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app.py

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+import os
+import streamlit as st
+import pandas as pd
+import pickle
+import base64
+from io import BytesIO, StringIO
+import sys
+import operator
+from typing import Literal, Sequence, TypedDict, Annotated, List, Dict, Tuple
+import tempfile
+import shutil
+import plotly.io as pio
+import io
+import re
+import json
+import openai
+# from fpdf import FPDF
+import base64
+from datetime import datetime
+from reportlab.lib.pagesizes import letter
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image
+from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+from reportlab.lib.units import inch
+from PIL import Image as PILImage
+
+# Import LangChain and LangGraph components
+from langchain_core.messages import AIMessage, ToolMessage, HumanMessage, BaseMessage
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from langchain_experimental.utilities import PythonREPL
+from langgraph.prebuilt import ToolInvocation, ToolExecutor
+from langchain_core.tools import tool
+from langgraph.prebuilt import InjectedState
+from langgraph.graph import StateGraph, END
+from reportlab.platypus import PageBreak
+
+
+# Initialize session state for AI provider settings
+if 'ai_provider' not in st.session_state:
+    st.session_state.ai_provider = "openai"
+    
+if 'api_key' not in st.session_state:
+    st.session_state.api_key = ""
+    
+if 'selected_model' not in st.session_state:
+    st.session_state.selected_model = "gpt-4"
+    
+# Define model options for each provider
+OPENAI_MODELS = ["gpt-4", "gpt-4-turbo", "gpt-4-mini", "gpt-3.5-turbo"]
+GROQ_MODELS = ["llama3.3-70b-versatile", "gemma2-9b-it", "llama-3-8b-8192"]
+
+# Create temporary directory for file storage
+if 'temp_dir' not in st.session_state:
+    st.session_state.temp_dir = tempfile.mkdtemp()
+    st.session_state.images_dir = os.path.join(st.session_state.temp_dir, "images/plotly_figures/pickle")
+    os.makedirs(st.session_state.images_dir, exist_ok=True)
+    print(f"Created temporary directory: {st.session_state.temp_dir}")
+    print(f"Created images directory: {st.session_state.images_dir}")
+
+# Define the system prompt
+SYSTEM_PROMPT = """## Role
+You are a professional data scientist helping a non-technical user understand, analyze, and visualize their data.
+
+## Capabilities
+1. **Execute python code** using the `complete_python_task` tool.
+
+## Goals
+1. Understand the user's objectives clearly.
+2. Take the user on a data analysis journey, iterating to find the best way to visualize or analyse their data to solve their problems.
+3. Investigate if the goal is achievable by running Python code via the `python_code` field.
+4. Gain input from the user at every step to ensure the analysis is on the right track and to understand business nuances.
+
+## Code Guidelines
+- **ALL INPUT DATA IS LOADED ALREADY**, so use the provided variable names to access the data.
+- **VARIABLES PERSIST BETWEEN RUNS**, so reuse previously defined variables if needed.
+- **TO SEE CODE OUTPUT**, use `print()` statements. You won't be able to see outputs of `pd.head()`, `pd.describe()` etc. otherwise.
+- **ONLY USE THE FOLLOWING LIBRARIES**:
+  - `pandas`
+  - `sklearn` (including all major ML models)
+  - `plotly`
+  - `numpy`
+  
+All these libraries are already imported for you.
+
+## Machine Learning Guidelines
+- For regression tasks:
+  - Linear Regression: `LinearRegression`
+  - Logistic Regression: `LogisticRegression`
+  - Ridge Regression: `Ridge`
+  - Lasso Regression: `Lasso`
+  - Random Forest Regression: `RandomForestRegressor`
+  
+- For classification tasks:
+  - Logistic Regression: `LogisticRegression`
+  - Decision Trees: `DecisionTreeClassifier`
+  - Random Forests: `RandomForestClassifier`
+  - Support Vector Machines: `SVC`
+  - K-Nearest Neighbors: `KNeighborsClassifier`
+  - Naive Bayes: `GaussianNB`
+  
+- For clustering:
+  - K-Means: `KMeans`
+  - DBSCAN: `DBSCAN`
+  
+- For dimensionality reduction:
+  - PCA: `PCA`
+  
+- Always preprocess data appropriately:
+  - Scale numerical features with `StandardScaler` or `MinMaxScaler`
+  - Encode categorical variables with `OneHotEncoder` when needed
+  - Handle missing values with `SimpleImputer`
+  
+- Always split data into training and testing sets using `train_test_split`
+- Evaluate models using appropriate metrics:
+  - For regression: `mean_squared_error`, `mean_absolute_error`, `r2_score`
+  - For classification: `accuracy_score`, `confusion_matrix`, `classification_report`
+  - For clustering: `silhouette_score`
+  
+- Consider using `cross_val_score` for more robust evaluation
+- Visualize ML results with plotly when possible
+
+## Plotting Guidelines
+- Always use the `plotly` library for plotting.
+- Store all plotly figures inside a `plotly_figures` list, they will be saved automatically.
+- Do not try and show the plots inline with `fig.show()`.
+"""
+
+# Define the State class
+class AgentState(TypedDict):
+    messages: Annotated[Sequence[BaseMessage], operator.add]
+    input_data: Annotated[List[Dict], operator.add]
+    intermediate_outputs: Annotated[List[dict], operator.add]
+    current_variables: dict
+    output_image_paths: Annotated[List[str], operator.add]
+
+# Initialize session state variables
+if 'in_memory_datasets' not in st.session_state:
+    st.session_state.in_memory_datasets = {}
+
+if 'persistent_vars' not in st.session_state:
+    st.session_state.persistent_vars = {}
+
+if 'dataset_metadata_list' not in st.session_state:
+    st.session_state.dataset_metadata_list = []
+
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+
+if 'dashboard_plots' not in st.session_state:
+    st.session_state.dashboard_plots = [None, None, None, None]
+
+if 'columns' not in st.session_state:
+    st.session_state.columns = ["No columns available"]
+
+if 'custom_plots_to_save' not in st.session_state:
+    st.session_state.custom_plots_to_save = {}
+
+# Set up the tools
+repl = PythonREPL()
+plotly_saving_code = """import pickle
+
+import uuid
+import os
+for figure in plotly_figures:
+    pickle_filename = f"{images_dir}/{uuid.uuid4()}.pickle"
+    with open(pickle_filename, 'wb') as f:
+        pickle.dump(figure, f)
+"""
+
+@tool
+def complete_python_task(
+    graph_state: Annotated[dict, InjectedState],
+    thought: str,
+    python_code: str
+) -> Tuple[str, dict]:
+    """Execute Python code for data analysis and visualization."""
