diff --git a/demos/svc-sample.ipynb b/demos/svc-sample.ipynb
index 2710f7e..16dd421 100644
--- a/demos/svc-sample.ipynb
+++ b/demos/svc-sample.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -74,7 +74,7 @@
     "    shrinking=True,\n",
     "    probability=False, \n",
     "    tol=1e-3,     \n",
-    "    cache_size=200, \n",
+    "    cache_size=100, \n",
     "    class_weight=None,\n",
     "    verbose=False, \n",
     "    max_iter=-1,  \n",
@@ -127,57 +127,52 @@
     "    print(f\"\\nNote: Class {excluded_class} was intentionally excluded from the evaluation.\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Start logging with `logllm`"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\n",
-      "    \"method\": \"SVC\",\n",
-      "    \"dataset\": \"Iris\",\n",
-      "    \"task\": \"classification\",\n",
-      "    \"accuracy\": 1.0,\n",
-      "    \"C\": 1.0,\n",
-      "    \"degree\": 3,\n",
-      "    \"tol\": 0.001,\n",
-      "    \"cache_size\": 200,\n",
-      "    \"max_iter\": -1,\n",
-      "    \"test_size\": 0.2,\n",
-      "    \"random_state\": 42,\n",
-      "    \"kernel\": \"linear\",\n",
-      "    \"condition_as_natural_langauge\": [\n",
-      "        \"Using linear kernel on SVC model.\",\n",
-      "        \"Excluding class 2 from Iris dataset.\",\n",
-      "        \"Splitting data into 80% training and 20% testing.\",\n",
-      "        \"Shrinking enabled for SVC.\"\n",
-      "    ],\n",
-      "    \"advice_to_improve_acc\": [\n",
-      "        \"Consider using cross-validation for better performance evaluation.\",\n",
-      "        \"Experiment with different kernels to optimize results.\",\n",
-      "        \"Increase the dataset size to improve generalization.\"\n",
-      "    ]\n",
-      "}\n",
-      "Response from OpenAI logged to W&B.\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "from logllm import log_llm\n",
+    "from logllm.log_llm import log_llm\n",
+    "from logllm.plot import plot_metrics\n",
+    "from logllm.plot_v1 import plot_ml_metrics\n",
+    "\n",
+    "# code_string = extract_notebook_code(notebook_path)\n",
+    "notebook_path = \"svc-sample.ipynb\" \n",
+    "notebook_pat = \"train.ipynb\"\n",
+    "notebook_pa = \"cal.ipynb\"\n",
     "\n",
-    "notebook_path = \"svc-sample.ipynb\" # Here is target file to log\n",
+    "# Extract experimental conditions and results using log_llm\n",
+    "code_string = log_llm(notebook_path, provider=\"gemini\")\n",
+    "code_strin = log_llm(notebook_pat, provider=\"gemini\")\n",
+    "code_str = log_llm(notebook_pa, provider=\"gemini\")\n",
     "\n",
-    "log_llm(notebook_path,is_logging=False)"
+    "\n",
+    "# Plot the response from the query\n",
+    "plot_metrics(code_string, code_strin, code_str)\n",
+    "plot_ml_metrics(code_str)\n",
+    "\n"
    ]
   },
   {
@@ -214,27 +209,6 @@
     "\n",
     "```"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ChatCompletion(id='chatcmpl-9zwYXx1NmoGH6pF3HgxopPsanZpVl', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='To convert the user\\'s query into a W&B (Weights & Biases) API query, you might want to search for the best model based on certain metrics or criteria defined in your W&B workspace. Here\\'s a sample API query to retrieve the best model:\\n\\n```python\\nimport wandb\\n\\n# Initialize a W&B run\\nwandb.login()\\n\\n# Define your project and entity\\nproject = \\'your_project_name\\'\\nentity = \\'your_entity_name\\'\\n\\n# Retrieve the best model based on a specific metric\\nbest_model = wandb.Api().runs(f\"{entity}/{project}\", {\"order\": \"-summary.best_metric_name\"}).get(0)\\n\\n# Display the best model details\\nprint(\"Best Model ID:\", best_model.id)\\nprint(\"Best Model Summary:\", best_model.summary)\\n```\\n\\nReplace `your_project_name`, `your_entity_name`, and `summary.best_metric_name` with the appropriate values for your specific use case.', refusal=None, role='assistant', function_call=None, tool_calls=None))], created=1724550125, model='gpt-4o-mini-2024-07-18', object='chat.completion', service_tier=None, system_fingerprint='fp_48196bc67a', usage=CompletionUsage(completion_tokens=186, prompt_tokens=40, total_tokens=226))\n"
-     ]
-    }
-   ],
-   "source": [
-    "from logllm import query\n",
-    "\n",
-    "notebook_path = \"svc-sample.ipynb\" # Here is target file to log\n",
-    "\n",
-    "query(\"what is  the best model?\")"
-   ]
   }
  ],
  "metadata": {
diff --git a/extractor.py b/extractor.py
deleted file mode 120000
index 531c796..0000000
--- a/extractor.py
+++ /dev/null
@@ -1 +0,0 @@
-../logllm/extractor.py
\ No newline at end of file
diff --git a/logllm/log_llm.py b/logllm/log_llm.py
index d3cfbe6..7c5ca77 100644
--- a/logllm/log_llm.py
+++ b/logllm/log_llm.py
@@ -4,119 +4,123 @@
 import json
 import os
 from dotenv import load_dotenv
-import markdown as md
 from openai import OpenAI
 
