwargs={'k': 2}),
+                                                      return_source_documents=True,
+                                                      combine_docs_chain_kwargs={"prompt": PROMPT},
+                                                      memory= memory
+                                                     )
+
+        st.session_state.conversation = retrieval_chain
+
+
+def on_click_callback():
+    human_prompt = st.session_state.human_prompt
+    st.session_state.human_prompt=""
+    response = st.session_state.conversation(
+        human_prompt
+    )
+    llm_response = response['answer']
+    st.session_state.history.append(
+        Message("👤 Human", human_prompt)
+    )
+    st.session_state.history.append(
+        Message("👨🏻‍⚖️ Ai", llm_response)
+    )
+
+
+initialize_session_state()
+
+st.title("LegalEase Advisor Chatbot 🇮🇳")
+
+st.markdown(
+    """
+    👋 **Namaste! Welcome to LegalEase Advisor!**
+    I'm here to assist you with your legal queries within the framework of Indian law. Whether you're navigating through specific legal issues or seeking general advice, I'm here to help.
+    
+    📚 **How I Can Assist:**
+    
+    - Answer questions on various aspects of Indian law.
+    - Guide you through legal processes relevant to India.
+    - Provide information on your rights and responsibilities as per Indian legal standards.
+    
+    ⚖️ **Disclaimer:**
+    
+    While I can provide general information, it's essential to consult with a qualified Indian attorney for advice tailored to your specific situation.
+    
+    🤖 **Getting Started:**
+    
+    Feel free to ask any legal question related to Indian law, using keywords like "property rights," "labor laws," or "family law." I'm here to assist you!
+    Let's get started! How can I assist you today?
+    """
 )
 
+chat_placeholder = st.container()
+prompt_placeholder = st.form("chat-form")
+
+with chat_placeholder:
+    for chat in st.session_state.history:
+        st.markdown(f"{chat.origin} : {chat.message}")
 
-if __name__ == "__main__":
-    demo.launch()
+with prompt_placeholder:
+    st.markdown("**Chat**")
+    cols = st.columns((6, 1))
+    cols[0].text_input(
+        "Chat",
+        label_visibility="collapsed",
+        key="human_prompt",
+    )
+    cols[1].form_submit_button(
+        "Submit",
+        type="primary",
+        on_click=on_click_callback,
+    )