+
+    current_variables = graph_state.get("current_variables", {})
+
+    # Load datasets from in-memory storage
+    for input_dataset in graph_state.get("input_data", []):
+        var_name = input_dataset.get("variable_name")
+        if var_name and var_name not in current_variables and var_name in st.session_state.in_memory_datasets:
+            print(f"Loading {var_name} from in-memory storage")
+            current_variables[var_name] = st.session_state.in_memory_datasets[var_name]
+    current_image_pickle_files = os.listdir(st.session_state.images_dir)
+
+    try:
+        # Capture stdout
+        old_stdout = sys.stdout
+        sys.stdout = StringIO()
+       
+        # Execute the code and capture the result
+        exec_globals = globals().copy()
+        exec_globals.update(st.session_state.persistent_vars)
+        exec_globals.update(current_variables)
+        
+        # Add scikit-learn modules to execution environment
+        import sklearn
+        import numpy as np
+        
+        # Import scikit-learn components
+        from sklearn.linear_model import LinearRegression, LogisticRegression, Ridge, Lasso # type: ignore
+        from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, GradientBoostingClassifier # type: ignore
+        from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
+        from sklearn.svm import SVC, SVR
+        from sklearn.naive_bayes import GaussianNB
+        from sklearn.decomposition import PCA
+        from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
+        from sklearn.cluster import KMeans, DBSCAN
+        from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
+        from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
+        from sklearn.metrics import (
+            accuracy_score, confusion_matrix, classification_report,
+            mean_squared_error, r2_score, mean_absolute_error, silhouette_score
+        )
+        from sklearn.pipeline import Pipeline
+        from sklearn.impute import SimpleImputer
+        
+        # Update execution globals with all ML components
+        exec_globals.update({
+            "plotly_figures": [], 
+            "images_dir": st.session_state.images_dir,
+            "np": np,
+            # Linear models
+            "LinearRegression": LinearRegression,
+            "LogisticRegression": LogisticRegression,
+            "Ridge": Ridge,
+            "Lasso": Lasso,
+            # Tree-based models
+            "DecisionTreeClassifier": DecisionTreeClassifier,
+            "DecisionTreeRegressor": DecisionTreeRegressor,
+            "RandomForestClassifier": RandomForestClassifier,
+            "RandomForestRegressor": RandomForestRegressor,
+            "GradientBoostingClassifier": GradientBoostingClassifier,
+            # SVM models
+            "SVC": SVC,
+            "SVR": SVR,
+            # Other models
+            "GaussianNB": GaussianNB,
+            "PCA": PCA,
+            "KNeighborsClassifier": KNeighborsClassifier,
+            "KNeighborsRegressor": KNeighborsRegressor,
+            "KMeans": KMeans,
+            "DBSCAN": DBSCAN,
+            # Preprocessing
+            "StandardScaler": StandardScaler,
+            "MinMaxScaler": MinMaxScaler,
+            "OneHotEncoder": OneHotEncoder,
+            "SimpleImputer": SimpleImputer,
+            # Model selection and evaluation
+            "train_test_split": train_test_split,
+            "cross_val_score": cross_val_score,
+            "GridSearchCV": GridSearchCV,
+            "accuracy_score": accuracy_score,
+            "confusion_matrix": confusion_matrix,
+            "classification_report": classification_report,
+            "mean_squared_error": mean_squared_error,
+            "r2_score": r2_score,
+            "mean_absolute_error": mean_absolute_error,
+            "silhouette_score": silhouette_score,
+            # Pipeline
+            "Pipeline": Pipeline
+        })
+        
+        exec(python_code, exec_globals)
+
+        st.session_state.persistent_vars.update({k: v for k, v in exec_globals.items() if k not in globals()})
+
+        # Get the captured stdout
+        output = sys.stdout.getvalue()
+
+        # Restore stdout
+        sys.stdout = old_stdout
+
+        updated_state = {
+            "intermediate_outputs": [{"thought": thought, "code": python_code, "output": output}],
+            "current_variables": st.session_state.persistent_vars
+        }
+
+        if 'plotly_figures' in exec_globals and exec_globals['plotly_figures']:
+            exec(plotly_saving_code, exec_globals)
+           
+            # Check if any images were created
+            new_image_folder_contents = os.listdir(st.session_state.images_dir)
+            new_image_files = [file for file in new_image_folder_contents if file not in current_image_pickle_files]
+           
+            if new_image_files:
+                updated_state["output_image_paths"] = new_image_files
+            st.session_state.persistent_vars["plotly_figures"] = []
+        return output, updated_state
+
+    except Exception as e:
+        sys.stdout = old_stdout  # Restore stdout in case of error
+        print(f"Error in complete_python_task: {str(e)}")
+        return str(e), {"intermediate_outputs": [{"thought": thought, "code": python_code, "output": str(e)}]}
+
+# Function to initialize the LLM based on selected provider and model
+def initialize_llm():
+    api_key = st.session_state.api_key
+    model = st.session_state.selected_model
+    
+    if not api_key:
+        return None
+    
+    try:
+        if st.session_state.ai_provider == "openai":
+            os.environ["OPENAI_API_KEY"] = api_key
+            return ChatOpenAI(model=model, temperature=0)
+        elif st.session_state.ai_provider == "groq":
+            os.environ["GROQ_API_KEY"] = api_key
+            # For Groq, set the base URL and use the model
+            from langchain_groq import ChatGroq
+            return ChatGroq(model=model, temperature=0)
+    except Exception as e:
+        print(f"Error initializing LLM: {str(e)}")
+        return None
+
+# Set up the tools
+tools = [complete_python_task]
+tool_executor = ToolExecutor(tools)
+
+# Load the prompt template
+chat_template = ChatPromptTemplate.from_messages([
+    ("system", SYSTEM_PROMPT),
+    ("placeholder", "{messages}"),
+])
+
+def create_data_summary(state: AgentState) -> str:
+    summary = ""
+    variables = []
+   
+    # Add sample data for each dataset
+    for d in state.get("input_data", []):
+        var_name = d.get("variable_name")
+        if var_name:
+           
+            variables.append(var_name)
+            summary += f"\n\nVariable: {var_name}\n"
+            summary += f"Description: {d.get('data_description', 'No description')}\n"
+
+            # Add sample data if available
+            if var_name in st.session_state.in_memory_datasets:
+                df = st.session_state.in_memory_datasets[var_name]
+                summary += "\nSample Data (first 5 rows):\n"
+                summary += df.head(5).to_string()
+
+    if "current_variables" in state:
+        remaining_variables = [v for v in state["current_variables"] if v not in variables and not v.startswith("_")]
+       
+        for v in remaining_variables:
+           
+            var_value = state["current_variables"].get(v)
+
+            if isinstance(var_value, pd.DataFrame):
+                summary += f"\n\nVariable: {v} (DataFrame with shape {var_value.shape})"
+            else:
+                summary += f"\n\nVariable: {v}"
+    return summary
+
+def route_to_tools(state: AgentState) -> Literal["tools", "__end__"]:
+    """Determine if we should route to tools or end the chain"""
+    if messages := state.get("messages", []):
+        ai_message = messages[-1]
+    else:
+        raise ValueError(f"No messages found in input state to tool_edge: {state}")
+
+    if hasattr(ai_message, "tool_calls") and len(ai_message.tool_calls) > 0:
+        return "tools"
+   
+    return "__end__"
+
+def call_model(state: AgentState):