-# Load environment variables
-load_dotenv()
-
-# Configure Google Generative AI
-genai.configure(api_key=os.getenv('API_KEY'))
-
-generation_config = {
-    "temperature": 0,
-    "top_p": 0.95,
-    "top_k": 64,
-    "max_output_tokens": 8192,
-    "response_mime_type": "application/json",
-}
-
-model = genai.GenerativeModel(
-    model_name="gemini-1.5-flash",
-    generation_config=generation_config,
-)
-
 def init_wandb(project_name):
     wandb.init(project=project_name, settings=wandb.Settings(_disable_stats=True))
 
-def extract_experimental_conditions_gemini(code):
-    user_input = f"""
-         You are an advanced machine learning experiment designer.
-         Extract all experimental conditions and results for logging via wandb API. 
-         Add your original parameters in your JSON response if you want to log other parameters.
-         Extract all information you can find in the given script as int, bool, or float values.
-         If you cannot describe conditions with int, bool, or float values, use a list of natural language.
-         Give advice to improve the accuracy.
-         If you use natural language, the answers should be short.
-         Do not include information already provided in param_name_1 for `condition_as_natural_language`.
-         
-         Here is a user's Jupyter Notebook script: {code}
-    """.replace("    ","")
-
-    chat_session = model.start_chat(
-        history=[
-            {"role": "user", "parts": ["Hello! help me analyse data in json format only"]},
-            {"role": "model", "parts": ["Sure I can do that, provide me with data"]},
-        ]
-    )
+# Load environment variables from a .env file
+load_dotenv()
 
