Initial commit

app.py

@@ -0,0 +1,66 @@
+import streamlit as st
+import pandas as pd
+from io import StringIO
+from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering
+import numpy as np
+
+# Initialize TAPAS model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("google/tapas-large-finetuned-wtq")
+model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-large-finetuned-wtq")
+
+def ask_llm_chunk(chunk, questions):
+    chunk = chunk.astype(str)
+    inputs = tokenizer(table=chunk, queries=questions, padding="max_length", return_tensors="pt")
+    if inputs["input_ids"].shape[1] > 1024:
+        return ["Token limit exceeded for chunk"] * len(questions)
+    outputs = model(**inputs)
+    predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
+        inputs,
+        outputs.logits.detach(),
+        outputs.logits_aggregation.detach()
+    )
+    answers = []
+    for coordinates in predicted_answer_coordinates:
+        if len(coordinates) == 1:
+            answers.append(chunk.iat[coordinates[0]])
+        else:
+            cell_values = []
+            for coordinate in coordinates:
+                cell_values.append(chunk.iat[coordinate])
+            answers.append(", ".join(cell_values))
+    return answers
+
+MAX_ROWS_PER_CHUNK = 200
+
+def summarize_map_reduce(data, questions):
+    dataframe = pd.read_csv(StringIO(data))
+    num_chunks = len(dataframe) // MAX_ROWS_PER_CHUNK + 1
+    dataframe_chunks = np.array_split(dataframe, num_chunks)
+    all_answers = []
+    for chunk in dataframe_chunks:
+        chunk_answers = ask_llm_chunk(chunk, questions)
+        all_answers.extend(chunk_answers)
+    aggregated_answers = all_answers
+    return aggregated_answers
+
+st.title("TAPAS Table Question Answering")
+
+# Upload CSV data
+csv_file = st.file_uploader("Upload a CSV file", type=["csv"])
+if csv_file is not None:
+    data = csv_file.read().decode("utf-8")
+    st.write("CSV Data Preview:")
+    st.write(pd.read_csv(StringIO(data)).head())
+
+    # Input for questions
+    questions = st.text_area("Enter your questions (one per line)")
+    questions = questions.split("\n")  # split questions by line
+    questions = [q for q in questions if q]  # remove empty strings
+
+    if st.button("Submit"):
+        if data and questions:
+            answers = summarize_map_reduce(data, questions)
+            st.write("Answers:")
+            for q, a in zip(questions, answers):
+                st.write(f"Question: {q}")
+                st.write(f"Answer: {a}")