Delete src.py

Versions deprecated

src.py

@@ -1,143 +0,0 @@
-import os
-import pandas as pd
-from pandasai import Agent, SmartDataframe
-from typing import Tuple
-from PIL import Image
-from pandasai.llm import HuggingFaceTextGen
-from dotenv import load_dotenv
-from langchain_groq.chat_models import ChatGroq
-from langchain_google_genai import GoogleGenerativeAI
-
-load_dotenv()
-Groq_Token = os.environ["GROQ_API_KEY"]
-models = {"mixtral": "mixtral-8x7b-32768", "llama": "llama2-70b-4096", "gemma": "gemma-7b-it", "gemini-pro": "gemini-pro"}
-
-hf_token = os.getenv("HF_READ")
-gemini_token = os.getenv("GEMINI_TOKEN")
-
-def preprocess_and_load_df(path: str) -> pd.DataFrame:
-    df = pd.read_csv(path)
-    df["Timestamp"] = pd.to_datetime(df["Timestamp"])
-    return df
-
-def load_agent(df: pd.DataFrame, context: str, inference_server: str, name="mixtral") -> Agent:
-    # llm = HuggingFaceTextGen(
-    # inference_server_url=inference_server,
-    # max_new_tokens=250,
-    # temperature=0.1,
-    # repetition_penalty=1.2,
-    # top_k=5,
-    # )
-    # llm.client.headers = {"Authorization": f"Bearer {hf_token}"}
-    if name == "gemini-pro":
-        llm = GoogleGenerativeAI(model=model, google_api_key=gemini_token, temperature=0.1)
-    else:
-        llm = ChatGroq(model=models[name], api_key=os.getenv("GROQ_API"), temperature=0.1)
-    
-    agent = Agent(df, config={"llm": llm, "enable_cache": False, "options": {"wait_for_model": True}})
-    agent.add_message(context)
-    return agent
-
-def load_smart_df(df: pd.DataFrame, inference_server: str, name="mixtral") -> SmartDataframe:
-    # llm = HuggingFaceTextGen(
-    # inference_server_url=inference_server,
-    # )
-    # llm.client.headers = {"Authorization": f"Bearer {hf_token}"}
-    llm = ChatGroq(model=models[name], api_key=os.getenv("GROQ_API"), temperature=0.1)
-    df = SmartDataframe(df, config={"llm": llm, "max_retries": 5, "enable_cache": False})
-    return df
-
-def get_from_user(prompt):
-    return {"role": "user", "content": prompt}
-
-def ask_agent(agent: Agent, prompt: str) -> Tuple[str, str, str]:
-    response = agent.chat(prompt)
-    gen_code = agent.last_code_generated
-    ex_code = agent.last_code_executed
-    last_prompt = agent.last_prompt
-    return {"role": "assistant", "content": response, "gen_code": gen_code, "ex_code": ex_code, "last_prompt": last_prompt}
-
-def decorate_with_code(response: dict) -> str:
-    return f"""<details>
-<summary>Generated Code</summary>
-    
-```python
-{response["gen_code"]}
-```
-</details>
-
-<details>
-<summary>Prompt</summary>
-
-{response["last_prompt"]}
-"""
-
-def show_response(st, response):
-    with st.chat_message(response["role"]):
-        try:
-            image = Image.open(response["content"])
-            if "gen_code" in response:
-                st.markdown(decorate_with_code(response), unsafe_allow_html=True)
-            st.image(image)
-            return {"is_image": True}
-        except Exception as e:
-            if "gen_code" in response:
-                display_content = decorate_with_code(response) + f"""</details>
-
-{response["content"]}"""
-            else:
-                display_content = response["content"]
-            st.markdown(display_content, unsafe_allow_html=True)
-            return {"is_image": False}
-
-def ask_question(model_name, question):
-    if model_name == "gemini-pro":
-        llm = GoogleGenerativeAI(model=model, google_api_key=os.environ.get("GOOGLE_API_KEY"), temperature=0)
-    else:
-        llm = ChatGroq(model=models[model_name], api_key=os.getenv("GROQ_API"), temperature=0.1)
-
-    df_check = pd.read_csv("Data.csv")
-    df_check["Timestamp"] = pd.to_datetime(df_check["Timestamp"])
-    df_check = df_check.head(5)
-
-    new_line = "\n"
-
-    template = f"""```python
-import pandas as pd
-import matplotlib.pyplot as plt
-
-df = pd.read_csv("Data.csv")
-df["Timestamp"] = pd.to_datetime(df["Timestamp"])
-
-    # df.dtypes
-    {new_line.join(map(lambda x: '# '+x, str(df_check.dtypes).split(new_line)))}
-
-    # {question.strip()}
-    # <your code here>
-    ```
-    """
-
-    query = f"""I have a pandas dataframe data of PM2.5 and PM10.
-    * Frequency of data is daily. 
-    * `pollution` generally means `PM2.5`.
-    * Save result in a variable `answer` and make it global.
-    * If result is a plot, save it and save path in `answer`. Example: `answer='plot.png'`
-    * If result is not a plot, save it as a string in `answer`. Example: `answer='The city is Mumbai'`
-
-    Complete the following code.
-
-    {template}
-
-    """
-    if model_name == "gemini-pro":
-        answer = llm.invoke(query)
-    else:
-        answer = llm.invoke(query).content
-    code = f"""
-    {template.split("```python")[1].split("```")[0]}
-    {answer.split("```python")[1].split("```")[0]}
-    """
-    # update variable `answer` when code is executed
-    exec(code)
-
-    return {"role": "assistant", "content": answer.content, "gen_code": code, "ex_code": code, "last_prompt": question}