-    response = chat_session.send_message(user_input)
-    result = response.candidates[0].content.parts[0].text
-    
-    parsed_json = json.loads(result)
-    formatted_json = json.dumps(parsed_json, indent=4, ensure_ascii=False)
-    print("response: ", formatted_json)
-    return formatted_json
-
-def extract_experimental_conditions_openai(code):
-    client = OpenAI()
-
-    system_prompt = """
-        You are an advanced machine learning experiment designer.
-        Extract all experimental conditions and results for logging via wandb API.
-        Add your original parameters in your JSON response if you want to log other parameters.
-        Extract all information you can find in the given script as int, bool, or float values.
-        If you cannot describe conditions with int, bool, or float values, use a list of natural language.
-        Give advice to improve the accuracy.
-        If you use natural language, the answers should be very short.
-        Do not include information already provided in param_name_1 for `condition_as_natural_language`.
-    """.replace("    ", "")
-
-    user_prompt = f"""
-    Here is a user's Jupyter Notebook script:{code}
-    """
-
-    response = client.chat.completions.create(
-        model="gpt-4o-mini-2024-07-18",
-        messages=[
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_prompt},
-        ],
-        response_format={"type": "json_object"},
+# Function to configure Google Generative AI only when needed
+def configure_google_genai():
+    # Set up Google Generative AI with API key and model configuration
+    genai.configure(api_key=os.getenv('API_KEY'))
+
+    # Define generation settings for the model
+    generation_config = {
+        "temperature": 0,  # Controls the randomness of the output
+        "top_p": 0.95,     # Nucleus sampling parameter
+        "top_k": 64,       # Limits the pool of candidates to the top-k
+        "max_output_tokens": 8192,  # Maximum number of tokens in the output
+        "response_mime_type": "application/json",  # Expected response format
+    }
+
+    # Initialize and return the GenerativeModel instance
+    return genai.GenerativeModel(
+        model_name="gemini-1.5-flash",
+        generation_config=generation_config,
     )
 
-    parsed_json = json.loads(response.choices[0].message.content)
-    formatted_json = json.dumps(parsed_json, indent=4, ensure_ascii=False)
-    print(formatted_json)
+# System prompt for guiding the AI model to extract experiment details
+system_prompt = """
+    You are an advanced machine learning experiment designer.
+    Extract all experimental conditions and results for logging via wandb API. 
+    Add your original parameters in your JSON response if you want to log other parameters.
+    Extract all information you can find in the given script as int, bool, or float values.
+    If you cannot describe conditions with int, bool, or float values, use a list of natural language.
+    Give advice to improve the accuracy.
+    If you use natural language, the answers should be very short.
+    Do not include information already provided in param_name_1 for `condition_as_natural_language`.
+    Output JSON schema example:
+    This is just an example, make changes as necessary. Use nested dictionaries if necessary.
+    {{
+        "method":"str",
+        "dataset":"str",
+        "task":"str",
+        "accuracy":"",
+        "other_param_here":{
+            "other_param_here":"",
+            "other_param_here":"",
+        },
+        "other_param_here":"",
+        ...
+        "condition_as_natural_language":["Small dataset."],
+        "advice_to_improve_acc":["Use a bigger dataset.","Use a simpler model."]
+    }}
+""".replace("    ", "")
+
+# Function to extract experimental conditions using the specified provider (Google or OpenAI)
+def extract_experimental_conditions(provider, code):
+    # Combine system prompt with user's code input
+    user_input = f"{system_prompt}\n\nHere is a user's Jupyter Notebook script: {code}"
 
-    return response.choices[0].message.content
+    if provider == "gemini":
+        # Configure and use Google Generative AI if specified
+        model = configure_google_genai()
+        chat_session = model.start_chat(
+            history=[{"role": "user", "parts": ["Hello! help me analyze data in JSON format only and return only json object nothing else"]}]
+        )
+        response = chat_session.send_message(user_input)
+        result = response.candidates[0].content.parts[0].text
+
+    elif provider == "openai":
+        # Use OpenAI's API to get the response
+        client = OpenAI()
+        response = client.chat.completions.create(
+            model="gpt-4o-mini-2024-07-18",
+            messages=[
+                {"role": "system", "content": user_input},
+            ],
+            response_format={"type": "json_object"},
+        )
+        result = response.choices[0].message.content
 
