Update functions/chat_functions.py

functions/chat_functions.py

@@ -1,93 +1,93 @@
-from data_sources import process_data_upload
-
-import gradio as gr
-import json
-
-from haystack.dataclasses import ChatMessage
-from haystack.components.generators.chat import OpenAIChatGenerator
-
-import os
-from getpass import getpass
-from dotenv import load_dotenv
-
-load_dotenv()
-
-if "OPENAI_API_KEY" not in os.environ:
-    os.environ["OPENAI_API_KEY"] = getpass("Enter OpenAI API key:")
-
-chat_generator = OpenAIChatGenerator(model="gpt-4o")
-response = None
-messages = [
-    ChatMessage.from_system(
-        "You are a helpful and knowledgeable agent who has access to an SQL database which has a table called 'data_source'"
-    )
-]
-
-def chatbot_with_fc(message, history):
-    print("CHATBOT FUNCTIONS")
-    from functions import sqlite_query_func
-    from pipelines import rag_pipeline_func
-    import tools
-    import importlib
-    importlib.reload(tools)
-
-    available_functions = {"sql_query_func": sqlite_query_func, "rag_pipeline_func": rag_pipeline_func}
-    messages.append(ChatMessage.from_user(message))
-    response = chat_generator.run(messages=messages, generation_kwargs={"tools": tools.tools})
-
-    while True:
-        # if OpenAI response is a tool call
-        if response and response["replies"][0].meta["finish_reason"] == "tool_calls":
-            function_calls = json.loads(response["replies"][0].content)
-            for function_call in function_calls:
-                ## Parse function calling information
-                function_name = function_call["function"]["name"]
-                function_args = json.loads(function_call["function"]["arguments"])
-
-                ## Find the correspoding function and call it with the given arguments
-                function_to_call = available_functions[function_name]
-                function_response = function_to_call(**function_args)
-                ## Append function response to the messages list using `ChatMessage.from_function`
-                messages.append(ChatMessage.from_function(content=function_response['reply'], name=function_name))
-                response = chat_generator.run(messages=messages, generation_kwargs={"tools": tools.tools})
-
-        # Regular Conversation
-        else:
-            messages.append(response["replies"][0])
-            break
-    return response["replies"][0].content
-
-css= ".file_marker .large{min-height:50px !important;}"
-
-with gr.Blocks(css=css) as demo:
-    title = gr.HTML("<h1 style='text-align:center;'>Virtual Data Analyst</h1>")
-    description = gr.HTML("<p style='text-align:center;'>Upload a CSV file and chat with our virtual data analyst to get insights on your data set</p>")
-    file_output = gr.File(label="CSV File", show_label=True, elem_classes="file_marker", file_types=['.csv'])
-
-    @gr.render(inputs=file_output)
-    def data_options(filename):
-        print(filename)
-        if filename:
-            bot = gr.Chatbot(type='messages', label="CSV Chat Window", show_label=True, render=False, visible=True, elem_classes="chatbot")
-            chat = gr.ChatInterface(
-                                fn=chatbot_with_fc,
-                                type='messages',
-                                chatbot=bot,
-                                title="Chat with your data file",
-                                examples=[
-                                    ["Describe the dataset"],
-                                    ["List the columns in the dataset"],
-                                    ["What could this data be used for?"],
-                                ],
-                                ) 
-            
-            process_upload(filename)
-    
-    def process_upload(upload_value):
-        if upload_value:
-            print("UPLOAD VALUE")
-            print(upload_value)
-            process_data_upload(upload_value)
-        return [], []
-
-                  
+from data_sources import process_data_upload
+
+import gradio as gr
+import json
+
+from haystack.dataclasses import ChatMessage
+from haystack.components.generators.chat import OpenAIChatGenerator
+
+import os
+from getpass import getpass
+from dotenv import load_dotenv
+
+load_dotenv()
+
+if "OPENAI_API_KEY" not in os.environ:
+    os.environ["OPENAI_API_KEY"] = getpass("Enter OpenAI API key:")
+
+chat_generator = OpenAIChatGenerator(model="gpt-4o")
+response = None
+messages = [
+    ChatMessage.from_system(
+        "You are a helpful and knowledgeable agent who has access to an SQL database which has a table called 'data_source'"
+    )
+]
+
+def chatbot_with_fc(message, history):
+    print("CHATBOT FUNCTIONS")
+    from functions import sqlite_query_func
+    from pipelines import rag_pipeline_func
+    import tools
+    import importlib
+    importlib.reload(tools)
+
+    available_functions = {"sql_query_func": sqlite_query_func, "rag_pipeline_func": rag_pipeline_func}
+    messages.append(ChatMessage.from_user(message))
+    response = chat_generator.run(messages=messages, generation_kwargs={"tools": tools.tools})
+
+    while True:
+        # if OpenAI response is a tool call
+        if response and response["replies"][0].meta["finish_reason"] == "tool_calls":
+            function_calls = json.loads(response["replies"][0].text)
+            for function_call in function_calls:
+                ## Parse function calling information
+                function_name = function_call["function"]["name"]
+                function_args = json.loads(function_call["function"]["arguments"])
+
+                ## Find the correspoding function and call it with the given arguments
+                function_to_call = available_functions[function_name]
+                function_response = function_to_call(**function_args)
+                ## Append function response to the messages list using `ChatMessage.from_function`
+                messages.append(ChatMessage.from_function(content=function_response['reply'], name=function_name))
+                response = chat_generator.run(messages=messages, generation_kwargs={"tools": tools.tools})
+
+        # Regular Conversation
+        else:
+            messages.append(response["replies"][0])
+            break
+    return response["replies"][0].text
+
+css= ".file_marker .large{min-height:50px !important;}"
+
+with gr.Blocks(css=css) as demo:
+    title = gr.HTML("<h1 style='text-align:center;'>Virtual Data Analyst</h1>")
+    description = gr.HTML("<p style='text-align:center;'>Upload a CSV file and chat with our virtual data analyst to get insights on your data set</p>")
+    file_output = gr.File(label="CSV File", show_label=True, elem_classes="file_marker", file_types=['.csv'])
+
+    @gr.render(inputs=file_output)
+    def data_options(filename):
+        print(filename)
+        if filename:
+            bot = gr.Chatbot(type='messages', label="CSV Chat Window", show_label=True, render=False, visible=True, elem_classes="chatbot")
+            chat = gr.ChatInterface(
+                                fn=chatbot_with_fc,
+                                type='messages',
+                                chatbot=bot,
+                                title="Chat with your data file",
+                                examples=[
+                                    ["Describe the dataset"],
+                                    ["List the columns in the dataset"],
+                                    ["What could this data be used for?"],
+                                ],
+                                ) 
+            
+            process_upload(filename)
+    
+    def process_upload(upload_value):
+        if upload_value:
+            print("UPLOAD VALUE")
+            print(upload_value)
+            process_data_upload(upload_value)
+        return [], []
+
+