gsheet save tool

.gitignore

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+.env
+.venv
+__pycache__/
+.gitattributes
+gradio_cached_examples/
+app_*.py

app.py

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+import streamlit as st
+import os
+import datetime as DT
+import pytz
+import time
+import json
+import re
+from typing import List
+from transformers import AutoTokenizer
+from gradio_client import Client
+from tools import toolsInfo
+
+from dotenv import load_dotenv
+load_dotenv()
+
+useGpt4 = os.environ.get("USE_GPT_4") == "1"
+
+if useGpt4:
+    from openai import OpenAI
+    client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+    MODEL = "gpt-4o-mini"
+    MAX_CONTEXT = 128000
+    tokenizer = AutoTokenizer.from_pretrained("Xenova/gpt-4o")
+else:
+    from groq import Groq
+    client = Groq(
+        api_key=os.environ.get("GROQ_API_KEY"),
+    )
+    MODEL = "llama-3.1-70b-versatile"
+    MODEL = "llama3-groq-70b-8192-tool-use-preview"
+    MAX_CONTEXT = 8000
+    tokenizer = AutoTokenizer.from_pretrained("Xenova/Meta-Llama-3.1-Tokenizer")
+
+
+def countTokens(text):
+    text = str(text)
+    tokens = tokenizer.encode(text, add_special_tokens=False)
+    return len(tokens)
+
+
+SYSTEM_MSG = f"""
+You are a personalized email generator for cold outreach. You take the user through a workflow. Step by step.
+
+- You ask for industry of the recipient
+- His/her role
+- More details about the recipient
+
+Highlight the exact entity you're requesting for.
+Once collected, you store these info in a Google Sheet
+
+"""
+
+USER_ICON = "icons/man.png"
+ASSISTANT_ICON = "icons/magic-wand(1).png"
+TOOL_ICON = "icons/completed-task.png"
+
+IMAGE_LOADER = "icons/ripple.svg"
+TEXT_LOADER = "icons/balls.svg"
+START_MSG = "Let's start 😊"
+
+ROLE_TO_AVATAR = {
+    "user": USER_ICON,
+    "assistant": ASSISTANT_ICON,
+    "tool": TOOL_ICON,
+}
+
+st.set_page_config(
+    page_title="EmailGenie",
+    page_icon=ASSISTANT_ICON,
+)
+ipAddress = st.context.headers.get("x-forwarded-for")
+
+
+def __nowInIST() -> DT.datetime:
+    return DT.datetime.now(pytz.timezone("Asia/Kolkata"))
+
+
+def pprint(log: str):
+    now = __nowInIST()
+    now = now.strftime("%Y-%m-%d %H:%M:%S")
+    print(f"[{now}] [{ipAddress}] {log}")
+
+
+pprint("\n")
+
+st.markdown(
+    """
+    <style>
+    @keyframes blinker {
+        0% {
+            opacity: 1;
+        }
+        50% {
+            opacity: 0.2;
+        }
+        100% {
+            opacity: 1;
+        }
+    }
+
+    .blinking {
+        animation: blinker 3s ease-out infinite;
+    }
+
+    .code {
+        color: green;
+        border-radius: 3px;
+        padding: 2px 4px; /* Padding around the text */
+        font-family: 'Courier New', Courier, monospace; /* Monospace font */
+    }
+
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+
+def __isInvalidResponse(response: str):
+    # new line followed by small case char
+    if len(re.findall(r'\n[a-z]', response)) > 3:
+        return True
+
+    # lot of repeating words
+    if len(re.findall(r'\b(\w+)(\s+\1){2,}\b', response)) > 1:
+        return True
+
+    # lots of paragraphs
+    if len(re.findall(r'\n\n', response)) > 15:
+        return True
+
+
+def __matchingKeywordsCount(keywords: List[str], text: str):
+    return sum([
+        1 if keyword in text else 0
+        for keyword in keywords
+    ])
+
+
+def __isStringNumber(s: str) -> bool:
+    try:
+        float(s)
+        return True
+    except ValueError:
+        return False
+
+
+def __resetButtonState():
+    st.session_state["buttonValue"] = ""
+
+
+def __setStartMsg(msg):
+    st.session_state.startMsg = msg
+
+
+if "chatHistory" not in st.session_state:
+    st.session_state.chatHistory = []
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+if "buttonValue" not in st.session_state:
+    __resetButtonState()
+
+if "startMsg" not in st.session_state:
+    st.session_state.startMsg = ""
+
+
+def __getMessages():
+    def getContextSize():
+        currContextSize = countTokens(SYSTEM_MSG) + countTokens(st.session_state.messages) + 100
+        pprint(f"{currContextSize=}")
+        return currContextSize
+
+    while getContextSize() > MAX_CONTEXT:
+        pprint("Context size exceeded, removing first message")
+        st.session_state.messages.pop(0)
+
+    return st.session_state.messages
+
+
+tools = [
+    toolsInfo["saveInGSheet"]["schema"]