+    else:
+        # Raise an error if an invalid provider is specified
+        raise ValueError("Invalid provider specified. Use 'gemini' or 'openai'.")
+
+    # Parse the result from JSON string to Python dictionary
+    result = json.loads(result)
+    # Format the JSON output for better readability
+    return json.dumps(result, indent=4, ensure_ascii=False)
+
+# Function to log the extracted information to Weights & Biases (W&B)
 def log_to_wandb(response_text):
     try:
+        # Parse the JSON response and log it to W&B
         response_dict = json.loads(response_text)
         wandb.log(response_dict)
-    except json.JSONDecodeError as e:
-        print(f"Error parsing JSON: {e}")
-    except Exception as e:
+    except (json.JSONDecodeError, Exception) as e:
+        # Handle errors in JSON parsing or W&B logging
         print(f"Error logging to W&B: {e}")
 
+# Main function to extract and log experimental conditions from a Jupyter Notebook
 def log_llm(notebook_path, project_name=None, is_logging=False, provider=None):
-    if project_name is None:
-        project_name = os.path.basename(notebook_path).replace(".ipynb", "")
-    
+    # Use the notebook file name as the project name if not specified
+    project_name = project_name or os.path.basename(notebook_path).replace(".ipynb", "")
     if is_logging:
+        # Initialize a new W&B run if logging is enabled
         init_wandb(project_name)
 
+    # Extract the code from the notebook
     code_string = extract_notebook_code(notebook_path)
-
-    if provider == "gemini":
-        parsed_json = extract_experimental_conditions_gemini(code_string)
-    elif provider == "openai":
-        parsed_json = extract_experimental_conditions_openai(code_string)
-    else:
-        raise ValueError("Invalid provider specified. Use 'gemini' or 'openai'.")
+    # Extract the experimental conditions using the specified AI provider
+    parsed_json = extract_experimental_conditions(provider, code_string)
 
     if is_logging and parsed_json:
+        # Log the extracted information to W&B
         log_to_wandb(parsed_json)
 
-    print("Response from the provider processed and logged to W&B.")
+    # Inform the user that the process is complete
+    print("Response from the provider processed and logged to W&B.")
\ No newline at end of file
diff --git a/logllm/plot.py b/logllm/plot.py
new file mode 100644
index 0000000..799d24e
--- /dev/null
+++ b/logllm/plot.py
@@ -0,0 +1,85 @@
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+
+def plot_metrics(*models_results):
+    # Process each model to ensure it's in dictionary format
+    processed_models = []
+    for model in models_results:
+        # Convert to dictionary if the input is a JSON string
+        if isinstance(model, str):
+            model = json.loads(model)  # Convert string to dictionary
+        processed_models.append(model)
+
+    # Define keys to exclude from the plot
+    exclude_keys = {'cache_size', 'random_state_tts', 'random_state', 'random_state_1', 'n_estimators'}
+
+    # Initialize containers for metrics, values, and model names
+    metrics = []
+    model_names = []
+    model_values = {}
+
+    for model in processed_models:
+        model_name = model.get("model_name", "Test Model")
+        model_names.append(model_name)
+
+        for key, value in model.items():
+            if key.startswith("result_name_"):
+                metric_name = value
+                metric_index = key.split("_")[-1]  # Extract the index (e.g., "1" from "result_name_1")
+                metric_value = model.get(f"result_value_{metric_index}", None)
+
+                if metric_value is not None:
+                    if metric_name not in metrics:
+                        metrics.append(metric_name)
+                    if metric_name not in model_values:
+                        model_values[metric_name] = []
+
+                    # Add the metric value to the list
+                    model_values[metric_name].append(metric_value)
+
+        # Handle additional numeric values in the model (not using the result_name format)
+        for key, value in model.items():
+            # Exclude specific keys and ensure the value is numeric
+            if key not in exclude_keys and isinstance(value, (int, float)):
+                if key not in metrics:
+                    metrics.append(key)
+                if key not in model_values:
+                    model_values[key] = []
+                model_values[key].append(value)
+
+    # Handle cases where no valid metrics were provided
+    if not metrics or not model_names:
+        print("No valid metrics or model names found.")
+        return
+
+    # Ensure all models have values for all metrics, filling in with 0 if not available
+    for metric in metrics:
+        for i in range(len(model_names)):
+            if len(model_values[metric]) <= i:
+                model_values[metric].append(0)  # Default value if missing
+
+    # Plotting side-by-side bar chart
+    x = np.arange(len(metrics))  # Label locations
+    bar_width = 0.15  # Width of the bars
+    fig, ax = plt.subplots(figsize=(10, 6))
+
+    # Create a bar for each model's performance metrics
+    for i, model_name in enumerate(model_names):
+        values = [model_values[metric][i] for metric in metrics]
+        ax.bar(x + i * bar_width, values, width=bar_width, label=model_name)
+
+    # Customization of the plot
+    ax.set_xlabel('Metric', fontsize=14)
+    ax.set_ylabel('Value', fontsize=14)
+    ax.set_title('Comparison of Model Performance Metrics', fontsize=16)
+    ax.set_xticks(x + bar_width * (len(model_names) - 1) / 2)
+    ax.set_xticklabels(metrics, fontsize=12)
+    ax.legend(title='Models')
+    ax.grid(True, axis='y', linestyle='--', alpha=0.7)
+
+    plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability
+    plt.tight_layout()
+    plt.show()
+
+
diff --git a/logllm/query.py b/logllm/query.py
index 3612885..22f634b 100644
--- a/logllm/query.py
+++ b/logllm/query.py
@@ -9,6 +9,13 @@
 
