Final work of v4

app.py

@@ -1,6 +1,5 @@
 import gradio as gr
 import pickle
-import os
 from langchain_community.vectorstores import FAISS
 from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain.prompts import PromptTemplate
@@ -26,11 +25,9 @@ embeddings = load_embeddings()
 # Load FAISS index and pickle file without explicitly defining paths
 def load_db():
     try:
-        # Load FAISS index directly from the folder "law_vector_db"
         db = FAISS.load_local("law_vector_db", embeddings, allow_dangerous_deserialization=True)
         print(f"FAISS index loaded successfully.")
 
-        # Load metadata from the pickle file located in the same directory as FAISS index
         with open('law_vector_db/index.pkl', 'rb') as pkl_file:
             metadata = pickle.load(pkl_file)
         print("Pickle file loaded successfully.")
@@ -62,8 +59,8 @@ ANSWER:</s>[INST]
 
 prompt = PromptTemplate(template=prompt_template, input_variables=['context', 'question', 'chat_history'])
 
-# Directly set Together API key
-TOGETHER_AI_API = "66bd7a6dc11956ddb311b773c0deabda8870e8c90e9f548ce064880ac47c4b05"  # Hardcoded API key
+# Together API Key (hardcoded)
+TOGETHER_AI_API = "66bd7a6dc11956ddb311b773c0deabda8870e8c90e9f548ce064880ac47c4b05"
 
 # Initialize LLM (Together API)
 llm = Together(
@@ -75,7 +72,7 @@ llm = Together(
 
 
 # Function to process user input and generate responses
-def ask_question(user_question, chat_history=""):
+def ask_question(user_question, chat_history=[]):
     try:
         # Retrieve relevant documents from FAISS index
         context_docs = db_retriever.get_relevant_documents(user_question)
@@ -86,26 +83,51 @@ def ask_question(user_question, chat_history=""):
         input_data = {
             "context": context,
             "question": user_question,
-            "chat_history": chat_history
+            "chat_history": "\n".join(chat_history)  # Chat history as string
         }
 
         # Generate the answer using Together API
         response = llm(prompt.format(**input_data))
         return response
-
     except Exception as e:
         return f"Error: {e}"
 
 
-# Set up Gradio interface
-iface = gr.Interface(
-    fn=ask_question,
-    inputs=[gr.Textbox(label="Ask a Question", placeholder="Type your question here..."),
-            gr.Textbox(label="Chat History (Optional)", placeholder="Type chat history here...", lines=2)],
-    outputs="text",
-    title="Legal Chatbot",
-    description="Ask questions about the Indian Penal Code."
-)
+# Function to manage conversation flow
+def chat_bot_interface(user_message, chat_history=[]):
+    if not user_message:
+        return chat_history, chat_history  # No update if message is empty
+
+    # Append user message
+    chat_history.append(("User", user_message))
+
+    # Get system response
+    response = ask_question(user_message, [msg[1] for msg in chat_history if msg[0] == "User"])
+
+    # Append system response
+    chat_history.append(("Assistant", response))
+
+    return chat_history, chat_history
+
+
+# Set up Gradio interface with a professional chatbot UI
+with gr.Blocks() as iface:
+    gr.Markdown("<h1 style='text-align: center;'>Legal Chatbot</h1>")
+
+    chatbot = gr.Chatbot(label="Chatbot Interface")
+    user_input = gr.Textbox(label="Ask a Question", placeholder="Type your question here...", lines=1)
+    clear_button = gr.Button("Clear")
+
+    # Maintain chat history
+    chat_history = gr.State([])
+
+
+    def clear_chat():
+        return [], []
+
+
+    user_input.submit(chat_bot_interface, inputs=[user_input, chat_history], outputs=[chatbot, chat_history])
+    clear_button.click(clear_chat, outputs=[chatbot, chat_history])
 
 # Launch the Gradio app
 if __name__ == "__main__":