+]
+
+
+def __showTaskStatus(msg):
+    taskContainer = st.container()
+    taskContainer.image(TOOL_ICON, width=30)
+    taskContainer.markdown(
+        f"""
+        <div class='code'>
+        {msg}
+        </div>
+        """,
+        unsafe_allow_html=True
+    )
+
+
+def __processToolCalls(tool_calls):
+    for toolCall in tool_calls:
+        functionName = toolCall.function.name
+        functionToCall = toolsInfo[functionName]["func"]
+        functionArgs = json.loads(toolCall.function.arguments)
+        functionResult = functionToCall(**functionArgs)
+        functionResponse = functionResult["response"]
+        shouldShow = functionResult["shouldShow"]
+        pprint(f"{functionResponse=}")
+
+        if shouldShow:
+            st.session_state.chatHistory.append(
+                {
+                    "role": "tool",
+                    "content": functionResponse,
+                }
+            )
+           
+            __showTaskStatus(functionResponse)
+
+        st.session_state.messages.append(
+            {
+                "role": "tool",
+                "tool_call_id": toolCall.id,
+                "name": functionName,
+                "content": functionResponse,
+            }
+        )
+
+def __process_stream_chunk(chunk):
+    delta = chunk.choices[0].delta
+    if delta.content:
+        return delta.content
+    elif delta.tool_calls:
+        return delta.tool_calls[0]
+    return None
+
+
+def __addToolCallsToMsgs(toolCalls):
+    st.session_state.messages.append(
+        {
+            "role": "assistant",
+            "tool_calls": [
+                {
+                    "id": toolCall.id,
+                    "function": {
+                        "name": toolCall.function.name,
+                        "arguments": toolCall.function.arguments,
+                    },
+                    "type": toolCall.type,
+                }
+                for toolCall in toolCalls
+            ],
+        }
+    )
+
+def __add_tool_call(tool_call):
+    st.session_state.messages.append({
+        "role": "assistant",
+        "tool_calls": [{
+            "id": tool_call.id,
+            "function": {
+                "name": tool_call.function.name,
+                "arguments": tool_call.function.arguments,
+            },
+            "type": tool_call.type,
+        }]
+    })
+
+def predict1():
+    shouldStream = True
+
+    messagesFormatted = [{"role": "system", "content": SYSTEM_MSG}]
+    messagesFormatted.extend(__getMessages())
+    contextSize = countTokens(messagesFormatted)
+    pprint(f"{contextSize=} | {MODEL}")
+
+    response = client.chat.completions.create(
+        model=MODEL,
+        messages=messagesFormatted,
+        temperature=0.8,
+        max_tokens=4000,
+        stream=shouldStream,
+        tools=tools
+    )
+
+    content = ""
+    tool_call = None
+
+    for chunk in response:
+        chunk_content = __process_stream_chunk(chunk)
+        if isinstance(chunk_content, str):
+            content += chunk_content
+            yield chunk_content
+        elif chunk_content:
+            if not tool_call:
+                tool_call = chunk_content
+            else:
+                tool_call.function.arguments += chunk_content.function.arguments
+
+    if tool_call:
+        pprint(f"{tool_call=}")
+
+        __addToolCallsToMsgs([tool_call])
+        try:
+            __processToolCalls([tool_call])
+            return predict()
+        except Exception as e:
+            pprint(e)
+
+
+def __dedupeToolCalls(toolCalls: list):
+    toolCallsDict = {}
+    for toolCall in toolCalls:
+        toolCallsDict[toolCall.function.name] = toolCall
+    dedupedToolCalls = list(toolCallsDict.values())
+
+    if len(toolCalls) != len(dedupedToolCalls):
+        pprint("Deduped tool calls!")
+        pprint(f"{toolCalls=} -> {dedupedToolCalls=}")
+
+    return dedupedToolCalls
+
+
+def predict():
+    shouldStream = False
+
+    messagesFormatted = [{"role": "system", "content": SYSTEM_MSG}]
+    messagesFormatted.extend(__getMessages())
+    contextSize = countTokens(messagesFormatted)
+    pprint(f"{contextSize=} | {MODEL}")
+    pprint(f"{messagesFormatted=}")
+
+    response = client.chat.completions.create(
+        model=MODEL,
+        messages=messagesFormatted,
+        temperature=0.8,
+        max_tokens=4000,
+        stream=shouldStream,