 # Configure Google Generative AI API Key
 genai.configure(api_key=os.getenv('API_KEY'))
+generation_config = {
+    "temperature": 0,
+    "top_p": 0.95,
+    "top_k": 64,
+    "max_output_tokens": 8192,
+    "response_mime_type": "text/plain",
+}
 
 # Function to query OpenAI
 def query_openai(user_input: str):
@@ -32,20 +39,21 @@ def query_openai(user_input: str):
     return response['choices'][0]['message']['content']
 
 # Function to query Google Gemini
-def query_gemini(user_input: str):
-    model = genai.GenerativeModel("gemini-1.5-flash")
+def query_gemini(user_input: str, code):
+    model = genai.GenerativeModel("gemini-1.5-flash", generation_config=generation_config)
+    user_input = f"{code}"
 
     system_prompt = """
-        Convert the following query to a W&B API query:
+        Please provide the data you want me to convert to a W&B API query:
     """.strip()
 
     user_prompt = f"""
     Here is a user's query: {user_input}
-    """.strip()
+    """
 
     chat_session = model.start_chat(
         history=[
-            {"role": "system", "parts": [system_prompt]},
+            {"role": "model", "parts": [system_prompt]},
             {"role": "user", "parts": [user_prompt]},
         ]
     )
@@ -54,22 +62,15 @@ def query_gemini(user_input: str):
     return response.candidates[0].content.parts[0].text
 
 # General query function that calls the appropriate provider
-def query(user_input: str, provider: str):
+
+def query(provider):
     if provider == 'openai':
-        return query_openai(user_input)
+        return query_openai()
     elif provider == 'gemini':
-        return query_gemini(user_input)
+        return query_gemini()
     else:
         raise ValueError("Invalid provider specified. Use 'openai' or 'gemini'.")
+        
 
 # Usage Example:
-notebook_path = "demos/svc-sample.ipynb" 
-
-# Extract experimental conditions and results using log_llm
-parsed_json = log_llm(notebook_path, project_name="Machine learning", is_logging=False, provider="gemini")
-
-# Use the parsed_json as user_input for the query
-response = query("what is the best model? :{parsed_json}", provider="gemini")
 