+    """Call the LLM to get a response"""
+    current_data_template = """The following data is available:\n{data_summary}"""
+    current_data_message = HumanMessage(
+        content=current_data_template.format(data_summary=create_data_summary(state))
+    )
+    messages = [current_data_message] + state["messages"]
+    
+    # Get the initialized LLM
+    llm = initialize_llm()
+    if llm is None:
+        return {"messages": [AIMessage(content="Please configure a valid API key and model in the settings tab.")]}
+    
+    # Create the model with bound tools
+    model = llm.bind_tools(tools)
+    model = chat_template | model
+    
+    llm_outputs = model.invoke({"messages": messages})
+    return {"messages": [llm_outputs], "intermediate_outputs": [current_data_message.content]}
+
+def call_tools(state: AgentState):
+    """Execute tools called by the LLM"""
+    last_message = state["messages"][-1]
+    tool_invocations = []
+
+    if isinstance(last_message, AIMessage) and hasattr(last_message, 'tool_calls'):
+        tool_invocations = [
+            ToolInvocation(
+                tool=tool_call["name"],
+                tool_input={**tool_call["args"], "graph_state": state}
+            ) for tool_call in last_message.tool_calls
+        ]
+    responses = tool_executor.batch(tool_invocations, return_exceptions=True)
+
+    tool_messages = []
+    state_updates = {}
+
+    for tc, response in zip(last_message.tool_calls, responses):
+        if isinstance(response, Exception):
+            print(f"Exception in tool execution: {str(response)}")
+            tool_messages.append(ToolMessage(                
+                content=f"Error: {str(response)}",
+                name=tc["name"],
+                tool_call_id=tc["id"]
+            ))
+            continue
+
+        message, updates = response
+        tool_messages.append(ToolMessage(
+            content=str(message),
+            name=tc["name"],
+            tool_call_id=tc["id"]
+        ))
+
+        # Merge updates instead of overwriting
+        for key, value in updates.items():
+            if key in state_updates:
+                if isinstance(value, list) and isinstance(state_updates[key], list):
+                    state_updates[key].extend(value)
+                elif isinstance(value, dict) and isinstance(state_updates[key], dict):
+                    state_updates[key].update(value)
+                else:
+                    state_updates[key] = value
+            else:
+                state_updates[key] = value
+
+    if 'messages' not in state_updates:
+        state_updates["messages"] = []
+
+    state_updates["messages"] = tool_messages
+    return state_updates
+
+# Set up the graph
+workflow = StateGraph(AgentState)
+workflow.add_node("agent", call_model)
+workflow.add_node("tools", call_tools)
+workflow.add_conditional_edges(
+    "agent",
+    route_to_tools,
+    {
+        "tools": "tools",
+        "__end__": END
+    }
+)
+workflow.add_edge("tools", "agent")
+workflow.set_entry_point("agent")
+
+chain = workflow.compile()
+
+def process_file_upload(files):
+    """Process uploaded files and return dataframe previews and column names"""
+    st.session_state.in_memory_datasets = {}  # Clear previous datasets
+    st.session_state.dataset_metadata_list = []  # Clear previous metadata
+    st.session_state.persistent_vars.clear()  # Clear persistent variables for new session
+
+    if not files:
+        return "No files uploaded.", [], ["No columns available"]
+
+    results = []
+    all_columns = []  # Track all columns from all datasets
+
+    for file in files:
+        try:
+            # Use file object directly
+            if file.name.endswith('.csv'):
+                df = pd.read_csv(file)
+            elif file.name.endswith(('.xls', '.xlsx')):
+                df = pd.read_excel(file)
+            else:
+                results.append(f"Unsupported file format: {file.name}. Please upload CSV or Excel files.")
+                continue
+
+            var_name = file.name.split('.')[0].replace('-', '_').replace(' ', '_').lower()
+            st.session_state.in_memory_datasets[var_name] = df
+
+            # Collect all columns
+            all_columns.extend(df.columns.tolist())
+
+            # Create dataset metadata
+            dataset_metadata = {
+                "variable_name": var_name,
+                "data_path": "in_memory",
+                "data_description": f"Dataset containing {df.shape[0]} rows and {df.shape[1]} columns. Columns: {', '.join(df.columns.tolist())}",
+                "original_filename": file.name
+            }
+
+            st.session_state.dataset_metadata_list.append(dataset_metadata)
+
+            # Return preview of the dataset
+            preview = f"### Dataset: {file.name}\nVariable name: `{var_name}`\n\n"
+            preview += df.head(10).to_markdown()
+            results.append(preview)
+            print(f"Successfully processed {file.name}")
+
+        except Exception as e:
+            print(f"Error processing {file.name}: {str(e)}")
+            results.append(f"Error processing {file.name}: {str(e)}")
+
+    # Get unique columns
+    unique_columns = []
+    seen = set()
+
+    for col in all_columns:
+        if col not in seen:
+            seen.add(col)
+            unique_columns.append(col)
+
+    if not unique_columns:
+        unique_columns = ["No columns available"]
+
+    print(f"Found {len(unique_columns)} unique columns across datasets")
+    return "\n\n".join(results), st.session_state.dataset_metadata_list, unique_columns
+
+def get_columns():
+    """Directly gets columns from in-memory datasets"""
+    all_columns = []
+
+    for var_name, df in st.session_state.in_memory_datasets.items():
+        if isinstance(df, pd.DataFrame):
+            all_columns.extend(df.columns.tolist())
+
+    # Remove duplicates while preserving order
+    unique_columns = []
+    seen = set()
+   
+    for col in all_columns:
+        if col not in seen:
+            seen.add(col)
+            unique_columns.append(col)
+
+    if not unique_columns:
+        unique_columns = ["No columns available"]
+
+    print(f"Populating dropdowns with {len(unique_columns)} columns")
+    return unique_columns
+
+# === FUNCTIONS ===
+import openai
+import pandas as pd
+import json
+import re
+
+def standard_clean(df):
+    df.columns = [re.sub(r'\W+', '_', col).strip().lower() for col in df.columns]
+    df.drop_duplicates(inplace=True)
+    df.dropna(axis=1, how='all', inplace=True)
+    df.dropna(axis=0, how='all', inplace=True)
+    for col in df.select_dtypes(include='object').columns:
+        df[col] = df[col].astype(str).str.strip()
+    return df
+
+def query_openai(prompt):
+    try:
+        # Use the configured API key and model from session state
+        api_key = st.session_state.api_key
+        model = st.session_state.selected_model
+        
+        if st.session_state.ai_provider == "openai":
+            client = openai.OpenAI(api_key=api_key)
+            response = client.chat.completions.create(
+                model=model,
+                messages=[{"role": "user", "content": prompt}],
+                temperature=0.7
+            )
+            return response.choices[0].message.content
+        elif st.session_state.ai_provider == "groq":
+            from groq import Groq
+            client = Groq(api_key=api_key)
+            response = client.chat.completions.create(
+                model=model,
+                messages=[{"role": "user", "content": prompt}],
+                temperature=0.7
+            )
+            return response.choices[0].message.content
+    except Exception as e:
+        print(f"API Error: {e}")
+        return "{}"
+
+def llm_suggest_cleaning(df):
+    sample = df.head(10).to_csv(index=False)
+    prompt = f"""
+You are a professional data wrangler. Below is a sample of a messy dataset.
+
+Your task is to study the sample and provide a full data cleaning plan. Follow the structure below and provide **only a valid Python dictionary**.
