From ae48e616ec6ed2a33cf4aa9cc5f7119fb1c6421d Mon Sep 17 00:00:00 2001
From: cybercoder <barqawi.88@outlook.com>
Date: Tue, 27 Feb 2024 07:00:26 -0500
Subject: [PATCH 01/10] Create kagent.py

add keras agent skeleton
---
 intelli/flow/agents/kagent.py | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)
 create mode 100644 intelli/flow/agents/kagent.py

diff --git a/intelli/flow/agents/kagent.py b/intelli/flow/agents/kagent.py
new file mode 100644
index 0000000..005fe7d
--- /dev/null
+++ b/intelli/flow/agents/kagent.py
@@ -0,0 +1,19 @@
+from intelli.flow.agents.agent import BasicAgent
+from intelli.flow.input.agent_input import AgentInput, TextAgentInput, ImageAgentInput
+
+class KerasAgent(BasicAgent):
+    def __init__(self, agent_type, provider, mission, model_params, options=None):
+        super().__init__(agent_type, provider, mission, model_params, options)
+        try:
+            import keras_nlp
+            self.keras_nlp = keras_nlp 
+            self.KERAS_AVAILABLE = True
+        except ImportError:
+            self.KERAS_AVAILABLE = False
+    
+    def execute(self, agent_input: AgentInput):
+        if self.KERAS_AVAILABLE:
+            # todo add the execute logic
+            pass
+        else:
+            raise Exception("keras_nlp is not available. This function cannot proceed.")
\ No newline at end of file

From 11f3289bf85fade5d189f878c7e25de48dd996c2 Mon Sep 17 00:00:00 2001
From: cybercoder <barqawi.88@outlook.com>
Date: Tue, 27 Feb 2024 07:36:33 -0500
Subject: [PATCH 02/10] Update setup.py

add optional requirements
---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index b260cfb..b3768cb 100644
--- a/setup.py
+++ b/setup.py
@@ -21,6 +21,6 @@
         "python-dotenv==1.0.1", "networkx==3.2.1",
     ],
     extras_require={
-        'visual': ["matplotlib==3.6.0"],
+        'visual': ["matplotlib==3.6.0", "keras>=3", "keras-nlp"],
     }
 )
\ No newline at end of file

From 1091336e2e1a9d6bce9d14f5b319133c9b4b0277 Mon Sep 17 00:00:00 2001
From: cybercoder <barqawi.88@outlook.com>
Date: Tue, 27 Feb 2024 08:12:56 -0500
Subject: [PATCH 03/10] Add keras agent full logic

---
 instructions/run_integration_text.sh         |  4 +-
 intelli/flow/agents/kagent.py                | 61 +++++++++++++++++---
 intelli/test/integration/test_keras_agent.py | 44 ++++++++++++++
 setup.py                                     |  2 +-
 4 files changed, 100 insertions(+), 11 deletions(-)
 create mode 100644 intelli/test/integration/test_keras_agent.py

diff --git a/instructions/run_integration_text.sh b/instructions/run_integration_text.sh
index c5cf762..718bade 100644
--- a/instructions/run_integration_text.sh
+++ b/instructions/run_integration_text.sh
@@ -44,4 +44,6 @@ python3 -m unittest intelli.test.integration.test_chatbot_with_data
 # basic flow
 python3 -m unittest intelli.test.integration.test_flow_sequence
 # map flow
-python3 -m unittest intelli.test.integration.test_flow_map
\ No newline at end of file
+python3 -m unittest intelli.test.integration.test_flow_map
+# keras nlp
+python3 -m unittest intelli.test.integration.test_keras_agent
\ No newline at end of file
diff --git a/intelli/flow/agents/kagent.py b/intelli/flow/agents/kagent.py
index 005fe7d..a79a1cc 100644
--- a/intelli/flow/agents/kagent.py
+++ b/intelli/flow/agents/kagent.py
@@ -1,19 +1,62 @@
 from intelli.flow.agents.agent import BasicAgent
 from intelli.flow.input.agent_input import AgentInput, TextAgentInput, ImageAgentInput
+import os  
 
