Added v2 Code to tree main

app.py

@@ -1,12 +1,9 @@
 import gradio as gr
-import PyPDF2
-from langchain_community.vectorstores import FAISS  # Updated import
-from langchain_huggingface import HuggingFaceEmbeddings  # Updated import
+from huggingface_hub import hf_hub_download
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain.prompts import PromptTemplate
 from langchain_together import Together
-from huggingface_hub import hf_hub_download
-import faiss
-import pickle
 
 # Load embeddings
 embeddings = HuggingFaceEmbeddings(
@@ -14,22 +11,12 @@ embeddings = HuggingFaceEmbeddings(
     model_kwargs={"trust_remote_code": True, "revision": "289f532e14dbbbd5a04753fa58739e9ba766f3c7"}
 )
 
-# Download and load the FAISS index from the Hugging Face repo
-faiss_index_path = hf_hub_download(repo_id="sairamn/LawGPT", filename="index.faiss")
-db = faiss.read_index(faiss_index_path)
-
-# Download and load the pickle file
-pkl_index_path = hf_hub_download(repo_id="sairamn/LawGPT", filename="index.pkl")
-with open(pkl_index_path, 'rb') as f:
-    data = pickle.load(f)
+# Download FAISS index files from Hugging Face Hub
+index_file = hf_hub_download(repo_id="sairamn/LawGPT", filename="index.faiss")
+pkl_file = hf_hub_download(repo_id="sairamn/LawGPT", filename="index.pkl")
 
-# Create a FAISS vector store from the loaded data
-# Extracting the document store from the loaded data
-docstore = data['docstore']  # Ensure 'docstore' key exists in your pickle file
-index_to_docstore_id = data['index_to_docstore_id']  # Ensure 'index_to_docstore_id' key exists in your pickle file
-
-# Now initializing the FAISS with required arguments
-db = FAISS(embedding_function=embeddings.embed_documents, index=db, docstore=docstore, index_to_docstore_id=index_to_docstore_id)
+# Load FAISS index from the downloaded files
+db = FAISS.load_local(index_file, embeddings, allow_dangerous_deserialization=True)
 db_retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 4})
 
 # Define the prompt template for the chatbot
@@ -53,7 +40,7 @@ llm = Together(
     together_api_key=TOGETHER_AI_API
 )
 
-# Create a function to answer questions
+# Create a function to process user input and generate responses
 def ask_question(user_question):
     # Retrieve relevant documents
     context_docs = db_retriever.get_relevant_documents(user_question)
@@ -70,16 +57,18 @@ def ask_question(user_question):
 
     # Generate an answer
     response = llm(prompt.format(**input_data))
+
     return response
 
-# Gradio interface
+# Set up Gradio interface
 iface = gr.Interface(
     fn=ask_question,
-    inputs=gr.inputs.Textbox(label="Ask a question"),
-    outputs=gr.outputs.Textbox(label="Answer"),
-    title="IPC Legal Chatbot",
-    description="Ask questions related to the Indian Penal Code and get concise answers."
+    inputs=gr.inputs.Textbox(label="Ask a Question", placeholder="Type your question here..."),
+    outputs="text",
+    title="Legal Chatbot",
+    description="Ask questions about the Indian Penal Code."
 )
 
-# Launch the app
-iface.launch()
+# Launch the Gradio app
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

@@ -2,8 +2,9 @@ langchain
 faiss-cpu
 together
 transformers
-sentence-transformers
 langchain-community
 langchain-together
 einops
 PyPDF2
+huggingface-hub
+gradio