Added v2 Code to tree main

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 from huggingface_hub import hf_hub_download
 from langchain_community.vectorstores import FAISS
-from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_huggingface import HuggingFaceEmbeddings  # Updated import
 from langchain.prompts import PromptTemplate
 from langchain_together import Together
 
@@ -31,7 +31,7 @@ ANSWER:
 prompt = PromptTemplate(template=prompt_template, input_variables=['context', 'question', 'chat_history'])
 
 # Set up Together API
-TOGETHER_AI_API = "66bd7a6dc11956ddb311b773c0deabda8870e8c90e9f548ce064880ac47c4b05"  # Replace this with your actual API key
+TOGETHER_AI_API = "your_together_api_key"  # Use a secure method to store and retrieve your API key
 
 llm = Together(
     model="mistralai/Mistral-7B-Instruct-v0.2",
@@ -41,7 +41,7 @@ llm = Together(
 )
 
 # Create a function to process user input and generate responses
-def ask_question(user_question):
+def ask_question(user_question, chat_history=""):
     # Retrieve relevant documents
     context_docs = db_retriever.get_relevant_documents(user_question)
 
@@ -52,7 +52,7 @@ def ask_question(user_question):
     input_data = {
         "context": context,
         "question": user_question,
-        "chat_history": ""  # You can maintain chat history if needed
+        "chat_history": chat_history  # Use provided chat history
     }
 
     # Generate an answer
@@ -63,7 +63,10 @@ def ask_question(user_question):
 # Set up Gradio interface
 iface = gr.Interface(
     fn=ask_question,
-    inputs=gr.inputs.Textbox(label="Ask a Question", placeholder="Type your question here..."),
+    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)  # Added chat history input
+    ],
     outputs="text",
     title="Legal Chatbot",
     description="Ask questions about the Indian Penal Code."
@@ -71,4 +74,4 @@ iface = gr.Interface(
 
 # Launch the Gradio app
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()

requirements.txt

@@ -1,10 +1,10 @@
+gradio
+huggingface_hub
 langchain
-faiss-cpu
-together
-transformers
+langchain-huggingface  # New package for HuggingFaceEmbeddings
 langchain-community
 langchain-together
+faiss-cpu
+sentence-transformers
 einops
-PyPDF2
-huggingface-hub
-gradio
+PyPDF2