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README.md

@@ -1,12 +1,12 @@
----
-title: Anath
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 5.0.1
-app_file: app.py
-pinned: false
----
-
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+---
+title: ClearMLIISC
+emoji: 🐢
+colorFrom: green
+colorTo: purple
+sdk: gradio
+sdk_version: 5.7.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,124 +1,142 @@
-import gradio as gr
-from langchain_community.chat_models import ChatOpenAI
-from langchain.memory import ConversationBufferMemory, SimpleMemory
-from langchain.agents import initialize_agent, AgentType
-from langchain_community.callbacks import ClearMLCallbackHandler
-from langchain_core.callbacks import StdOutCallbackHandler
-from clearml import Logger, Task
-from dotenv import load_dotenv
-import os
-import agent.planning_agent as planning_agent
-import logging
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Global variables
-llm = None
-chat_memory = None
-query_memory = None
-clearml_callback = None
-
-def initialize_components():
-    global llm, chat_memory, query_memory,clearml_callback
-    load_dotenv()
-    #api_key = os.environ['OA_API']           
-    #os.environ['OPENAI_API_KEY'] = api_key
-    
-    OPENAI_API_KEY='sk-ah4Ar22m-1gdxwT5lg0KAodXVK5wt19cOzMBCV1mZ0T3BlbkFJzIhW66UefL-sl55mh4W-bcP1jUymqLHFuFEWC5bl8A'
-    CLEARML_API_ACCESS_KEY="MXUXWWPITREXDVYSU3PLC2D7PBJPH0"
-    CLEARML_API_SECRET_KEY="TsAQnRl5bvA9k-zPvqwcMRrULkRJUR_IoBuFmj_uzhMAzNeADs5AtHVhIlks7CHcNco"
-    SERPAPI_API_KEY="619f2302253fbe56448bcf82565caf2a3263d845944682533f10b09a0d1650e6"
-    
-    llm = ChatOpenAI(
-        model_name="gpt-3.5-turbo",
-        temperature=0,
-        api_key='sk-ah4Ar22m-1gdxwT5lg0KAodXVK5wt19cOzMBCV1mZ0T3BlbkFJzIhW66UefL-sl55mh4W-bcP1jUymqLHFuFEWC5bl8A'
-    )
-
-    # Initialize memories
-    chat_memory = ConversationBufferMemory(
-        memory_key="chat_history",
-        return_messages=True
-    )
-    # Setup and use the ClearML Callback
-    clearml_callback = ClearMLCallbackHandler(
-    task_type="inference",
-    project_name="langchain_callback_demo",
-    task_name="llm",
-    tags=["test"],
-    # Change the following parameters based on the amount of detail you want tracked
-    visualize=True,
-    complexity_metrics=True,
-    stream_logs=True,)
-    
-    callbacks = [StdOutCallbackHandler(), clearml_callback]
-
-    # Initialize planning agent with both memories
-    planning_agent.initialize_planning_agent(llm, chat_memory, query_memory, callbacks)
-
-    logger.info("Components initialized successfully")
-
-def process_query(query, history):
-    try:
-        # Restore chat history from Gradio's history
-        if history:
-            for human_msg, ai_msg in history:
-                if chat_memory and hasattr(chat_memory, 'chat_memory'):
-                    chat_memory.chat_memory.add_user_message(human_msg)
-                    chat_memory.chat_memory.add_ai_message(ai_msg)
-        
-        # Store original query in query memory
-        query_memory.memories['original_query'] = query
-        
-        # Execute query through planning agent
-        response = planning_agent.execute(query)
-        
-        # Add current interaction to chat memory
-        if chat_memory and hasattr(chat_memory, 'chat_memory'):
-            chat_memory.chat_memory.add_user_message(query)
-            chat_memory.chat_memory.add_ai_message(response)
-        
-        return response
-
-    except Exception as e:
-        error_msg = f"Error processing query: {str(e)}"
-        logger.error(f"Error details: {str(e)}")
-
-        if chat_memory and hasattr(chat_memory, 'chat_memory'):
-            chat_memory.chat_memory.add_user_message(query)
-            chat_memory.chat_memory.add_ai_message(error_msg)
-
-        return error_msg
-
-def clear_context():
-    planning_agent.clear_context()
-    chat_memory.clear()
-    query_memory.memories.clear()
-    return [], []
-
-def create_gradio_app():
-    from interface import create_interface
-    return create_interface(process_query, clear_context)
-
-def main():
-    """Main application entry point"""
-    try:
-        initialize_components()
-        app = create_gradio_app()
-        app.queue()
-        app.launch(server_name="0.0.0.0", server_port=7860, share=True)
-        
-        a_task = Task.get_task(project_name='langchain_callback_demo', task_name='llm')
-        loer = a_task.get_logger()
-        report_logs(loer)
-        # report text as debug example
-        report_debug_text(loer)
-        loer.flush()
-    except Exception as e:
-        logger.error(f"Error in main: {str(e)}")
-        raise
-
-if __name__ == "__main__":
+import gradio as gr
+from langchain_community.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory, SimpleMemory