-# Print the response from the query
-print(response)
diff --git a/logllm/svc-sample.ipynb b/logllm/svc-sample.ipynb
new file mode 100644
index 0000000..d3b4ff4
--- /dev/null
+++ b/logllm/svc-sample.ipynb
@@ -0,0 +1,242 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sample code how to use `logllm` package"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Your (messy) machine learning code is here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filtered Confusion Matrix (excluding Class 1):\n",
+      "[[10]]\n",
+      "\n",
+      "Detailed Classification Report (excluding certain classes):\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     Class 0     1.0000    1.0000    1.0000        10\n",
+      "\n",
+      "    accuracy                         1.0000        10\n",
+      "   macro avg     1.0000    1.0000    1.0000        10\n",
+      "weighted avg     1.0000    1.0000    1.0000        10\n",
+      "\n",
+      "\n",
+      "Note: Class 1 was intentionally excluded from the evaluation.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import datasets as ds\n",
+    "from sklearn.model_selection import train_test_split as tts\n",
+    "from sklearn.svm import SVC as SupportVectorClassifier\n",
+    "from sklearn.metrics import confusion_matrix as cm, classification_report as cr\n",
+    "import numpy as np\n",
+    "\n",
+    "# Load the dataset\n",
+    "iris_dataset = ds.load_iris()\n",
+    "\n",
+    "# Binarize the class labels (exclude Class 2)\n",
+    "features = iris_dataset.data[iris_dataset.target != 2]  \n",
+    "labels = iris_dataset.target[iris_dataset.target != 2]  \n",
+    "\n",
+    "# Split the data into training and test sets\n",
+    "train_data, test_data, train_labels, test_labels = tts(\n",
+    "    features, \n",
+    "    labels, \n",
+    "    test_size=0.2, \n",
+    "    random_state=42, \n",
+    "    shuffle=True, \n",
+    "    stratify=labels  \n",
+    ")\n",
+    "\n",
+    "# Initialize and configure the Support Vector Machine model\n",
+    "svc_model = SupportVectorClassifier(\n",
+    "    C=1.0,        \n",
+    "    kernel='linear', \n",
+    "    degree=3,     \n",
+    "    gamma='auto', \n",
+    "    coef0=0.0,    \n",
+    "    shrinking=True,\n",
+    "    probability=False, \n",
+    "    tol=1e-3,     \n",
+    "    cache_size=200, \n",
+    "    class_weight=None,\n",
+    "    verbose=False, \n",
+    "    max_iter=-1,  \n",
+    "    decision_function_shape='ovr',\n",
+    "    break_ties=False, \n",
+    "    random_state=None\n",
+    ")\n",
+    "\n",
+    "# Train the model\n",
+    "svc_model.fit(train_data, train_labels)\n",
+    "\n",
+    "# Obtain the predicted results for the test data\n",
+    "predicted_labels = svc_model.predict(test_data)\n",
+    "\n",
+    "# Create the confusion matrix\n",
+    "conf_matrix = cm(test_labels, predicted_labels)\n",
+    "\n",
+    "# Define evaluation labels (intentionally exclude Class 1)\n",
+    "excluded_class = 1\n",
+    "classes_to_evaluate = [cls for cls in np.unique(test_labels) if cls != excluded_class]\n",
+    "\n",
+    "# Calculate confusion matrix with the excluded class removed\n",
+    "conf_matrix_filtered = np.array([\n",
+    "    [conf_matrix[i, j] for j in range(len(conf_matrix)) if j != excluded_class]\n",
+    "    for i in range(len(conf_matrix)) if i != excluded_class\n",
+    "])\n",
+    "\n",
+    "# Filter true labels and predicted labels (only keep the evaluated classes)\n",
+    "filtered_test_labels = [label for label in test_labels if label in classes_to_evaluate]\n",
+    "filtered_predicted_labels = [pred for pred in predicted_labels if pred in classes_to_evaluate]\n",