 class KerasAgent(BasicAgent):
-    def __init__(self, agent_type, provider, mission, model_params, options=None):
-        super().__init__(agent_type, provider, mission, model_params, options)
+    def __init__(self, agent_type, provider="", mission="", model_params={}, options=None):
+        super().__init__()
+        
+        # set the parameters
+        self.agent_type = agent_type
+        self.provider = provider
+        self.mission = mission
+        self.model_params = model_params
+        self.options = options if options is not None else {}
+        
+        self.model = self.load_model()
+    
+    def load_model(self):
+        """
+        Dynamically load a model based on `model_params`.
+        This example demonstrates loading Gemma models, but you should add similar logic for other models.
+        """
         try:
+            # import keras
             import keras_nlp
             self.keras_nlp = keras_nlp 
-            self.KERAS_AVAILABLE = True
-        except ImportError:
-            self.KERAS_AVAILABLE = False
+            model_param = self.model_params['model']
+            
+            if "gemma" in model_param:
+                print('start gemma model')
+                from keras_nlp.models import GemmaCausalLM
+                
+                # set the username and password
+                if "KAGGLE_USERNAME" in self.model_params:
+                    os.environ["KAGGLE_USERNAME"] = self.model_params["KAGGLE_USERNAME"]
+                    os.environ["KAGGLE_KEY"] = self.model_params["KAGGLE_KEY"]
+                
+                return GemmaCausalLM.from_preset(model_param)
+            # ------------------------------------------------------------------ #
+            # Add similar conditions for models like Mistral, RoBERTa, or BERT   #
+            # ------------------------------------------------------------------ #
+            else:
+                raise Exception("The received model not supported in this version.")
+            
+        except ImportError as e:
+            raise ImportError("keras_nlp is not installed or model is not supported.") from e
     
     def execute(self, agent_input: AgentInput):
-        if self.KERAS_AVAILABLE:
-            # todo add the execute logic
-            pass
+        """
+        Execute the agent task based on input.
+        """
+        
+        if not isinstance(agent_input, TextAgentInput):
+            raise ValueError("This agent requires a TextAgentInput.")
+                
+        max_length = self.model_params.get("max_length", 64)
+        model_input = agent_input.desc if not self.mission else self.mission + ": " + agent_input.desc
+        
+        if hasattr(self.model, 'generate'):     
+            return self.model.generate(model_input, max_length=max_length)
         else:
-            raise Exception("keras_nlp is not available. This function cannot proceed.")
\ No newline at end of file
+            raise NotImplementedError("Model does not support text generation.")
diff --git a/intelli/test/integration/test_keras_agent.py b/intelli/test/integration/test_keras_agent.py
new file mode 100644
index 0000000..7f8364b
--- /dev/null
+++ b/intelli/test/integration/test_keras_agent.py
@@ -0,0 +1,44 @@
+import os
+import base64
+import unittest
+from intelli.flow.types import *
+from intelli.flow.agents.agent import Agent
+from intelli.flow.agents.kagent import KerasAgent
+from intelli.flow.input.task_input import TextTaskInput, ImageTaskInput
+from intelli.flow.processors.basic_processor import TextProcessor
+from intelli.flow.sequence_flow import SequenceFlow
+from intelli.flow.tasks.task import Task
+from dotenv import load_dotenv
+
+load_dotenv()
+
+class TestKerasFlows(unittest.TestCase):
+    def setUp(self):
+        self.kaggle_username = os.getenv("KAGGLE_USERNAME")
+        self.kaggle_pass = os.getenv("GEMINI_API_KEY")
+    
+    def test_blog_post_flow(self):
+        print("---- start simple blog post flow ----")
+        
+        # Define agents
+        gemma_model_params = {
+            "model": "gemma_2b_en",
+            "max_length": 64
+        }
+        gemma_agent = KerasAgent(agent_type="text", 
+                                 mission="write blog posts",
+                                 model_params=gemma_model_params)
+        # Define tasks
+        task1 = Task(
+            TextTaskInput("blog post about electric cars"), gemma_agent, log=True
+        )
+
+        # Start SequenceFlow
+        flow = SequenceFlow([task1], log=True)
+        final_result = flow.start()
+
+        print("Final result:", final_result)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/setup.py b/setup.py
index b3768cb..51dbcca 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setup(
     name="intelli",
-    version="0.1.6",
+    version="0.1.7",
     author="Intellinode",
     author_email="admin@intellinode.ai",
     description="Create chatbots and AI agent workflows. Intelli provide unified layer to connect your data with multiple AI models like OpenAI, Gemini, and Mistral.",

From 1a2a61ba5080105d624270457ce44e38217ea1ea Mon Sep 17 00:00:00 2001
From: cybercoder <barqawi.88@outlook.com>
Date: Tue, 27 Feb 2024 08:17:28 -0500
Subject: [PATCH 04/10] Update setup.py

