Update app.py

app.py

@@ -5,38 +5,38 @@ from langchain.llms import HuggingFacePipeline
 from langchain.chains import RetrievalQA
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 import json
-import os
 
-# Define JSON file path
-dataset_path = "/mnt/data/constitution_qa.json"
+# Streamlit UI
+st.title("Indian Constitution Q&A RAG App")
 
-# Load JSON dataset
-with open(dataset_path, "r", encoding="utf-8") as f:
-    data = json.load(f)
+# Upload JSON File
+uploaded_file = st.file_uploader("Upload Constitution JSON", type="json")
 
-# Extract questions and answers
-texts = [f"Q: {item['question']}\nA: {item['answer']}" for item in data]
+if uploaded_file is not None:
+    # Load JSON dataset
+    data = json.load(uploaded_file)
 
-# Load the embedding model
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    # Extract questions and answers
+    texts = [f"Q: {item['question']}\nA: {item['answer']}" for item in data]
 
-# Create FAISS vector database
-vector_db = FAISS.from_texts(texts, embeddings)
+    # Load the embedding model
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
 
-# Load Open-Source LLM (LLaMA-2 7B Open Chat Model)
-tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
-model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
-text_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
-llm = HuggingFacePipeline(pipeline=text_pipeline)
+    # Create FAISS vector database
+    vector_db = FAISS.from_texts(texts, embeddings)
 
-# Create RAG pipeline
-qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=vector_db.as_retriever())
+    # Load Open-Source LLM (LLaMA-2 7B Open Chat Model)
+    tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
+    model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
+    text_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
+    llm = HuggingFacePipeline(pipeline=text_pipeline)
 
-# Streamlit UI
-st.title("Indian Constitution Q&A RAG App")
-query = st.text_input("Enter your legal query:")
+    # Create RAG pipeline
+    qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=vector_db.as_retriever())
+
+    query = st.text_input("Enter your legal query:")
 
-if query:
-    response = qa_chain.run(query)
-    st.write("### AI-Generated Answer:")
-    st.write(response)
+    if query:
+        response = qa_chain.run(query)
+        st.write("### AI-Generated Answer:")
+        st.write(response)