+from langchain.agents import initialize_agent, AgentType
+from langchain_community.callbacks import ClearMLCallbackHandler
+from langchain_core.callbacks import StdOutCallbackHandler
+from clearml import Logger, Task
+from dotenv import load_dotenv
+from dotenv import load_dotenv, find_dotenv
+import os
+import agent.planning_agent as planning_agent
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Global variables
+llm = None
+chat_memory = None
+query_memory = None
+clearml_callback = None
+
+def initialize_components():
+    global llm, chat_memory, query_memory, clearml_callback
+    load_dotenv()
+    
+    OPENAI_API_KEY="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"
+    CLEARML_API_ACCESS_KEY="NYZZ07E2ZEW08V4DUGYY2PA7O6JX5F"
+    CLEARML_API_SECRET_KEY="MkfQrIOuKNFRWHfCz32cN-UVm_19M7_vgxAwRn8twnvHYJ1xeqD9T2GZcIX9RwnD8mw"
+    SERPAPI_API_KEY="619f2302253fbe56448bcf82565caf2a3263d845944682533f10b09a0d1650e6"
+    
+    # openai.api_key=OPENAI_API_KEY
+    
+    llm = ChatOpenAI(
+        model_name="gpt-3.5-turbo",
+        temperature=0,
+        openai_api_key="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"
+    )
+
+    # Initialize memories
+    chat_memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        return_messages=True
+    )
+    query_memory = SimpleMemory()
+
+    # Setup and use the ClearML Callback
+    clearml_callback = ClearMLCallbackHandler(
+    task_type="inference",
+    project_name="langchain_callback_demo",
+    task_name="llm",
+    tags=["test"],
+    # Change the following parameters based on the amount of detail you want tracked
+    visualize=True,
+    complexity_metrics=True,
+    stream_logs=True,)
+    
+    callbacks = [StdOutCallbackHandler(), clearml_callback]
+    
+    # Initialize planning agent with both memories
+    planning_agent.initialize_planning_agent(llm, chat_memory, query_memory, callbacks)
+
+    logger.info("Components initialized successfully")
+
+def process_query(query, history):
+    try:
+        # Restore chat history from Gradio's history
+        if history:
+            for human_msg, ai_msg in history:
+                if chat_memory and hasattr(chat_memory, 'chat_memory'):
+                    chat_memory.chat_memory.add_user_message(human_msg)
+                    chat_memory.chat_memory.add_ai_message(ai_msg)
+        
+        # Store original query in query memory
+        query_memory.memories['original_query'] = query
+        
+        # Execute query through planning agent
+        response = planning_agent.execute(query)
+        #clearml_callback.flush_tracker(name="Planning agent", finish=True)
+        #clearml_callback.flush_tracker(langchain_asset=llm, name="simple_sequential")
+        
+        # Add current interaction to chat memory
+        if chat_memory and hasattr(chat_memory, 'chat_memory'):
+            chat_memory.chat_memory.add_user_message(query)
+            chat_memory.chat_memory.add_ai_message(response)
+        
+        return response
+
+    except Exception as e:
+        error_msg = f"Error processing query: {str(e)}"
+        logger.error(f"Error details: {str(e)}")
+
+        if chat_memory and hasattr(chat_memory, 'chat_memory'):
+            chat_memory.chat_memory.add_user_message(query)
+            chat_memory.chat_memory.add_ai_message(error_msg)
+
+        return error_msg
+
+def clear_context():
+    planning_agent.clear_context()
+    chat_memory.clear()
+    query_memory.memories.clear()
+    return [], []
+
+def create_gradio_app():
+    from interface import create_interface
+    return create_interface(process_query, clear_context)
+
+def report_table(loer, iteration=0):
+    # type: (Logger, int) -> ()
+    """
+    reporting tables to the plots section
+    :param logger: The task.logger to use for sending the plots
+    :param iteration: The iteration number of the current reports
+    """
+    # report table
+    # Report table - CSV from path
+    csv_path = './data/cleaned_dataset_full.csv'
+    loer.report_table("Data Set Capstone", "remote csv", iteration=iteration, csv=csv_path)
+
+def main():
+    """Main application entry point"""
+    try:
+        initialize_components()
+        app = create_gradio_app()
+        app.queue()
+        app.launch(server_name="0.0.0.0", server_port=7860, share=True)
+        a_task = Task.get_task(project_name='langchain_callback_demo', task_name='llm')
+        loer = a_task.get_logger()
+        report_logs(loer)
+        # report text as debug example
+        report_debug_text(loer)
+        # report graphs
+        report_table(loer)
+        loer.flush()
+    except Exception as e:
+        logger.error(f"Error in main: {str(e)}")
+        raise
+
+if __name__ == "__main__":
     main()