+
+### Return a dictionary with these keys:
+
+1. **rename_columns** – rename unclear or inconsistent column names  
+2. **convert_types** – convert columns to appropriate datatypes: int, float, str, or date  
+3. **fill_missing** – fill missing values using the most suitable strategy for each column  
+4. **value_map** – map inconsistent values (e.g., yes/Yes/Y → Yes)  
+5. **handle_duplicates** – specify either "exact", "fuzzy", or "both"  
+6. **handle_outliers** – suggest per-column strategies for handling outliers  
+7. **text_standardization** – list columns for:
+   - "case_normalization"
+   - "remove_special_chars"
+   - "normalize_whitespace"
+
+8. **report_summary** – provide an example summary in the following structure:
+{{
+  "original_records": 4000,
+  "cleaned_records": 200,
+  "missing_values": {{"total": 0, "fixed": 0}},
+  "duplicates": {{"found": 3800, "removed": 3800}},
+  "outliers": {{"found": 60, "fixed": 13}},
+  "text_standardization": {{
+    "fields_modified": 600,
+    "case_fixed": 400,
+    "special_chars_fixed": 212,
+    "whitespace_fixed": 200
+  }},
+  "time_taken": "0.00 seconds"
+}}
+
+### For missing values, suggest the most suitable method based on column type and content:
+
+- "mean", "median" → for numerical columns  
+- "mode" → for categorical columns  
+- "knn", "interpolate", "forward_fill", "backward_fill" → for time series  
+- constant value (e.g., 0, "Unknown") → if appropriate  
+- "drop_rows_with_many_missing" → if a row has too many nulls
+
+### For outliers:
+Per-column, suggest one of the following:
+- "remove"
+- "replace_with_mean"
+- "replace_with_median"
+- "cap_iqr"
+- "cap_zscore"
+- "log_transform"
+- "flag_only"
+- "keep"
+
+### For duplicates:
+Choose: "exact", "fuzzy", or "both"
+
+### For text columns:
+Suggest columns for:
+- "case_normalization"
+- "remove_special_chars"
+- "normalize_whitespace"
+
+Do NOT drop any columns unless it’s extremely necessary. Ensure your output is a valid Python dictionary only.
+
+Sample data:
+{sample}
+"""
+    raw_response = query_openai(prompt)
+    try:
+        suggestions = eval(raw_response)
+        return suggestions
+    except Exception as e:
+        print("Could not parse suggestions:", e)
+        return {
+            "rename_columns": {},
+            "convert_types": {},
+            "fill_missing": {},
+            "value_map": {},
+            "handle_duplicates": "exact",
+            "handle_outliers": {},
+            "text_standardization": {
+                "case_normalization": [],
+                "remove_special_chars": [],
+                "normalize_whitespace": []
+            },
+            "report_summary": {}
+        }
+
+def apply_suggestions(df, suggestions):
+    df.rename(columns=suggestions.get("rename_columns", {}), inplace=True)
+
+    for col, dtype in suggestions.get("convert_types", {}).items():
+        if col not in df.columns:
+            continue
+        try:
+            if dtype == "int":
+                df[col] = pd.to_numeric(df[col], errors='coerce').astype("Int64")
+            elif dtype == "float":
+                df[col] = pd.to_numeric(df[col], errors='coerce')
+            elif dtype == "str":
+                df[col] = df[col].astype(str)
+            elif dtype == "date":
+                df[col] = pd.to_datetime(df[col], errors='coerce')
+        except:
+            print(f"Failed to convert {col} to {dtype}")
+
+    for col, method in suggestions.get("fill_missing", {}).items():
+        if col not in df.columns:
+            continue
+        try:
+            if method == "mean":
+                df[col].fillna(df[col].mean(), inplace=True)
+            elif method == "median":
+                df[col].fillna(df[col].median(), inplace=True)
+            elif method == "mode":
+                df[col].fillna(df[col].mode().iloc[0], inplace=True)
+            elif isinstance(method, str):
+                df[col].fillna(method, inplace=True)
+        except:
+            print(f"Could not fill missing values for {col}")
+
+    for col, mapping in suggestions.get("value_map", {}).items():
+        if col in df.columns:
+            try:
+                df[col] = df[col].replace(mapping)
+            except:
+                print(f"Could not map values in {col}")
+
+    return df
+
+def capture_dashboard_screenshot():
+    """Capture the entire dashboard as a single image"""
+    try:
+        # Create a figure that combines all dashboard plots
+        import plotly.graph_objects as go
+        from plotly.subplots import make_subplots
+       
+        # Create a 2x2 subplot
+        fig = make_subplots(rows=2, cols=2,
+                           subplot_titles=["Visualization 1", "Visualization 2",
+                                          "Visualization 3", "Visualization 4"])
+
+        # Add each plot from the dashboard to the combined figure
+        for i, plot in enumerate(st.session_state.dashboard_plots):
+            if plot is not None:
+                row = (i // 2) + 1
+                col = (i % 2) + 1
+
+                # Extract traces from the original figure and add to our subplot
+                for trace in plot.data:
+                    fig.add_trace(trace, row=row, col=col)
+               
+                # Copy layout properties for each subplot
+                for axis_type in ['xaxis', 'yaxis']:
+                    axis_name = f"{axis_type}{i+1 if i > 0 else ''}"
+                    subplot_name = f"{axis_type}{row}{col}"
+
+                    # Copy axis properties if they exist
+                    if hasattr(plot.layout, axis_name):
+                        axis_props = getattr(plot.layout, axis_name)
+                        fig.update_layout({subplot_name: axis_props})
+       
+        # Update layout for better appearance
+        fig.update_layout(
+            height=800,
+            width=1000,
+            title_text="Dashboard Overview",
+            showlegend=False,
+        )
+
+        # Save to a temporary file
+        dashboard_path = f"{st.session_state.temp_dir}/dashboard_combined.png"
+        fig.write_image(dashboard_path, scale=2)  # Higher scale for better resolution
+        return dashboard_path
+
+    except Exception as e:
+        import traceback
+        print(f"Error capturing dashboard: {str(e)}")
+        print(traceback.format_exc())
+        return None
+    
+def generate_enhanced_pdf_report():
+    """Generate an enhanced PDF report with proper handling of base64 image data"""
+    try:
+        # Create a buffer for the PDF
+        buffer = io.BytesIO()
+        
+        # Create the PDF document
+        doc = SimpleDocTemplate(buffer, pagesize=letter,
+                               leftMargin=36, rightMargin=36,
+                               topMargin=36, bottomMargin=36)
+
+        # Create custom styles with better formatting
+        styles = getSampleStyleSheet()
+        
+        # Add custom styles with improved formatting
+        styles.add(ParagraphStyle(
+            name='ReportTitle',
+            parent=styles['Heading1'],
+            fontSize=24,
+            alignment=1,  # Centered
+            spaceAfter=20,
+            textColor='#2C3E50'  # Dark blue color
+        ))
+        
+        styles.add(ParagraphStyle(
+            name='SectionHeader',
+            parent=styles['Heading2'],
+            fontSize=16,
+            spaceBefore=15,
+            spaceAfter=10,
+            textColor='#2C3E50',
+            borderWidth=1,
+            borderColor='#95A5A6',
+            borderPadding=5,
+            borderRadius=5
+        ))
+        
+        styles.add(ParagraphStyle(
+            name='SubHeader',
+            parent=styles['Heading3'],
+            fontSize=14,
+            spaceBefore=10,
+            spaceAfter=8,
+            textColor='#34495E',
+            fontWeight='bold'
+        ))
+        styles.add(ParagraphStyle(
+                name='UserMessage',
+                parent=styles['Normal'],
+                fontSize=11,
+                leftIndent=10,
+                spaceBefore=8,
+                spaceAfter=4
+            ))
+            
+        styles.add(ParagraphStyle(
+            name='AssistantMessage',
+            parent=styles['Normal'],
+            fontSize=11,
+            leftIndent=10,
+            spaceBefore=4,
+            spaceAfter=12,
+            textColor='#2980B9'
+        ))
+        
+        styles.add(ParagraphStyle(
+            name='Timestamp',
+            parent=styles['Italic'],
+            fontSize=10,
+            textColor='#7F8C8D',
+            alignment=2  # Right aligned
+        ))
+
+        # Create the document content
+        elements = []
+        
+        # Add title
+        elements.append(Paragraph('Data Analysis Report', styles['ReportTitle']))
+        
+        # Add timestamp