---
 setup.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 51dbcca..e4de5bf 100644
--- a/setup.py
+++ b/setup.py
@@ -21,6 +21,7 @@
         "python-dotenv==1.0.1", "networkx==3.2.1",
     ],
     extras_require={
-        'visual': ["matplotlib==3.6.0", "keras>=3", "keras-nlp"],
+        "visual": ["matplotlib==3.6.0"],
+        "offline": ["keras>=3", "keras-nlp"],
     }
 )
\ No newline at end of file

From e5ef3bfbcd63bc643e7312d8487f47ee9f8d72fd Mon Sep 17 00:00:00 2001
From: cybercoder <barqawi.88@outlook.com>
Date: Fri, 1 Mar 2024 07:33:34 -0500
Subject: [PATCH 05/10] Update keras agent

- fix a bug in the agent type.
- add mistral support.
---
 intelli/flow/agents/kagent.py                | 28 +++++++++++++-------
 intelli/test/integration/test_keras_agent.py |  1 +
 setup.py                                     |  4 +--
 3 files changed, 21 insertions(+), 12 deletions(-)

diff --git a/intelli/flow/agents/kagent.py b/intelli/flow/agents/kagent.py
index a79a1cc..c8749eb 100644
--- a/intelli/flow/agents/kagent.py
+++ b/intelli/flow/agents/kagent.py
@@ -7,7 +7,7 @@ def __init__(self, agent_type, provider="", mission="", model_params={}, options
         super().__init__()
         
         # set the parameters
-        self.agent_type = agent_type
+        self.type = agent_type
         self.provider = provider
         self.mission = mission
         self.model_params = model_params
@@ -15,6 +15,9 @@ def __init__(self, agent_type, provider="", mission="", model_params={}, options
         
         self.model = self.load_model()
     
+    def update_model_params(self, model_params):
+        self.model_params = model_params
+        
     def load_model(self):
         """
         Dynamically load a model based on `model_params`.
@@ -23,19 +26,24 @@ def load_model(self):
         try:
             # import keras
             import keras_nlp
-            self.keras_nlp = keras_nlp 
-            model_param = self.model_params['model']
+            os.environ["KERAS_BACKEND"] = "jax"
+            model_param = self.model_params["model"]
+            
+            # set the username and password
+            if "KAGGLE_USERNAME" in self.model_params:
+                os.environ["KAGGLE_USERNAME"] = self.model_params["KAGGLE_USERNAME"]
+                os.environ["KAGGLE_KEY"] = self.model_params["KAGGLE_KEY"]
             
             if "gemma" in model_param:
-                print('start gemma model')
+                print("start gemma model")
                 from keras_nlp.models import GemmaCausalLM
                 
-                # set the username and password
-                if "KAGGLE_USERNAME" in self.model_params:
-                    os.environ["KAGGLE_USERNAME"] = self.model_params["KAGGLE_USERNAME"]
-                    os.environ["KAGGLE_KEY"] = self.model_params["KAGGLE_KEY"]
-                
                 return GemmaCausalLM.from_preset(model_param)
+            elif "mistral" in model_param:
+                print("start mistral model")
+                from keras_nlp.models import MistralCausalLM
+                
+                return MistralCausalLM.from_preset(model_param)
             # ------------------------------------------------------------------ #
             # Add similar conditions for models like Mistral, RoBERTa, or BERT   #
             # ------------------------------------------------------------------ #
@@ -56,7 +64,7 @@ def execute(self, agent_input: AgentInput):
         max_length = self.model_params.get("max_length", 64)
         model_input = agent_input.desc if not self.mission else self.mission + ": " + agent_input.desc
         
-        if hasattr(self.model, 'generate'):     
+        if hasattr(self.model, "generate"):     
             return self.model.generate(model_input, max_length=max_length)
         else:
             raise NotImplementedError("Model does not support text generation.")
diff --git a/intelli/test/integration/test_keras_agent.py b/intelli/test/integration/test_keras_agent.py
index 7f8364b..0d24151 100644
--- a/intelli/test/integration/test_keras_agent.py
+++ b/intelli/test/integration/test_keras_agent.py
@@ -28,6 +28,7 @@ def test_blog_post_flow(self):
         gemma_agent = KerasAgent(agent_type="text", 
                                  mission="write blog posts",
                                  model_params=gemma_model_params)
+        
         # Define tasks
         task1 = Task(
             TextTaskInput("blog post about electric cars"), gemma_agent, log=True
diff --git a/setup.py b/setup.py
index e4de5bf..114ec16 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setup(
     name="intelli",
-    version="0.1.7",
+    version="0.1.10",
     author="Intellinode",
     author_email="admin@intellinode.ai",
     description="Create chatbots and AI agent workflows. Intelli provide unified layer to connect your data with multiple AI models like OpenAI, Gemini, and Mistral.",
@@ -22,6 +22,6 @@
     ],
     extras_require={
         "visual": ["matplotlib==3.6.0"],
-        "offline": ["keras>=3", "keras-nlp"],
+        "offline": ["keras-nlp", "keras>=3"],
     }
 )
\ No newline at end of file

