Create app.py

app.py

@@ -0,0 +1,97 @@
+import streamlit as st
+import logging
+from BanglaRAG.bangla_rag_pipeline import BanglaRAGChain
+import warnings
+
+warnings.filterwarnings("ignore")
+
+# Default constants for the script
+DEFAULT_CHAT_MODEL_ID = "hassanaliemon/bn_rag_llama3-8b"
+DEFAULT_EMBED_MODEL_ID = "l3cube-pune/bengali-sentence-similarity-sbert"
+DEFAULT_K = 4
+DEFAULT_TOP_K = 2
+DEFAULT_TOP_P = 0.6
+DEFAULT_TEMPERATURE = 0.6
+DEFAULT_CHUNK_SIZE = 500
+DEFAULT_CHUNK_OVERLAP = 150
+DEFAULT_MAX_NEW_TOKENS = 256
+
+# Set up logging
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+
+# Initialize and load the RAG model
+@st.cache_resource(show_spinner=False)
+def load_model(chat_model_id, embed_model_id, text_path, k, top_k, top_p, temperature, chunk_size, chunk_overlap, hf_token, max_new_tokens, quantization):
+    rag_chain = BanglaRAGChain()
+    rag_chain.load(
+        chat_model_id=chat_model_id,
+        embed_model_id=embed_model_id,
+        text_path=text_path,
+        k=k,
+        top_k=top_k,
+        top_p=top_p,
+        temperature=temperature,
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap,
+        hf_token=hf_token,
+        max_new_tokens=max_new_tokens,
+        quantization=quantization,
+    )
+    return rag_chain
+
+def main():
+    st.title("Bangla RAG Chatbot")
+    
+    # Sidebar for model configuration
+    st.sidebar.header("Model Configuration")
+    
+    chat_model_id = st.sidebar.text_input("Chat Model ID", DEFAULT_CHAT_MODEL_ID)
+    embed_model_id = st.sidebar.text_input("Embed Model ID", DEFAULT_EMBED_MODEL_ID)
+    k = st.sidebar.slider("Number of Documents to Retrieve (k)", 1, 10, DEFAULT_K)
+    top_k = st.sidebar.slider("Top K", 1, 10, DEFAULT_TOP_K)
+    top_p = st.sidebar.slider("Top P", 0.0, 1.0, DEFAULT_TOP_P)
+    temperature = st.sidebar.slider("Temperature", 0.0, 1.0, DEFAULT_TEMPERATURE)
+    max_new_tokens = st.sidebar.slider("Max New Tokens", 1, 512, DEFAULT_MAX_NEW_TOKENS)
+    chunk_size = st.sidebar.slider("Chunk Size", 100, 1000, DEFAULT_CHUNK_SIZE)
+    chunk_overlap = st.sidebar.slider("Chunk Overlap", 0, 500, DEFAULT_CHUNK_OVERLAP)
+    text_path = st.sidebar.text_input("Text File Path", "text.txt")
+    quantization = st.sidebar.checkbox("Enable Quantization (4-bit)", value=False)
+    show_context = st.sidebar.checkbox("Show Retrieved Context", value=False)
+
+    hf_token = st.text_input("Hugging Face API Token", type="password")
+    
+    # Load the model with the above configuration
+    rag_chain = load_model(
+        chat_model_id=chat_model_id,
+        embed_model_id=embed_model_id,
+        text_path=text_path,
+        k=k,
+        top_k=top_k,
+        top_p=top_p,
+        temperature=temperature,
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap,
+        hf_token=hf_token,
+        max_new_tokens=max_new_tokens,
+        quantization=quantization,
+    )
+    
+    st.write("### Enter your question:")
+    query = st.text_input("আপনার প্রশ্ন")
+
+    if st.button("Generate Answer"):
+        if query:
+            try:
+                answer, context = rag_chain.get_response(query)
+                st.write(f"**Answer:** {answer}")
+                if show_context:
+                    st.write(f"**Context:** {context}")
+            except Exception as e:
+                st.error(f"Couldn't generate an answer: {e}")
+        else:
+            st.warning("Please enter a query.")
+        
+if __name__ == "__main__":
+    main()