Upload app.py

app.py

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+import streamlit as st
+import pandas as pd
+from transformers import AutoTokenizer, BartForConditionalGeneration
+
+# Load the TAPEX tokenizer and model (replace with your fine-tuned model names)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wtq")
+model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-large-finetuned-wtq")
+
+
+def predict(table_path, query):
+  """
+  Predicts answer to a question using the TAPEX model on a given table.
+
+  Args:
+      table_path: Path to the CSV file containing the table data.
+      query: The question to be answered.
+
+  Returns:
+      The predicted answer as a string.
+  """
+  # Load the sales data from CSV
+  sales_record = pd.read_csv(r"C:/Users/sahit/Downloads/LLm of chatbot/10000 Sales Records.csv")
+  sales_record = sales_record.astype(str)  # Ensure string type for tokenizer
+
+  # Truncate the input to fit within the model's maximum sequence length
+  max_length = model.config.max_position_embeddings
+  encoding = tokenizer(table=sales_record, query=query, return_tensors="pt", truncation=True, max_length=max_length)
+
+  # Generate the output
+  outputs = model.generate(**encoding)
+
+  # Decode the output
+  prediction = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+  return prediction
+
+st.title("Chatbot with CSV using TAPEX")
+
+# Upload table data
+uploaded_file = st.file_uploader("Upload Sales Data (CSV)", type="csv")
+
+if uploaded_file is not None:
+  # Read the uploaded CSV file
+  df = pd.read_csv(uploaded_file)
+  st.write(df)  # Display the uploaded table
+
+  # User query input
+  query = st.text_input("Hello ! Ask me anything about " + uploaded_file.name + " 🤗")
+
+  if query:
+    # Predict answer using the model
+    prediction = predict(uploaded_file.name, query)
+    st.write(f"*Your Question:* {query}")
+    st.write(f"*Predicted Answer:* {prediction}")
+else:
+  st.info("Please upload a CSV file containing sales data.")