From 827b0d43f728881508c66cd00764bc547f1d14f4 Mon Sep 17 00:00:00 2001
From: cybercoder <barqawi.88@outlook.com>
Date: Fri, 1 Mar 2024 20:00:27 -0500
Subject: [PATCH 06/10] Update kagent.py

- add fine tuning.
- adjust the initial.
- add a function to add externally fined tuned model.
---
 intelli/flow/agents/kagent.py | 131 +++++++++++++++++++++++++---------
 1 file changed, 98 insertions(+), 33 deletions(-)

diff --git a/intelli/flow/agents/kagent.py b/intelli/flow/agents/kagent.py
index c8749eb..09c1fee 100644
--- a/intelli/flow/agents/kagent.py
+++ b/intelli/flow/agents/kagent.py
@@ -3,7 +3,7 @@
 import os  
 
 class KerasAgent(BasicAgent):
-    def __init__(self, agent_type, provider="", mission="", model_params={}, options=None):
+    def __init__(self, agent_type, provider="", mission="", model_params={}, options=None, log=False, external=False):
         super().__init__()
         
         # set the parameters
@@ -12,10 +12,30 @@ def __init__(self, agent_type, provider="", mission="", model_params={}, options
         self.mission = mission
         self.model_params = model_params
         self.options = options if options is not None else {}
+        self.log = log
+        self.external = external
+        try:
+            import keras_nlp
+            import keras
+            self.nlp_manager = keras_nlp
+            self.keras_manager = keras
+            if not external:
+                self.model = self.load_model()
+        except ImportError as e:
+            raise ImportError("keras_nlp is not installed or model is not supported.") from e
+    
+    def set_keras_model(self, model, model_params):
+        if not self.external:
+            raise Exception("Initiate the agent with external flag to set the model.")
         
-        self.model = self.load_model()
+        if not hasattr(model, "generate"):
+            raise ValueError("The provided model does not have a 'generate' method, which is required for this agent.")
+        
+        self.model = model
+        self.model_params = model_params
     
     def update_model_params(self, model_params):
+        
         self.model_params = model_params
         
     def load_model(self):
@@ -23,35 +43,27 @@ def load_model(self):
         Dynamically load a model based on `model_params`.
         This example demonstrates loading Gemma models, but you should add similar logic for other models.
         """
-        try:
-            # import keras
-            import keras_nlp
-            os.environ["KERAS_BACKEND"] = "jax"
-            model_param = self.model_params["model"]
-            
-            # set the username and password
-            if "KAGGLE_USERNAME" in self.model_params:
-                os.environ["KAGGLE_USERNAME"] = self.model_params["KAGGLE_USERNAME"]
-                os.environ["KAGGLE_KEY"] = self.model_params["KAGGLE_KEY"]
-            
-            if "gemma" in model_param:
-                print("start gemma model")
-                from keras_nlp.models import GemmaCausalLM
-                
-                return GemmaCausalLM.from_preset(model_param)
-            elif "mistral" in model_param:
-                print("start mistral model")
-                from keras_nlp.models import MistralCausalLM
-                
-                return MistralCausalLM.from_preset(model_param)
-            # ------------------------------------------------------------------ #
-            # Add similar conditions for models like Mistral, RoBERTa, or BERT   #
-            # ------------------------------------------------------------------ #
-            else:
-                raise Exception("The received model not supported in this version.")
-            
-        except ImportError as e:
-            raise ImportError("keras_nlp is not installed or model is not supported.") from e
+
+        self.keras_manager = keras
+        os.environ["KERAS_BACKEND"] = "jax"
+        model_param = self.model_params["model"]
+        
+        # set the username and password
+        if "KAGGLE_USERNAME" in self.model_params:
+            os.environ["KAGGLE_USERNAME"] = self.model_params["KAGGLE_USERNAME"]
+            os.environ["KAGGLE_KEY"] = self.model_params["KAGGLE_KEY"]
+        
+        if "gemma" in model_param:
+            print("start gemma model")
+            return self.nlp_manager.models.GemmaCausalLM.from_preset(model_param)
+        elif "mistral" in model_param:
+            print("start mistral model")
+            return self.nlp_manager.models.MistralCausalLM.from_preset(model_param)
+        # ------------------------------------------------------------------ #
+        # Add similar conditions for models like Mistral, RoBERTa, or BERT   #
+        # ------------------------------------------------------------------ #
+        else:
+            raise Exception("The received model not supported in this version.")
     