+        tools=tools
+    )
+    # pprint(f"llmResponse: {response}")
+
+    if shouldStream:
+        content = ""
+        toolCall = None
+
+        for chunk in response:
+            chunkContent = __process_stream_chunk(chunk)
+            if isinstance(chunkContent, str):
+                content += chunkContent
+                yield chunkContent
+            elif chunkContent:
+                if not toolCall:
+                    toolCall = chunkContent
+                else:
+                    toolCall.function.arguments += chunkContent.function.arguments
+
+        toolCalls = [toolCall] if toolCall else []
+    else:
+        responseMessage = response.choices[0].message
+        # pprint(f"{responseMessage=}")
+        responseContent = responseMessage.content
+        pprint(f"{responseContent=}")
+        if responseContent:
+            yield responseContent
+        toolCalls = responseMessage.tool_calls
+        # pprint(f"{toolCalls=}")
+
+    if toolCalls:
+        pprint(f"{toolCalls=}")
+        toolCalls = __dedupeToolCalls(toolCalls)
+        __addToolCallsToMsgs(toolCalls)
+        try:
+            __processToolCalls(toolCalls)
+            return predict()
+        except Exception as e:
+            pprint(e)
+
+
+st.title("EmailGenie 💌")
+if not (st.session_state["buttonValue"] or st.session_state["startMsg"]):
+    st.button(START_MSG, on_click=lambda: __setStartMsg(START_MSG))
+
+for chat in st.session_state.chatHistory:
+    role = chat["role"]
+    content = chat["content"]
+    imagePath = chat.get("image")
+    avatar = ROLE_TO_AVATAR[role]
+    with st.chat_message(role, avatar=avatar):
+        if role == "tool":
+            st.markdown(
+                f"""
+                <div class='code'>
+                {content}
+                </div>
+                """,
+                unsafe_allow_html=True
+            )
+        else:
+            st.markdown(content)
+
+        if imagePath:
+            st.image(imagePath)
+
+if prompt := (st.chat_input() or st.session_state["buttonValue"] or st.session_state["startMsg"]):
+    __resetButtonState()
+    __setStartMsg("")
+
+    with st.chat_message("user", avatar=USER_ICON):
+        st.markdown(prompt)
+    pprint(f"{prompt=}")
+    st.session_state.messages.append({"role": "user", "content": prompt })
+    st.session_state.chatHistory.append({"role": "user", "content": prompt })
+
+    with st.chat_message("assistant", avatar=ASSISTANT_ICON):
+        responseContainer = st.empty()
+
+        def __printAndGetResponse():
+            response = ""
+            # responseContainer.markdown(".....")
+            responseContainer.image(TEXT_LOADER)
+            responseGenerator = predict()
+
+            for chunk in responseGenerator:
+                response += chunk
+                if __isInvalidResponse(response):
+                    pprint(f"{response=}")
+                    return
+                responseContainer.markdown(response)
+
+            return response
+
+        response = __printAndGetResponse()
+        while not response:
+            pprint("Empty response. Retrying..")
+            time.sleep(0.5)
+            response = __printAndGetResponse()
+
+        pprint(f"{response=}")
+
+        def selectButton(optionLabel):
+            st.session_state["buttonValue"] = optionLabel
+            pprint(f"Selected: {optionLabel}")
+
+        # responseParts = response.split(JSON_SEPARATOR)
+
+        # jsonStr = None
+        # if len(responseParts) > 1:
+        #     [response, jsonStr] = responseParts
+
+        # if jsonStr:
+        #     try:
+        #         json.loads(jsonStr)
+        #         jsonObj = json.loads(jsonStr)
+        #         options = jsonObj["options"]
+
+        #         for option in options:
+        #             st.button(
+        #                 option["label"],
+        #                 key=option["id"],
+        #                 on_click=lambda label=option["label"]: selectButton(label)
+        #             )
+        #         # st.code(jsonStr, language="json")
+        #     except Exception as e:
+        #         pprint(e)
+
+        st.session_state.messages.append({
+            "role": "assistant",
+            "content": response,
+        })
+        st.session_state.chatHistory.append({
+            "role": "assistant",
+            "content": response,
+        })