+        elements.append(Paragraph(f'Generated on: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}',
+                              styles['Timestamp']))
+        elements.append(Spacer(1, 0.5*inch))
+        
+        # Add conversation history with better formatting
+        elements.append(Paragraph('Analysis Conversation History', styles['SectionHeader']))
+        
+        if st.session_state.chat_history:
+            for i, (user_msg, assistant_msg) in enumerate(st.session_state.chat_history):
+                # Format user message with proper styling
+                elements.append(Paragraph(f'<b>You:</b>', styles['SubHeader']))
+                user_msg_formatted = user_msg.replace('\n', '<br/>')
+                elements.append(Paragraph(user_msg_formatted, styles['UserMessage']))
+                
+                # Process assistant message to handle visualization
+                # Look for markdown image syntax with base64 data
+                base64_pattern = r'!\[Visualization\]\(data:image\/png;base64,([^\)]+)\)'
+                
+                # Check if the message contains visualizations
+                if '### Visualizations' in assistant_msg or re.search(base64_pattern, assistant_msg):
+                    # Split the message at the Visualizations header if it exists
+                    if '### Visualizations' in assistant_msg:
+                        parts = assistant_msg.split('### Visualizations', 1)
+                        text_part = parts[0]
+                        viz_part = "### Visualizations" + parts[1] if len(parts) > 1 else ""
+                    else:
+                        # If no header but still has visualization
+                        match = re.search(base64_pattern, assistant_msg)
+                        text_part = assistant_msg[:match.start()]
+                        viz_part = assistant_msg[match.start():]
+                    
+                    # Format the text part
+                    elements.append(Paragraph(f'<b>Assistant:</b>', styles['SubHeader']))
+                    text_part = text_part.replace('\n', '<br/>')
+                    elements.append(Paragraph(text_part, styles['AssistantMessage']))
+                    
+                    # Process visualizations
+                    matches = re.findall(base64_pattern, viz_part)
+                    for j, base64_data in enumerate(matches):
+                        try:
+                            # Decode the base64 image
+                            image_data = base64.b64decode(base64_data)
+                            
+                            # Create a temporary file for the image
+                            temp_img_path = f"{st.session_state.temp_dir}/chat_viz_{i}_{j}.png"
+                            
+                            with open(temp_img_path, 'wb') as f:
+                                f.write(image_data)
+                            
+                            # Add the image to the PDF
+                            elements.append(Paragraph(f'<b>Visualization:</b>', styles['SubHeader']))
+                            elements.append(Spacer(1, 0.1*inch))
+                            img = Image(temp_img_path, width=6*inch, height=4*inch)
+                            elements.append(img)
+                            elements.append(Spacer(1, 0.2*inch))
+                        except Exception as e:
+                            print(f"Error processing base64 image: {str(e)}")
+                            elements.append(Paragraph(f"[Error displaying visualization: {str(e)}]", 
+                                                    styles['Normal']))
+                else:
+                    # No visualizations, just format the text
+                    elements.append(Paragraph(f'<b>Assistant:</b>', styles['SubHeader']))
+                    assistant_msg_formatted = assistant_msg.replace('\n', '<br/>')
+                    if len(assistant_msg_formatted) > 1500:
+                        assistant_msg_formatted = assistant_msg_formatted[:1500] + '...'
+                    elements.append(Paragraph(assistant_msg_formatted, styles['AssistantMessage']))
+                
+                elements.append(Spacer(1, 0.2*inch))
+        else:
+            elements.append(Paragraph('No conversation history available.', styles['Normal']))
+        
+        # Force a page break before the dashboard
+        elements.append(PageBreak())
+        
+        # Add dashboard section header
+        elements.append(Paragraph('Dashboard Overview', styles['SectionHeader']))
+        elements.append(Spacer(1, 0.2*inch))
+        
+        # Capture the dashboard as a single image
+        dashboard_img_path = capture_dashboard_screenshot()
+        
+        if dashboard_img_path:
+            # Calculate available width
+            available_width = doc.width
+            
+            # Create PIL image to get dimensions
+            pil_img = PILImage.open(dashboard_img_path)
+            img_width, img_height = pil_img.size
+            
+            # Calculate scaling factor to fit within page width
+            scale_factor = available_width / img_width
+            
+            # Calculate new height based on aspect ratio
+            new_height = img_height * scale_factor
+            
+            # Add the image with scaled dimensions
+            img = Image(dashboard_img_path, width=available_width, height=new_height)
+            elements.append(img)
+        else:
+            # Fallback: Add individual plots if combined dashboard fails
+            plot_count = 0
+            for i, plot in enumerate(st.session_state.dashboard_plots):
+                if plot is not None:
+                    plot_count += 1
+                    
+                    # Convert plotly figure to image
+                    img_bytes = io.BytesIO()
+                    plot.write_image(img_bytes, format='png', width=500, height=300)
+                    img_bytes.seek(0)
+                    
+                    # Create a temporary file for the image
+                    temp_img_path = f"{st.session_state.temp_dir}/plot_{i}.png"
+                    
+                    with open(temp_img_path, 'wb') as f:
+                        f.write(img_bytes.getvalue())
+                    
+                    # Add to PDF with appropriate caption and formatting
+                    elements.append(Paragraph(f'Dashboard Visualization {i+1}', styles['SubHeader']))
+                    elements.append(Spacer(1, 0.1*inch))
+                    
+                    # Add the image with proper scaling
+                    img = Image(temp_img_path, width=6.5*inch, height=4*inch)
+                    elements.append(img)
+                    elements.append(Spacer(1, 0.3*inch))
+            
+            if plot_count == 0:
+                elements.append(Paragraph('No visualizations have been added to the dashboard.',
+                                        styles['Normal']))
+        
+        # Build the PDF with improved formatting
+        doc.build(elements)
+        
+        # Get the value of the buffer
+        pdf_value = buffer.getvalue()
+        buffer.close()
+        
+        return pdf_value
+        
+    except Exception as e:
+        import traceback
+        print(f"Error generating enhanced PDF report: {str(e)}")
+        print(traceback.format_exc())
+        return None
+
+def chat_with_workflow(message, history, dataset_info):
+    """Send user query to the workflow and get response"""
+    
+    if not dataset_info:
+        return "Please upload at least one dataset before asking questions."
+
+    # Check if we have a valid API key and model
+    if not st.session_state.api_key:
+        return "Please set up your API key and model in the Settings tab before chatting."
+
+    print(f"Chat with workflow called with {len(dataset_info)} datasets")
+
+    try:
+        # Extract chat history for context (last 3 exchanges)
+        max_history = 3
+        previous_messages = []
+        
+        if history:
+            start_idx = max(0, len(history) - max_history)
+            recent_history = history[start_idx:]
+            
+            for exchange in recent_history:
+                if exchange[0]:  # User message
+                    previous_messages.append(HumanMessage(content=exchange[0]))
+                if exchange[1]:  # AI response
+                    previous_messages.append(AIMessage(content=exchange[1]))
+
+        # Initialize the workflow state
+        state = AgentState(
+            messages=previous_messages + [HumanMessage(content=message)],
+            input_data=dataset_info,
+            intermediate_outputs=[],
+            current_variables=st.session_state.persistent_vars,
+            output_image_paths=[]
+        )
+
+        # Execute the workflow
+        print("Executing workflow...")