     def execute(self, agent_input: AgentInput):
         """
@@ -63,8 +75,61 @@ def execute(self, agent_input: AgentInput):
                 
         max_length = self.model_params.get("max_length", 64)
         model_input = agent_input.desc if not self.mission else self.mission + ": " + agent_input.desc
-        
+            
         if hasattr(self.model, "generate"):     
-            return self.model.generate(model_input, max_length=max_length)
+            if self.log:
+                print("Call the model generate with input: ", model_input)
+            
+            generated_output = self.model.generate(model_input, max_length=max_length)
+            
+            if isinstance(generated_output, str) and generated_output.startswith(model_input):
+                generated_output = generated_output.replace(model_input, "", 1).strip()
+            
+            return generated_output
         else:
             raise NotImplementedError("Model does not support text generation.")
+
+    def fine_tune_model_with_lora(self, fine_tuning_config):
+        """
+        Finetunes the model as per the provided config.
+        """
+        print("Fine tuning model...")
+        
+        # rank=4 replaces the weights matrix of relevant layers with the product AxB of two matrices of rank 4,
+        # which reduces the number of trainable parameters.
+        lora_rank = fine_tuning_config.get("lora_rank", 4)
+        
+        # Enable lora for the model learning
+        self.model.backbone.enable_lora(rank=lora_rank)
+        
+        # Set the preprocessor sequence_length
+        self.model.preprocessor.sequence_length = fine_tuning_config.get("sequence_length", 512)
+        
+        # Use AdamW optimizer.
+        learning_rate = fine_tuning_config.get("learning_rate", 0.001)
+        weight_decay = fine_tuning_config.get("weight_decay", 0.004)
+        beta_1 = fine_tuning_config.get("beta_1", 0.9)
+        beta_2 = fine_tuning_config.get("beta_2", 0.999)
+        
+        optimizer = self.keras_manager.optimizers.AdamW(
+            learning_rate=learning_rate,
+            weight_decay=weight_decay,
+            beta_1=beta_1,
+            beta_2=beta_2
+        )
+        
+        # Exclude layernorm and bias terms from decay.
+        optimizer.exclude_from_weight_decay(var_names=["bias", "scale"])
+        
+        # Compile the model
+        self.model.compile(
+            loss=self.keras_manager.losses.SparseCategoricalCrossentropy(from_logits=True),
+            optimizer=optimizer,
+            weighted_metrics=[self.keras_manager.metrics.SparseCategoricalAccuracy()],
+        )
+        
+        # Fit using input dataset, epochs and batch size
+        dataset = fine_tuning_config.get('dataset')
+        epochs = fine_tuning_config.get('epochs', 3)
+        batch_size = fine_tuning_config.get('batch_size', 1)
+        self.model.fit(dataset, epochs=epochs, batch_size=batch_size)

From 0710d14055f5c27463fa50456d18fb8875b47cba Mon Sep 17 00:00:00 2001
From: cybercoder <barqawi.88@outlook.com>
Date: Fri, 1 Mar 2024 20:03:00 -0500
Subject: [PATCH 07/10] Update setup.py

update the version.
---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 114ec16..0966d7e 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setup(
     name="intelli",
-    version="0.1.10",
+    version="0.1.11",
     author="Intellinode",
     author_email="admin@intellinode.ai",
     description="Create chatbots and AI agent workflows. Intelli provide unified layer to connect your data with multiple AI models like OpenAI, Gemini, and Mistral.",

From a68166ccde297999c2d4967ae233a6215682138d Mon Sep 17 00:00:00 2001
From: cybercoder <barqawi.88@outlook.com>
Date: Fri, 1 Mar 2024 20:38:04 -0500
Subject: [PATCH 08/10] update the version