requirements.txt

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+python-dotenv
+groq
+transformers
+gradio_client
+oauth2client
+gspread

tools.py

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+
+import gspread
+from oauth2client.service_account import ServiceAccountCredentials
+import os
+import json
+
+# from dotenv import load_dotenv
+# load_dotenv()
+
+GCP_JSON_KEY = os.environ.get("GCP_JSON_KEY")
+
+
+def saveInGSheet(
+    industry: str,
+    role: str,
+    profileDetails: str
+):
+    client = gspread.service_account_from_dict(json.loads(GCP_JSON_KEY))
+
+    workBook = "test_sheet"
+    try:
+        spreadsheet = client.open(workBook)
+    except gspread.SpreadsheetNotFound:
+        spreadsheet = client.create(workBook)
+        spreadsheet.share('[email protected]', perm_type='user', role='writer')
+        print(f"Created new sheet: {spreadsheet.url}")
+
+    sheet = spreadsheet.sheet1
+    sheet.append_row([
+        industry,
+        role,
+        profileDetails
+    ])
+    return {
+        "response": "Saved in GSheet",
+        "shouldShow": True
+    }
+
+
+# saveInGSheet({
+#     "name": "Ashhar",
+#     "email": "XXXXXXXXXXXXXXXXXXXXX",
+#     "subject": "Test Subject",
+#     "message": "Test Message",
+#     "sent": True
+# })
+
+
+toolsInfo = {
+    "saveInGSheet": {
+        "func": saveInGSheet,
+        "schema": {
+            "type": "function",
+            "function": {
+                "name": "saveInGSheet",
+                "description": "Saves the profile details in the GSheet",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "industry": {
+                            "type": "string",
+                            "description": "Industry of the person"
+                        },
+                        "role": {
+                            "type": "string",
+                            "description": "Role of the person"
+                        },
+                        "profileDetails": {
+                            "type": "string",
+                            "description": "Profile details of the person"
+                        }
+                    },
+                    "required": ["industry", "role", "profileDetails"]
+                }
+            }
+        },
+    }
+}
+
+
+# def load_json_and_print():
+#     with open('emailgenie-434420-b9c81c93bb39.json', 'r') as json_file:
+#         data = json.load(json_file)
+#         json_string = json.dumps(data)
+#         print(json_string)
+
+# load_json_and_print()