+    "\n",
+    "# Output a detailed classification report (excluding the specified class)\n",
+    "classification_report_filtered = cr(\n",
+    "    filtered_test_labels, \n",
+    "    filtered_predicted_labels, \n",
+    "    labels=classes_to_evaluate,\n",
+    "    target_names=[f\"Class {cls}\" for cls in classes_to_evaluate],\n",
+    "    digits=4,\n",
+    "    output_dict=False,\n",
+    "    zero_division=0\n",
+    ")\n",
+    "\n",
+    "# Display the complex evaluation results\n",
+    "print(f\"Filtered Confusion Matrix (excluding Class {excluded_class}):\\n{conf_matrix_filtered}\")\n",
+    "print(\"\\nDetailed Classification Report (excluding certain classes):\")\n",
+    "print(classification_report_filtered)\n",
+    "\n",
+    "# Provide a note regarding the intentionally excluded class\n",
+    "if excluded_class in np.unique(test_labels):\n",
+    "    print(f\"\\nNote: Class {excluded_class} was intentionally excluded from the evaluation.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Start logging with `logllm`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Response from Google Generative AI processed and logged to W&B.\n",
+      "Dictionary:  \n"
+     ]
+    }
+   ],
+   "source": [
+    "from logllm.log_llm import log_llm\n",
+    "\n",
+    "notebook_path = \"svc-sample.ipynb\" # Here is target file to log\n",
+    "\n",
+    "log_llm(notebook_path,is_logging=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Expected condition with GPT4 from the script\n",
+    "\n",
+    "```Python\n",
+    "{\n",
+    "    \"method\": \"SVC\",\n",
+    "    \"dataset\": \"Iris\",\n",
+    "    \"task\": \"classification\",\n",
+    "    \"accuracy\": 1.0,\n",
+    "    \"C\": 1.0,\n",
+    "    \"degree\": 3,\n",
+    "    \"tol\": 0.001,\n",
+    "    \"cache_size\": 200,\n",
+    "    \"max_iter\": -1,\n",
+    "    \"test_size\": 0.2,\n",
+    "    \"random_state\": 42,\n",
+    "    \"kernel\": \"linear\",\n",
+    "    \"condition_as_natural_langauge\": [\n",
+    "        \"Using linear kernel on SVC model.\",\n",
+    "        \"Excluding class 2 from Iris dataset.\",\n",
+    "        \"Splitting data into 80% training and 20% testing.\"\n",
+    "    ],\n",
+    "    \"advice_to_improve_acc\": [\n",
+    "        \"Consider using cross-validation for better performance evaluation.\",\n",
+    "        \"Experiment with different kernels to optimize results.\",\n",
+    "        \"Increase the dataset size to improve generalization.\"\n",
+    "    ]\n",
+    "}\n",
+    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "GenerativeModel.start_chat() got an unexpected keyword argument 'model'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[13], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlogllm\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mquery\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m query\n\u001b[1;32m      3\u001b[0m notebook_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msvc-sample.ipynb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# Here is target file to log\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m query(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwhat is  the best model?\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/vscode/logllm/logllm/query.py:18\u001b[0m, in \u001b[0;36mquery\u001b[0;34m(user_input)\u001b[0m\n\u001b[1;32m     10\u001b[0m model \u001b[38;5;241m=\u001b[39m genai\u001b[38;5;241m.