Update

main.py

@@ -25,59 +25,18 @@ import pandasai as pai
 load_dotenv()
 
 
-'''
-def modify_file_path(file_path):
-    guid = uuid.uuid4()
-    print(guid)
-    guid = str(uuid.uuid4())
-    new_filename = f"{guid}temp_chart.png"
-    new_file_path = os.path.join("/home/runner/Flasktestrairo/plots/", new_filename)
-    # Copy the file
-    shutil.copy2(file_path, new_file_path)
-    return new_file_path
-'''
 
 
 
-guid = uuid.uuid4()
-new_filename = f"{guid}"
-user_defined_path = os.path.join("/plots/", new_filename)
-
-
-class FlaskResponse(ResponseParser):
-  def __init__(self, context) -> None:
-      super().__init__(context)
-
-  def format_dataframe(self, result):
-      return result['value'].to_html()
-
-  def format_plot(self, result):
-        # Save the plot using savefig
-      try:
-          
-          img_path = result['value']
-      
-        
-      except ValueError:
-          img_path = str(result['value'])
-          print("value error!", img_path)
-        
-      print("response_class_path:", img_path)
-      return img_path
-
-  def format_other(self, result):
-      return str(result['value'])
-
-
 # messenger object
 messenger = WhatsApp(
   os.environ["whatsapp_token"],
   phone_number_id=os.environ["phone_number_id"] )
 
-aai.settings.api_key = os.environ["aai_key"]
-transcriber = aai.Transcriber()
+# aai.settings.api_key = os.environ["aai_key"]
+# transcriber = aai.Transcriber()
+
 
-g_api_key = os.environ["google_api_key"]
 
 
 
@@ -85,23 +44,64 @@ app = Flask(__name__)
 
 VERIFY_TOKEN = "30cca545-3838-48b2-80a7-9e43b1ae8ce4"
 
-#df = pd.read_excel("craig.xlsx")
-df = pd.read_csv("small_business_data2.csv")
 
-
-llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=g_api_key, temperature=0.2)
+client = openai.OpenAI(
+    api_key=os.environ.get("sambanova_api_key"),
+    base_url="https://api.sambanova.ai/v1",
+)
 
 def generateResponse(prompt):
-  llm = GoogleGemini(api_key=g_api_key)
-  pandas_agent = Agent(df,config={"llm":llm, "save_charts_path": user_defined_path,"save_charts": False,"response_parser":FlaskResponse, "enable_cache": False, "conversational":True})
-  answer = pandas_agent.chat(prompt)
-  return answer
+  #----- Call API to classify and extract relevant transaction information
+    # These templates help provide a unified response format for use as context clues when
+    # parsing the AI generated response into a structured data format
+    relevant_info_template = """
+          Intent: The CRUD operation
+          Transaction Type: The type of transaction
+          Details: as a sublist of the key details like name of item, amount, description, among other details you are able to extract. 
+      """
+    sample_response_template = """
+      The information provided indicates that you want to **create/record** a new transaction.
+
+      **Extracted Information**:
+
+      **Intent**: Create
+
+      Transaction 1: 
+
+      **Transaction Type**: Purchase
+      **Details**: 
+              - Item: Car
+              - Purpose: Business
+              - Amount: 1000
+              - Tax: 200
+              - Note: A new car for business
+
+      Transaction 2:
+
+      **Transaction Type**: Expense  
+      **Details**:  
+            - Item: Office Chair  
+            - Amount: 300 USD  
+            - Category: Furniture  
+      """
+    response = client.chat.completions.create(
+        model='Meta-Llama-3.1-70B-Instruct',
+        messages=[
+              {"role": "system", "content": f"You are a helpful assistant that classifies transactions written in natural language into CRUD operations (Create, Read, Update, and Delete) and extracts relevant information. Format the relevant information extracted from the transaction text in this format: {relevant_info_template}. You can use markdown syntax to present a nicely formated and readable response to the user, but make sure the user does not see the markdown keyword. Keywords and field names must be in bold face. A sample response could look like this: {sample_response_template}. Delineate multiple transactions with the label 'Transaction 1' before the start of the relevant information for each transaction. There should be only one intent even in the case of multiple transactions."},
+              {"role": "user", "content": prompt}
+        ]
+    )
+    #----- Process response
+    try:
+      response = response.choices[0].message.content
+    except Exception as e:
+        print(f'An error occurred: {str(e)}')
+        response = None
+
+    return response
+
 
-img_ID = "344744a88ad1098"
-img_secret = "3c542a40c215327045d7155bddfd8b8bc84aebbf"
 
-url = "https://api.imgur.com/3/image"
-headers = {"Authorization": f"Client-ID {img_ID}"}
 
 def respond(query_str:str):
     response = "hello, I don't have a brain yet"
@@ -139,39 +139,25 @@ def hook():
           response = str(generateResponse(message))
           print("Response:", response)
           logging.info(f"\nAnswer: {response}\n")
-          # Check if response is a string and represents a valid image path
-          if isinstance(response, str) and os.path.isfile(os.path.join(response)):
-              image_path = os.path.join(response)
-              print("My image path:", image_path)
-              with open(image_path, "rb") as file:
-                data = file.read()
-                base64_data = base64.b64encode(data)
-
-              # Upload image to Imgur and get URL
-              response = requests.post(url, headers=headers, data={"image": base64_data})
-              url1= response.json()["data"]["link"]
-              print(url1)
-            
-              messenger.send_image(image=url1, recipient_id=mobile)
-          else:
-              # Handle cases where response is not a valid image path
-              messenger.send_message(message=f"{response}", recipient_id=mobile)
-            
-      elif message_type == "audio":
-        audio = messenger.get_audio(data)
-        audio_id, mime_type = audio["id"], audio["mime_type"]
-        audio_url = messenger.query_media_url(audio_id)
-        audio_filename = messenger.download_media(audio_url, mime_type)
-        transcript =transcriber.transcribe(audio_filename)
-        print(audio_filename)
-        print(transcript.text)
-        res = transcript.text
-        logging.info(f"\nAudio: {audio}\n")
-        response = str(generateResponse(res))
-        if isinstance(response, str):
+        # Handle cases where response is not a valid image path
           messenger.send_message(message=f"{response}", recipient_id=mobile)
-        elif isinstance(response, str) and os.path.isfile(response):
-          messenger.send_image(image_path=response, recipient_id=mobile)
+          
+            
+      # elif message_type == "audio":
+      #   audio = messenger.get_audio(data)
+      #   audio_id, mime_type = audio["id"], audio["mime_type"]
+      #   audio_url = messenger.query_media_url(audio_id)
+      #   audio_filename = messenger.download_media(audio_url, mime_type)
+      #   transcript =transcriber.transcribe(audio_filename)
+      #   print(audio_filename)
+      #   print(transcript.text)
+      #   res = transcript.text
+      #   logging.info(f"\nAudio: {audio}\n")
+      #   response = str(generateResponse(res))
+      #   if isinstance(response, str):
+      #     messenger.send_message(message=f"{response}", recipient_id=mobile)
+      #   elif isinstance(response, str) and os.path.isfile(response):
+      #     messenger.send_image(image_path=response, recipient_id=mobile)
 
       else:
         messenger.send_message(message="Please send me text or audio messages",recipient_id=mobile)