+        result = chain.invoke(state)
+        print("Workflow execution completed")
+
+        # Extract messages from the result
+        messages = result["messages"]
+
+        # Format the response - only get the latest response
+        response = ""
+        if messages:
+            latest_message = messages[-1]  # Get only the last message
+            if hasattr(latest_message, "content"):
+                content = latest_message.content
+                
+                # Clean up the response
+                # Remove any instances where the user's message is repeated
+                if message in content:
+                    content = content.split(message)[-1].strip()
+                
+                # Remove any chat history markers
+                content_lines = content.split('\n')
+                filtered_lines = [line for line in content_lines 
+                                if not line.strip().startswith(("You:", "User:", "Human:", "Assistant:"))]
+                content = '\n'.join(filtered_lines)
+                
+                response = content.strip() + "\n\n"
+
+        # Handle visualizations
+        if "output_image_paths" in result and result["output_image_paths"]:
+            response += "### Visualizations\n\n"
+            for img_path in result["output_image_paths"]:
+                try:
+                    full_path = os.path.join(st.session_state.images_dir, img_path)
+                    with open(full_path, 'rb') as f:
+                        fig = pickle.load(f)
+
+                    # Convert plotly figure to image
+                    img_bytes = BytesIO()
+                    fig.update_layout(width=800, height=500)
+                    pio.write_image(fig, img_bytes, format='png')
+                    img_bytes.seek(0)
+
+                    # Convert to base64 for markdown image
+                    b64_img = base64.b64encode(img_bytes.read()).decode()
+                    response += f"![Visualization](data:image/png;base64,{b64_img})\n\n"
+                except Exception as e:                    
+                    response += f"Error loading visualization: {str(e)}\n\n"
+                    
+        return response
+
+    except Exception as e:
+        import traceback
+        print(f"Error in chat_with_workflow: {str(e)}")
+        print(traceback.format_exc())
+        return f"Error executing workflow: {str(e)}"
+
+def auto_generate_dashboard(dataset_info):
+    """Generate an automatic dashboard with four plots"""
+
+    if not dataset_info:
+        return "Please upload a dataset first.", [None, None, None, None]
+
+    prompt = """
+    You are a data visualization expert. Given a dataset, identify the top 4 most insightful plots using statistical reasoning or patterns (correlation, distribution, trends).
+
+    Use plotly and store the plots in a list named plotly_figures.
+
+    Include multivariate plots using color/size/facets when helpful.
+    """
+
+    state = AgentState(
+        messages=[HumanMessage(content=prompt)],
+        input_data=dataset_info,
+        intermediate_outputs=[],
+        current_variables=st.session_state.persistent_vars,
+        output_image_paths=[]
+    )
+
+    result = chain.invoke(state)
+    figures = []
+
+    if "output_image_paths" in result:
+        for img_path in result["output_image_paths"][:4]:
+            try:
+                full_path = os.path.join(st.session_state.images_dir, img_path)
+                with open(full_path, 'rb') as f:
+                    fig = pickle.load(f)
+                    figures.append(fig)
+            except Exception as e:
+                print(f"Error loading figure: {e}")
+
+    while len(figures) < 4:
+        figures.append(None)
+
+    st.session_state.dashboard_plots = figures
+    return "Dashboard generated!", figures
+
+def generate_custom_plots_with_llm(dataset_info, x_col, y_col, facet_col):
+    """Generate custom plots based on user-selected columns"""
+
+    if not dataset_info or not x_col or not y_col:
+        return [None, None, None]
+
+    prompt = f"""
+    You are a data visualization expert.
+
+    Create 3 insightful visualizations using Plotly based on:
+
+    - X-axis: {x_col}
+    - Y-axis: {y_col}
+    - Facet (optional): {facet_col if facet_col != 'None' else 'None'}
+
+    Try to find interesting relationships, trends, or clusters using appropriate chart types.
+
+    Use `plotly_figures` list and avoid using fig.show().
+    """
+
+    state = AgentState(
+        messages=[HumanMessage(content=prompt)],
+        input_data=dataset_info,
+        intermediate_outputs=[],
+        current_variables=st.session_state.persistent_vars,
+        output_image_paths=[]
+    )
+
+    result = chain.invoke(state)
+    figures = []
+
+    if "output_image_paths" in result:
+        for img_path in result["output_image_paths"][:3]:
+            try:
+                full_path = os.path.join(st.session_state.images_dir, img_path)
+                with open(full_path, 'rb') as f:
+                    fig = pickle.load(f)
+                    figures.append(fig)
+            except Exception as e:
+                print(f"Error loading figure: {e}")
+               
+    while len(figures) < 3:
+        figures.append(None)
+    return figures
+
+def remove_plot(index):
+    """Remove a plot from the dashboard"""
+    if 0 <= index < len(st.session_state.dashboard_plots):
+        st.session_state.dashboard_plots[index] = None
+
+def respond(message):
+    """Handle chat message response"""
+    if not st.session_state.dataset_metadata_list:
+        bot_message = "Please upload at least one dataset before asking questions."
+    else:
+        bot_message = chat_with_workflow(message, st.session_state.chat_history, st.session_state.dataset_metadata_list)
+
+    st.session_state.chat_history.append((message, bot_message))
+    st.rerun()
+
+def save_plot_to_dashboard(plot_index):
+    """Callback for the Add Plot button"""
+    for i in range(len(st.session_state.dashboard_plots)):
+        if st.session_state.dashboard_plots[i] is None:
+            # Found an empty slot
+            st.session_state.dashboard_plots[i] = st.session_state.custom_plots_to_save[plot_index]
+            return
+
+# New function to check if settings are valid
+def is_settings_valid():
+    """Check if API key and model are configured"""
+    return st.session_state.api_key != ""
+
+# Streamlit UI with left panel settings
+st.set_page_config(page_title="QueryMind 🧠", layout="wide")
+
+# Create side panel for settings
+with st.sidebar:
+    st.header("Settings")
+    st.info("⚠️ Configure your API settings before uploading data")
+    
+    # AI Provider selection
+    provider = st.radio("Select AI Provider", 
+                         options=["OpenAI", "Groq"],
+                         index=0 if st.session_state.ai_provider == "openai" else 1,
+                         horizontal=True)
+    
+    # Update session state based on selection
+    st.session_state.ai_provider = provider.lower()
+    
+    # API Key input
+    api_key = st.text_input("Enter API Key", 
+                           value=st.session_state.api_key, 
+                           type="password",
+                           help="Your API key for the selected provider")
+    
+    # Display different model options based on provider
+    if st.session_state.ai_provider == "openai":
+        model_options = OPENAI_MODELS
+        model_help = "GPT-4 provides the best results but is slower. GPT-3.5-Turbo is faster but less capable."
+    else:  # groq
+        model_options = GROQ_MODELS
+        model_help = "Llama 3.3 70B is most capable. Gemma 2 9B offers good balance. Llama 3 8B is fastest."