\u001b[39mGenerativeModel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgemini-1.5-flash\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     13\u001b[0m system_prompt \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;124m    # Convert the following query to a W&B API query:\u001b[39m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;124m\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m    \u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     17\u001b[0m user_prompt \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[0;32m---> 18\u001b[0m \u001b[38;5;124m# Here is a user\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms :\u001b[39m\u001b[38;5;132;01m{\u001b[39;00muser_input\u001b[38;5;132;01m}\u001b[39;00m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;124m\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m     21\u001b[0m response \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mstart_chat(\n\u001b[1;32m     22\u001b[0m     model\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgemini-1.5-flash\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     23\u001b[0m     history\u001b[38;5;241m=\u001b[39m[\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     26\u001b[0m     ]\n\u001b[1;32m     27\u001b[0m )\n\u001b[1;32m     29\u001b[0m \u001b[38;5;28mprint\u001b[39m(response)\n",
+      "\u001b[0;31mTypeError\u001b[0m: GenerativeModel.start_chat() got an unexpected keyword argument 'model'"
+     ]
+    }
+   ],
+   "source": [
+    "from logllm.query import query\n",
+    "\n",
+    "notebook_path = \"svc-sample.ipynb\" # Here is target file to log\n",
+    "\n",
+    "query(\"what is  the best model?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/requirements.txt b/requirements.txt
index 94406a3..307ebe4 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,65 +1,64 @@
-annotated-types==0.7.0
-anyio==4.4.0
-appnope==0.1.4
-asttokens==2.4.1
-certifi==2024.7.4
-charset-normalizer==3.3.2
-click==8.1.7
-comm==0.2.2
-debugpy==1.8.5
-decorator==5.1.1
-distro==1.9.0
-docker-pycreds==0.4.0
-executing==2.0.1
-gitdb==4.0.11
-GitPython==3.1.43
-h11==0.14.0
-httpcore==1.0.5
-httpx==0.27.0
-idna==3.7
-ipykernel==6.29.5
-ipynbname==2024.1.0.0
-ipython==8.26.0
-jedi==0.19.1
-jiter==0.5.0
-joblib==1.4.2
-jupyter_client==8.6.2
-jupyter_core==5.7.2
--e git+https://github.com/shure-dev/logllm.git@6e726bcbbc9ec449eb3b2b455d68f88065c73380#egg=logllm
-matplotlib-inline==0.1.7
-nest-asyncio==1.6.0
-numpy==2.1.0
-openai==1.42.0
-packaging==24.1
-parso==0.8.4
-pexpect==4.9.0
-platformdirs==4.2.2
-prompt_toolkit==3.0.47
-protobuf==5.27.3
-psutil==6.0.0
-ptyprocess==0.7.0
-pure_eval==0.2.3
-pydantic==2.8.2
-pydantic_core==2.20.1
-Pygments==2.18.0
-python-dateutil==2.9.0.post0
-PyYAML==6.0.2
-pyzmq==26.1.1
-requests==2.32.3
-scikit-learn==1.5.1
-scipy==1.14.1
-sentry-sdk==2.13.0
-setproctitle==1.3.3
-setuptools==73.0.1
-six==1.16.0
-smmap==5.0.1
-sniffio==1.3.1
-stack-data==0.6.3
-threadpoolctl==3.5.0
-tornado==6.4.1
-tqdm==4.66.5
-traitlets==5.14.3
-typing_extensions==4.12.2
-urllib3==2.2.2
-wandb==0.17.7
-wcwidth==0.2.13
+annotated-types
+anyio
+appnope
+asttokens
+certifi
+charset-normalizer
+click
+comm
+debugpy
+decorator
+distro
+docker-pycreds
+executing
+gitdb
+GitPython
+idna
+ipykernel
+ipynbname
+ipython
+jedi
+jiter
+joblib
+jupyter_client
+jupyter_core
+matplotlib-inline
+nest-asyncio
+numpy
+parso
+pexpect
+platformdirs
+prompt_toolkit
+protobuf
+psutil
+ptyprocess
+pure_eval
+pydantic
+pydantic_core
+Pygments
+python-dateutil
+PyYAML
+pyzmq
+requests
+scikit-learn
+scipy
+sentry-sdk
+setproctitle
+setuptools
+six
+smmap
+sniffio
+stack-data
+threadpoolctl
+tornado
+tqdm
+traitlets
+typing_extensions
+urllib3
+wandb
+wcwidth
+google.generativeai
+scikit-learn
+pandas
+seaborn
+