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import streamlit as st |
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from openai import OpenAI |
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class OpenAIAgent: |
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def __init__(self): |
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self.base_url = "https://api.aimlapi.com/v1" |
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self.api_key = "c496d9094ba54ddb9d66eeeb35a6196f" |
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self.api = OpenAI(api_key=self.api_key, base_url=self.base_url) |
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self.system_prompt = "You are an AI assistant specialized in data analysis, visualization, and insights generation. Provide clear and concise responses." |
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def get_insights(self, df, prompt): |
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try: |
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data_sample = df.head(5).to_string() |
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data_info = f"Dataset shape: {df.shape}\nColumns: {', '.join(df.columns)}\n" |
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full_prompt = f"{data_info}\n{data_sample}\n\n{prompt}" |
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completion = self.api.chat.completions.create( |
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model="mistralai/Mistral-7B-Instruct-v0.2", |
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messages=[ |
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{"role": "system", "content": self.system_prompt}, |
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{"role": "user", "content": full_prompt}, |
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], |
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temperature=0.7, |
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max_tokens=500, |
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) |
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return completion.choices[0].message.content |
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except Exception as e: |
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return f"An error occurred while getting insights: {str(e)}" |
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def generate_insights(self, df): |
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insight_type = st.selectbox("Select insight type", |
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["General Insights", "Trend Analysis", "Anomaly Detection", "Predictive Analysis"]) |
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if st.button("Generate Insights"): |
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with st.spinner("Generating insights..."): |
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prompt = f"Analyze the following dataset and provide {insight_type}." |
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ai_response = self.get_insights(df, prompt) |
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st.write(ai_response) |
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def custom_ai_task(self, df): |
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custom_task = st.text_area("Describe your custom task:") |
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if st.button("Execute Custom Task"): |
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with st.spinner("Processing custom task..."): |
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prompt = f"Perform the following task on the given dataset:\n{custom_task}" |
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ai_response = self.get_insights(df, prompt) |
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st.write(ai_response) |
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'''import openai |
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class OpenAIAgent: |
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def __init__(self): |
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# Make sure to set your OpenAI API key in your environment variables |
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openai.api_key = "c496d9094ba54ddb9d66eeeb35a6196f" |
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def get_insights(self, df, prompt): |
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# Prepare the data for the API call |
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data_sample = df.head(5).to_string() |
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messages = [ |
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{"role": "system", "content": "You are a data analyst assistant."}, |
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{"role": "user", "content": f"Here's a sample of my data:\n{data_sample}\n\nBased on this data, {prompt}"} |
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] |
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response = openai.ChatCompletion.create( |
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model="gpt 4o", |
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messages=messages, |
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max_tokens=150 |
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) |
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return response.choices[0].message['content'] |
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''' |
