Update app.py

app.py

@@ -1,70 +1,37 @@
-import streamlit as st
-from data_preparation import load_dataset
-from retrieval import load_embedding_model, retrieve_top_k
-from reranking import load_ranking_model, rerank
-from evaluation import evaluate_ndcg
-
-# Set up the Streamlit interface
-st.title("Multi-Stage Text Retrieval Pipeline for QA")
-
-# Query Input
-query = st.text_input("Enter a question:", "What is the capital of France?")
-
-# Embedding model selection
-embedding_model = st.selectbox(
-    "Select Embedding Model for Candidate Retrieval",
-    ["sentence-transformers/all-MiniLM-L6-v2", "nvidia/nv-embedqa-e5-v5"]
-)
-
-# Ranking model selection
-ranking_model = st.selectbox(
-    "Select Ranking Model for Re-Ranking",
-    ["cross-encoder/ms-marco-MiniLM-L-12-v2", "nvidia/nv-rerankqa-mistral-4b-v3"]
-)
-
-# Run retrieval pipeline on button click
-if st.button("Run Retrieval"):
-    # Load dataset
-    st.write("Loading dataset...")
-    corpus, queries, qrels = load_dataset("nq")
-
-    # Load selected embedding model
-    st.write(f"Loading embedding model: {embedding_model}...")
-    embed_model = load_embedding_model(embedding_model)
-
-    # Retrieve top-k passages using embedding model
-    st.write("Retrieving top-k passages...")
-    top_k_passages = retrieve_top_k(embed_model, query, corpus, k=10)
-    
-    # Display retrieved passages
-    st.write("Top-k passages before reranking:")
-    for i, (passage, score) in enumerate(top_k_passages):
-        st.write(f"{i+1}. Passage: {passage}, Score: {score:.4f}")
-
-    # Load selected ranking model
-    st.write(f"Loading ranking model: {ranking_model}...")
-    rank_model, rank_tokenizer = load_ranking_model(ranking_model)
-
-    # Rerank the retrieved passages
-    st.write("Reranking passages...")
-    ranked_passages = rerank(rank_model, rank_tokenizer, query, top_k_passages)
-    
-    # Display reranked passages
-    st.write("Top-k passages after reranking:")
-    for i, (passage, score) in enumerate(ranked_passages):
-        st.write(f"{i+1}. Passage: {passage}, Score: {score:.4f}")
-
-    # Evaluate using NDCG@10
-    st.write("Evaluating NDCG@10...")
-    query_id = list(queries.keys())[0]  # Assuming we are using the first query for evaluation
-    ndcg_score = evaluate_ndcg(ranked_passages, qrels[query_id])
-    st.write(f"NDCG@10: {ndcg_score:.4f}")
-
-# Sidebar with instructions
-st.sidebar.title("Instructions")
-st.sidebar.write("""
-1. Enter a question in the text input.
-2. Select the embedding model for candidate retrieval.
-3. Select the ranking model for reranking the retrieved passages.
-4. Click 'Run Retrieval' to start the pipeline and display the results.
-""")
+import streamlit as st
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer
+import torch
+
+# Load model and tokenizer
+@st.cache_resource
+def load_model():
+    model_path = 'C:\Users\neeli\Downloads\bert-tensorflow2-uncased-tf2-qa-v1' 
+    model = AutoModelForQuestionAnswering.from_pretrained(model_path)
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+# Function to get answer from question and context
+def get_answer(question, context):
+    inputs = tokenizer.encode_plus(question, context, return_tensors='pt')
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    answer_start = torch.argmax(outputs.start_logits)
+    answer_end = torch.argmax(outputs.end_logits) + 1
+    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs['input_ids'][0][answer_start:answer_end]))
+    return answer
+
+# Streamlit UI
+st.title("Question Answering Application")
+
+question = st.text_input("Enter your question:")
+context = st.text_area("Enter context text:", height=200)
+
+if st.button("Get Answer"):
+    if question and context:
+        answer = get_answer(question, context)
+        st.write(f"**Answer:** {answer}")
+    else:
+        st.warning("Please enter both a question and context.")