+    
+    # Model selection
+    selected_model = st.selectbox("Select Model", 
+                                 options=model_options,
+                                 index=model_options.index(st.session_state.selected_model) if st.session_state.selected_model in model_options else 0,
+                                 help=model_help)
+    
+    # Save button
+    if st.button("Save Settings"):
+        st.session_state.api_key = api_key
+        st.session_state.selected_model = selected_model
+        
+        # Test the API key and model
+        try:
+            # Initialize LLM using the provided settings
+            test_llm = initialize_llm()
+            if test_llm:
+                st.success(f"✅ Successfully configured {provider} with model: {selected_model}")
+            else:
+                st.error("Failed to initialize the AI provider. Please check your API key and model selection.")
+        except Exception as e:
+            st.error(f"Error testing settings: {str(e)}")
+    
+    # Display current settings
+    st.subheader("Current Settings")
+    settings_info = f"""
+    - **Provider**: {st.session_state.ai_provider.upper()}
+    - **Model**: {st.session_state.selected_model}
+    - **API Key**: {'✅ Set' if st.session_state.api_key else '❌ Not Set'}
+    """
+    st.markdown(settings_info)
+    
+    # Provider-specific information
+    if st.session_state.ai_provider == "openai":
+        with st.expander("OpenAI Models Info"):
+            st.info("""
+            - **GPT-4**: Most powerful model, best for complex analysis and detailed explanations
+            - **GPT-4-Turbo**: Faster than GPT-4 with similar capabilities
+            - **GPT-4-Mini**: Economical option with good performance for standard tasks
+            - **GPT-3.5-Turbo**: Fastest option, suitable for basic analysis and visualization
+            """)
+    else:
+        with st.expander("Groq Models Info"):
+            st.info("""
+            - **llama3.3-70b-versatile**: Most powerful model for comprehensive analysis
+            - **gemma2-9b-it**: Good balance of speed and capabilities
+            - **llama-3-8b-8192**: Fastest option for basic analysis tasks
+            """)
+    
+    # Integration instructions
+    with st.expander("How to get API Keys"):
+        if st.session_state.ai_provider == "openai":
+            st.markdown("""
+            ### Getting an OpenAI API Key
+            
+            1. Go to [OpenAI's platform](https://platform.openai.com)
+            2. Sign up or log in to your account
+            3. Navigate to the API section
+            4. Create a new API key
+            5. Copy the key and paste it above
+            
+            Note: OpenAI API usage incurs charges based on tokens used.
+            """)
+        else:
+            st.markdown("""
+            ### Getting a Groq API Key
+            
+            1. Go to [Groq's website](https://console.groq.com/keys)
+            2. Sign up or log in to your account
+            3. Navigate to API Keys section
+            4. Create a new API key
+            5. Copy the key and paste it above
+            
+            Note: Check Groq's pricing page for current rates.
+            """)
+
+# Main content
+st.title(" QueryMind 🧠 - Data Assistant ")
+st.markdown("Upload your datasets, ask questions, and generate visualizations to gain insights.")
+
+# Check if settings are valid before showing tabs
+if not is_settings_valid():
+    st.warning("⚠️ Please configure your API key in the sidebar settings panel before proceeding.")
+    st.stop()
+
+# Create main tabs - only if settings are valid
+tab1, tab2, tab3, tab4, tab5 = st.tabs(["Upload Datasets", "Data Cleaning", "Chat with AI Assistant", "Auto Dashboard Generator", "Generate Report"])
+
+with tab1:
+    st.header("Upload Datasets")
+    uploaded_files = st.file_uploader("Upload CSV or Excel Files",
+                                    accept_multiple_files=True,
+                                    type=['csv', 'xlsx', 'xls'])
+
+    if uploaded_files and st.button("Process Uploaded Files"):
+        with st.spinner("Processing files..."):
+            preview, metadata_list, columns = process_file_upload(uploaded_files)
+            st.session_state.columns = columns
+           
+            # Display basic information about processed files
+            st.success(f"✅ Successfully processed {len(uploaded_files)} file(s)")
+           
+            # Show detailed preview for each dataset
+            st.subheader("Dataset Previews")
+           
+            for dataset_name, df in st.session_state.in_memory_datasets.items():
+                with st.expander(f"Preview: {dataset_name}"):
+                    # Display dataset info
+                    st.write(f"**Rows:** {df.shape[0]} | **Columns:** {df.shape[1]}")
+                   
+                    # Display column information
+                    col_info = pd.DataFrame({
+                        'Column Name': df.columns,
+                        'Data Type': df.dtypes.astype(str),
+                        'Non-Null Count': df.count().values,
+                        'Sample Values': [', '.join(df[col].dropna().astype(str).head(3).tolist()) for col in df.columns]
+                    })
+                   
+                    # Show column information in a compact table
+                    st.write("**Column Information:**")
+                    st.dataframe(col_info, use_container_width=True)
+                   
+                    # Show actual data preview
+                    st.write("**Data Preview (First 10 rows):**")
+                    st.dataframe(df.head(10), use_container_width=True)
+           
+            # Provide hint for the next steps
+            st.info("👆 Click on the dataset names above to see detailed previews. Then proceed to the Data Cleaning tab to clean your data or Chat with AI Assistant to analyze it.")
+
+with tab2:
+    st.header("Data Cleaning")
+
+    if 'cleaning_done' not in st.session_state:
+        st.session_state.cleaning_done = False
+
+    if 'cleaned_datasets' not in st.session_state:
+        st.session_state.cleaned_datasets = {}
+
+    if 'cleaning_summaries' not in st.session_state:
+        st.session_state.cleaning_summaries = {}
+
+    if st.session_state.get("in_memory_datasets"):
+        if not st.session_state.cleaning_done:
+            if st.button("Run Data Cleaning"):
+                with st.spinner("Running LLM-assisted cleaning..."):
+                    for name, df in st.session_state.in_memory_datasets.items():
+                        raw_df = df.copy()
+                        df_std = standard_clean(raw_df.copy())
+                        suggestions = llm_suggest_cleaning(df_std.copy())
+                        df_clean = apply_suggestions(df_std.copy(), suggestions)
+                        st.session_state.cleaned_datasets[name] = df_clean
+                        st.session_state.cleaning_summaries[name] = suggestions
+                    st.session_state.cleaning_done = True
+                    st.rerun()
+            else:
+                st.info("Click Run Data Cleaning to clean your datasets using the LLM.")
+        else:
+            for name, df_clean in st.session_state.cleaned_datasets.items():
+                raw_df = st.session_state.in_memory_datasets[name]
+
+                st.subheader(f"Dataset: {name}")
+                col1, col2 = st.columns(2)
+
+                with col1:
+                    st.markdown("Original Data (First 5 Rows)")
+                    st.dataframe(raw_df.head())
+
+                with col2:
+                    st.markdown("Cleaned Data (First 5 Rows)")
+                    st.dataframe(df_clean.head())
+
+                st.markdown("Summary of Cleaning Actions")
+                suggestions = st.session_state.cleaning_summaries[name]
+                summary_text = ""
+
+                if suggestions:
+                    for key, value in suggestions.items():
+                        summary_text += f"**{key}**: {json.dumps(value, indent=2)}\n\n"
+                    st.markdown(summary_text)
+
+                st.markdown("Refine the Cleaning (Natural Language Instructions)")
+                user_input = st.text_input("Example: Convert 'dob' to datetime and fill missing with '2000-01-01'",
+                                           key=f"user_input_{name}")
+
+                if f'corrections_{name}' not in st.session_state:
+                    st.session_state[f'corrections_{name}'] = []
+                if st.button("Apply Correction", key=f'apply_correction_{name}'):
+                    if user_input.strip():
+                        correction_prompt = f"""
+You are a data cleaning expert working with pandas. Here's a summary of the previous cleaning:
+
+{summary_text}
+
+The user now asks:
+\"{user_input.strip()}\"
+
+Please return Python code that modifies the existing pandas DataFrame `df`. The code must:
+- Assume `df` is already loaded
+- Perform the action as described
+- End with `df` being the modified DataFrame
+- Please include all the libaries that needed to imported in order to run the code in the same code snippet
+
+ONLY return the code. Do not include explanations, markdown, or extra text.