documents.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0132d52582d53422098dfaec3a361e369e791db0a137d8b012ad920851f18b3f
+size 2382882

interface.py

@@ -0,0 +1,45 @@
+import gradio as gr
+
+def create_interface(process_query, clear_context):
+    with gr.Blocks(title="AI Assistant") as demo:
+        chatbot = gr.Chatbot(
+            [],
+            elem_id="chatbot",
+            bubble_full_width=False,
+            height=400
+        )
+        
+        with gr.Row():
+            msg = gr.Textbox(
+                label="Your Message",
+                placeholder="Type your message here...",
+                scale=8
+            )
+            submit = gr.Button("Submit", scale=1)
+        
+        clear = gr.Button("Clear")
+
+        def process_message(message, history):
+            response = process_query(message, history)
+            history.append((message, response))
+            return "", history
+
+        msg.submit(
+            process_message,
+            [msg, chatbot],
+            [msg, chatbot]
+        )
+        
+        submit.click(
+            process_message,
+            [msg, chatbot],
+            [msg, chatbot]
+        )
+        
+        clear.click(
+            clear_context,
+            None,
+            [chatbot, msg]
+        )
+
+    return demo

requirements.txt

@@ -1,36 +1,36 @@
-# LangChain Ecosystem
-langchain==0.1.3
-langchain_openai==0.0.5
-langchain-community
-langchain-core
-
-# Vector Databases & Similarity Search
-chromadb>=0.5.0
-faiss-cpu
-
-# OpenAI
-openai==1.55.3
-tiktoken>=0.5.2
-
-# UI Framework
-gradio==4.44.1
-setuptools>=65.5.1
-
-# Data Processing & Utils
-numpy==1.26.2
-pandas==2.1.3
-python-dotenv==1.0.0
-
-# Core Dependencies
-#pydantic==2.5.2
-typing-extensions>=4.11
-requests==2.31.0
-
-# ClearML 
-clearml
-spacy>=3.0.0,<4.0.0
-en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl
-textstat
-serpapi==0.1.5
-google-search-results
+# LangChain Ecosystem
+langchain==0.1.3
+langchain_openai==0.0.5
+langchain-community
+langchain-core
+
+# Vector Databases & Similarity Search
+chromadb>=0.5.0
+faiss-cpu
+
+# OpenAI
+openai==1.55.3
+tiktoken>=0.5.2
+
+# UI Framework
+gradio==4.44.1
+setuptools>=65.5.1
+
+# Data Processing & Utils
+numpy==1.26.2
+pandas==2.1.3
+python-dotenv==1.0.0
+
+# Core Dependencies
+#pydantic==2.5.2
+typing-extensions>=4.11
+requests==2.31.0
+
+# ClearML 
+clearml
+spacy>=3.0.0,<4.0.0
+en_core_web_sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl
+textstat
+serpapi==0.1.5
+google-search-results
 numexpr