Create app2.py

app2.py

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+import streamlit as st
+import pandas as pd
+from pandasai import SmartDataframe
+from pandasai.llm import OpenAI
+from pandasai.llm.groq import ChatGroq
+from dotenv import load_dotenv
+from datasets import load_dataset
+import os
+
+def initialize_llm(model_choice):
+    """Initialize the chosen LLM based on the user's selection."""
+    groq_api_key = os.getenv("GROQ_API_KEY")
+    openai_api_key = os.getenv("OPENAI_API_KEY")
+
+    if model_choice == "llama-3.3-70b":
+        if not groq_api_key:
+            st.error("Groq API key is missing. Please set the GROQ_API_KEY environment variable.")
+            return None
+        return ChatGroq(groq_api_key=groq_api_key, model="groq/llama-3.3-70b-versatile")
+    elif model_choice == "GPT-4o":
+        if not openai_api_key:
+            st.error("OpenAI API key is missing. Please set the OPENAI_API_KEY environment variable.")
+            return None
+        return OpenAI(api_token=openai_api_key)
+
+def load_dataset_into_session():
+    """Load dataset from Hugging Face or via CSV upload."""
+    input_option = st.radio("Select Dataset Input:", ["Use Hugging Face Dataset", "Upload CSV File"])
+    
+    if input_option == "Use Hugging Face Dataset":
+        dataset_name = st.text_input("Enter Hugging Face Dataset Name:", value="HUPD/hupd")
+        if st.button("Load Dataset"):
+            try:
+                # Using the original parameters
+                dataset = load_dataset(
+                    dataset_name, 
+                    name="sample", 
+                    split="train", 
+                    trust_remote_code=True, 
+                    uniform_split=True
+                )
+                st.session_state.df = pd.DataFrame(dataset)
+                st.success(f"Dataset '{dataset_name}' loaded successfully!")
+                st.dataframe(st.session_state.df.head(10))
+            except Exception as e:
+                st.error(f"Error: {e}")
+    
+    elif input_option == "Upload CSV File":
+        uploaded_file = st.file_uploader("Upload CSV File:", type=["csv"])
+        if uploaded_file:
+            st.session_state.df = pd.read_csv(uploaded_file)
+            st.success("File uploaded successfully!")
+            st.dataframe(st.session_state.df.head(10))
+
+if "df" not in st.session_state:
+    st.session_state.df = None
+
+# Streamlit app
+st.title("Chat With Your Dataset Using PandasAI")
+
+# LLM Selection
+st.sidebar.title("Choose Your LLM")
+model_choice = st.sidebar.radio(
+    "Select a model:",
+    ("GPT-4o", "llama-3.3-70b"),
+    help="Choose between OpenAI GPT-4o or Groq Llama-3.3-70b."
+)
+
+# Initialize LLM
+llm = initialize_llm(model_choice)
+if not llm:
+    st.stop()
+
+# Dataset Loading
+#st.header("Dataset Selection")
+load_dataset_into_session()
+
+# Interact with DataFrame if it's loaded
+if st.session_state.df is not None:
+    st.subheader("Ask Questions About Your Dataset")
+    chat_df = SmartDataframe(st.session_state.df, config={"llm": llm})
+
+    user_query = st.text_input("Ask a question about your data:", "")
+    if user_query:
+        try:
+            response = chat_df.chat(user_query)
+            st.write("### Response:")
+            st.write(response)
+
+            # Check for keywords that indicate the user wants a plot
+            if any(keyword in user_query.lower() for keyword in ["plot", "graph", "draw", "visualize", "chart", "visualizations"]):
+                st.write("### Generating Plot...")
+                try:
+                    chat_df.chat(user_query)
+                except Exception as e:
+                    st.error(f"An error occurred while generating the plot: {e}")
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+else:
+    st.info("Please load a dataset to start interacting.")