Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import json
+import os
+import plotly.express as px
+from transformers import pipeline
+from datasets import Dataset
+from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
+
+# Load the LLaMA-based model with RAG
+@st.cache(allow_output_mutation=True)
+def load_rag_model():
+    retriever = RagRetriever.from_pretrained("facebook/rag-token-base", index_name="custom")
+    tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-base")
+    model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-base", retriever=retriever)
+    return model, tokenizer, retriever
+
+model, tokenizer, retriever = load_rag_model()
+
+# Title of the app
+st.title("Interactive Insights Chatbot with LLaMA + RAG")
+
+# Step 1: Upload prompt.json file
+prompt_file = st.file_uploader("Upload your prompt.json file", type=["json"])
+if prompt_file:
+    prompt_data = json.load(prompt_file)
+    st.write("Prompt JSON loaded successfully!")
+
+# Step 2: Upload CSV file
+csv_file = st.file_uploader("Upload your CSV file", type=["csv"])
+if csv_file:
+    data = pd.read_csv(csv_file)
+    st.write("Data Preview:")
+    st.dataframe(data.head())
+
+    # Convert the CSV data to a Hugging Face Dataset for retrieval
+    dataset = Dataset.from_pandas(data)
+
+# Step 3: Natural language prompt input
+user_prompt = st.text_input("Enter your natural language prompt:")
+
+# Step 4: Process the user prompt and generate insights using LLaMA + RAG
+if user_prompt and csv_file:
+    st.write(f"Processing your prompt: '{user_prompt}'")
+
+    # Tokenize the prompt for LLaMA + RAG
+    inputs = tokenizer(user_prompt, return_tensors="pt")
+
+    # Perform retrieval-augmented generation (RAG) by retrieving data from the dataset and generating the response
+    generated = model.generate(input_ids=inputs['input_ids'], num_return_sequences=1, num_beams=2)
+
+    # Decode the output from the LLaMA + RAG model
+    output = tokenizer.batch_decode(generated, skip_special_tokens=True)
+
+    st.write(f"Insights generated: {output[0]}")
+
+    # Example: if the prompt asks for a plot (like "show sales over time")
+    if "plot sales" in user_prompt.lower():
+        # Create a bar chart (you can customize based on the prompt)
+        fig = px.bar(data, x='Date', y='Sales', title="Sales Over Time")
+        st.plotly_chart(fig)
+    else:
+        st.write("No recognized visual request in the prompt.")