---
 intelli/flow/agents/kagent.py | 4 +---
 setup.py                      | 2 +-
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/intelli/flow/agents/kagent.py b/intelli/flow/agents/kagent.py
index 09c1fee..6a3c072 100644
--- a/intelli/flow/agents/kagent.py
+++ b/intelli/flow/agents/kagent.py
@@ -43,9 +43,7 @@ def load_model(self):
         Dynamically load a model based on `model_params`.
         This example demonstrates loading Gemma models, but you should add similar logic for other models.
         """
-
-        self.keras_manager = keras
-        os.environ["KERAS_BACKEND"] = "jax"
+        
         model_param = self.model_params["model"]
         
         # set the username and password
diff --git a/setup.py b/setup.py
index 0966d7e..b3adef9 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setup(
     name="intelli",
-    version="0.1.11",
+    version="0.2.0",
     author="Intellinode",
     author_email="admin@intellinode.ai",
     description="Create chatbots and AI agent workflows. Intelli provide unified layer to connect your data with multiple AI models like OpenAI, Gemini, and Mistral.",

From d7cfa7a529ef39a8e3439100b18485a0f8013558 Mon Sep 17 00:00:00 2001
From: cybercoder <barqawi.88@outlook.com>
Date: Fri, 1 Mar 2024 20:44:23 -0500
Subject: [PATCH 09/10] Update test_keras_agent.py

---
 intelli/test/integration/test_keras_agent.py | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/intelli/test/integration/test_keras_agent.py b/intelli/test/integration/test_keras_agent.py
index 0d24151..15021b3 100644
--- a/intelli/test/integration/test_keras_agent.py
+++ b/intelli/test/integration/test_keras_agent.py
@@ -1,29 +1,31 @@
 import os
-import base64
 import unittest
 from intelli.flow.types import *
 from intelli.flow.agents.agent import Agent
 from intelli.flow.agents.kagent import KerasAgent
-from intelli.flow.input.task_input import TextTaskInput, ImageTaskInput
-from intelli.flow.processors.basic_processor import TextProcessor
+from intelli.flow.input.task_input import TextTaskInput
 from intelli.flow.sequence_flow import SequenceFlow
 from intelli.flow.tasks.task import Task
 from dotenv import load_dotenv
-
+# set keras back
+os.environ["KERAS_BACKEND"] = "jax"
+# load env
 load_dotenv()
 
 class TestKerasFlows(unittest.TestCase):
     def setUp(self):
         self.kaggle_username = os.getenv("KAGGLE_USERNAME")
-        self.kaggle_pass = os.getenv("GEMINI_API_KEY")
+        self.kaggle_pass = os.getenv("KAGGLE_API_KEY")
     
     def test_blog_post_flow(self):
         print("---- start simple blog post flow ----")
         
         # Define agents
         gemma_model_params = {
-            "model": "gemma_2b_en",
-            "max_length": 64
+            "model": "gemma_instruct_2b_en",
+            "max_length": 64,
+            "KAGGLE_USERNAME": self.kaggle_username,
+            "KAGGLE_KEY": self.kaggle_pass,
         }
         gemma_agent = KerasAgent(agent_type="text", 
                                  mission="write blog posts",
@@ -39,6 +41,7 @@ def test_blog_post_flow(self):
         final_result = flow.start()
 
         print("Final result:", final_result)
+        self.assertIsNotNone(final_result)
 
 
 if __name__ == "__main__":

From a1caeb15c3c256ef432cea7fd2724834c7b5da7f Mon Sep 17 00:00:00 2001
From: cybercoder <barqawi.88@outlook.com>
Date: Fri, 1 Mar 2024 20:48:03 -0500
Subject: [PATCH 10/10] update Keras instructions

---
 README.md            | 5 ++++-
 intelli/.example.env | 2 ++
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 449b62d..462a67c 100644
--- a/README.md
+++ b/README.md
@@ -123,7 +123,10 @@ wrapper = RemoteImageModel(your_api_key, provider)
 results = wrapper.generate_images(image_input)
 ```
 
-# The Repository Setup
+## Keras Agent
+Load gemma or mistral models offline using keras agent, [check the docs](https://docs.intellinode.ai/docs/python/flows/kagent).
+
+# Repository Setup
 1. Install the requirements.
 ```shell
 pip install -r requirements.txt
diff --git a/intelli/.example.env b/intelli/.example.env
index 6420c26..b290449 100644
--- a/intelli/.example.env
+++ b/intelli/.example.env
@@ -7,3 +7,5 @@ REPLICATE_API_KEY=
 MISTRAL_API_KEY=
 INTELLI_ONE_KEY=
 GEMINI_API_KEY=
+KAGGLE_USERNAME=
+KAGGLE_API_KEY=
\ No newline at end of file