+"""
+                        correction_code = query_openai(correction_prompt)
+
+                        # Clean LLM code block if wrapped in markdown syntax
+                        if correction_code.startswith("```"):
+                            correction_code = correction_code.strip().strip("`")  # remove backticks
+                            correction_code = correction_code.replace("python", "", 1).strip()
+
+
+                        try:
+                            df = st.session_state.cleaned_datasets[name].copy()
+                            local_vars = {"df": df}
+
+                            # Ensure code is executable and updates `df`
+                            exec(correction_code, {}, local_vars)
+                            df_updated = local_vars.get("df")
+
+                            if df_updated is not None and isinstance(df_updated, pd.DataFrame):
+                                st.session_state.cleaned_datasets[name] = df_updated
+                                st.session_state[f'corrections_{name}'].append((user_input, correction_code))
+                                st.success("Correction applied.")
+                                st.rerun()
+                            else:
+                                st.warning("LLM did not return a valid DataFrame. Here's the response:")
+                                st.code(correction_code, language="python")
+
+                        except Exception as e:
+                            st.error(f"Failed to apply correction: {str(e)}")
+                            st.code(correction_code, language="python")
+
+with tab3:
+    st.header("Chat with AI Assistant")
+   
+    st.markdown("""
+    ## Example Questions
+    - "What analysis can you perform on this dataset?"
+    - "Show me basic statistics for all columns"
+    - "Create a correlation heatmap"
+    - "Plot the distribution of a specific column"
+    - "What is the relationship between two columns?"
+    """)
+
+    # Display chat history
+    for exchange in st.session_state.chat_history:
+        with st.chat_message("user"):
+            st.write(exchange[0])
+        with st.chat_message("assistant"):
+            st.write(exchange[1])
+
+    # Chat input
+    if prompt := st.chat_input("Your question"):
+        with st.spinner("Thinking..."):
+            respond(prompt)
+
+with tab4:
+    st.header("Auto Dashboard Generator")
+
+    # Dashboard controls
+    dashboard_title = st.text_input("Dashboard Title", placeholder="Enter your dashboard title")
+ 
+    col1, col2 = st.columns(2)
+
+    with col1:
+        if st.button("Generate Suggested Dashboard (Auto)"):
+            with st.spinner("Generating dashboard..."):
+                message, figures = auto_generate_dashboard(st.session_state.dataset_metadata_list)
+                st.success(message)
+
+    with col2:
+        if st.button("Refresh Column Options"):
+            st.session_state.columns = get_columns()
+            st.rerun()
+
+    # Dashboard display
+    st.subheader("Dashboard")
+
+    # Row 1
+    col1, col2 = st.columns(2)
+
+    with col1:
+        if st.session_state.dashboard_plots[0]:
+            st.plotly_chart(st.session_state.dashboard_plots[0], use_container_width=True)
+            if st.button("Remove Plot 1"):
+                remove_plot(0)
+                st.rerun()
+
+    with col2:
+        if st.session_state.dashboard_plots[1]:
+            st.plotly_chart(st.session_state.dashboard_plots[1], use_container_width=True)
+            if st.button("Remove Plot 2"):
+                remove_plot(1)
+                st.rerun()
+
+    # Row 2
+    col3, col4 = st.columns(2)
+
+    with col3:
+        if st.session_state.dashboard_plots[2]:
+            st.plotly_chart(st.session_state.dashboard_plots[2], use_container_width=True)
+            if st.button("Remove Plot 3"):
+                remove_plot(2)
+                st.rerun()
+
+    with col4:
+        if st.session_state.dashboard_plots[3]:
+            st.plotly_chart(st.session_state.dashboard_plots[3], use_container_width=True)
+            if st.button("Remove Plot 4"):
+                remove_plot(3)
+                st.rerun()
+
+    # Custom plot generator
+    st.subheader("Custom Plot Generator")
+
+    # Column selection
+    col1, col2, col3 = st.columns(3)
+
+    with col1:
+        x_axis = st.selectbox("X-axis Column", options=st.session_state.columns)
+
+    with col2:
+        y_axis = st.selectbox("Y-axis Column", options=st.session_state.columns)
+
+    with col3:
+        facet = st.selectbox("Facet (optional)", options=["None"] + st.session_state.columns)
+ 
+    if st.button("Generate Custom Visualizations"):
+        with st.spinner("Generating custom visualizations..."):
+            custom_plots = generate_custom_plots_with_llm(st.session_state.dataset_metadata_list, x_axis, y_axis, facet)
+            # Store plots in session state
+            for i, plot in enumerate(custom_plots):
+                if plot:
+                    st.session_state.custom_plots_to_save[i] = plot
+
+            # Display custom plots with add buttons
+            for i, plot in enumerate(custom_plots):
+                if plot:
+                    st.plotly_chart(plot, use_container_width=True)
+                    st.button(
+                        f"Add Plot {i+1} to Dashboard",
+                        key=f"add_plot_{i}",
+                        on_click=save_plot_to_dashboard,
+                        args=(i,)
+                    )
+
+with tab5:
+    st.header("Generate Analysis Report")
+
+    st.markdown("""
+    Generate a PDF report containing:
+    - Dashboard visualizations
+    - Chat conversation history
+    """)
+
+    report_title = st.text_input("Report Title (Optional)", "Data Analysis Report")
+   
+    if st.button("Generate PDF Report"):
+        with st.spinner("Generating report..."):
+            pdf_data = generate_enhanced_pdf_report()
+            if pdf_data:
+                # Create download button for PDF
+                b64_pdf = base64.b64encode(pdf_data).decode('utf-8')
+                # Create download link
+                pdf_download_link = f'<a href="data:application/pdf;base64,{b64_pdf}" download="data_analysis_report.pdf">Download PDF Report</a>'
+                st.markdown("### Your report is ready!")
+                st.markdown(pdf_download_link, unsafe_allow_html=True)
+                # Preview option (simplified)
+                with st.expander("Preview Report"):
+                    st.warning("PDF preview is not available in Streamlit, please download the report to view it.")
+            else:
+                st.error("Failed to generate the report. Please try again.")
+
+# Cleanup on app exit
+def cleanup():
+    try:
+        shutil.rmtree(st.session_state.temp_dir)
+        print(f"Cleaned up temporary directory: {st.session_state.temp_dir}")
+    except Exception as e:
+        print(f"Error cleaning up: {e}")
+
+import atexit
+atexit.register(cleanup)

requirements.txt

@@ -0,0 +1,15 @@
+streamlit
+pandas
+numpy
+plotly
+langchain
+langgraph==0.2.74
+langchain-core
+langchain-groq
+langchain_openai
+langchain-experimental
+reportlab
+Pillow
+scikit-learn
+tabulate
+kaleido