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| import pandas as pd | |
| import pdfplumber | |
| import docx | |
| import openai | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| import gradio as gr | |
| openai.api_key = 'sk-proj-PMkGJxtGRdaihzh15yJYT3BlbkFJ0bEWbrsZjjwV5d3XYSFc' | |
| def load_file(file): | |
| file_type = file.name.split('.')[-1] | |
| if file_type == 'csv': | |
| return pd.read_csv(file.name) | |
| elif file_type in ['xls', 'xlsx']: | |
| return pd.read_excel(file.name) | |
| elif file_type == 'pdf': | |
| return load_pdf(file) | |
| elif file_type in ['doc', 'docx']: | |
| return load_doc(file) | |
| else: | |
| raise ValueError("Unsupported file type") | |
| def load_pdf(file): | |
| with pdfplumber.open(file.name) as pdf: | |
| pages = [page.extract_text() for page in pdf.pages] | |
| text = "\n".join(pages) | |
| return pd.DataFrame({"text": [text]}) | |
| def load_doc(file): | |
| doc = docx.Document(file.name) | |
| text = "\n".join([para.text for para in doc.paragraphs]) | |
| return pd.DataFrame({"text": [text]}) | |
| def generate_query(prompt): | |
| response = openai.Completion.create( | |
| engine="text-davinci-003", | |
| prompt=prompt, | |
| max_tokens=150 | |
| ) | |
| return response.choices[0].text.strip() | |
| def handle_query(query, df): | |
| if "number of columns" in query.lower(): | |
| return f"The number of columns is {df.shape[1]}" | |
| elif "number of rows" in query.lower(): | |
| return f"The number of rows is {df.shape[0]}" | |
| else: | |
| try: | |
| # Try executing the query as a pandas query | |
| result_df = df.query(query) | |
| return result_df.to_html() | |
| except Exception as e: | |
| return str(e) | |
| def draw_chart(query, df): | |
| try: | |
| result_df = df.query(query) | |
| sns.scatterplot(data=result_df, x=result_df.columns[0], y=result_df.columns[1]) | |
| plt.title("Generated Chart") | |
| plt.xlabel(result_df.columns[0]) | |
| plt.ylabel(result_df.columns[1]) | |
| plt.savefig('/content/chart.png') | |
| plt.close() | |
| return '/content/chart.png' | |
| except Exception as e: | |
| return str(e) | |
| def generate_query(prompt): | |
| response = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo", | |
| messages=[ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "user", "content": prompt} | |
| ] | |
| ) | |
| return response['choices'][0]['message']['content'].strip() | |
| def handle_query(query, df): | |
| if "number of columns" in query.lower(): | |
| return f"The number of columns is {df.shape[1]}" | |
| elif "number of rows" in query.lower(): | |
| return f"The number of rows is {df.shape[0]}" | |
| else: | |
| try: | |
| result_df = df.query(query) | |
| return result_df.to_html() | |
| except Exception as e: | |
| return str(e) | |
| def draw_chart(query, df): | |
| try: | |
| result_df = df.query(query) | |
| sns.scatterplot(data=result_df, x=result_df.columns[0], y=result_df.columns[1]) | |
| plt.title("Generated Chart") | |
| plt.xlabel(result_df.columns[0]) | |
| plt.ylabel(result_df.columns[1]) | |
| plt.savefig('/content/chart.png') | |
| plt.close() | |
| return '/content/chart.png' | |
| except Exception as e: | |
| return str(e) | |
| def chatbot(file, input_text): | |
| try: | |
| # Load the file into a DataFrame | |
| df = load_file(file) | |
| # Generate a query from the input text | |
| query = generate_query(input_text) | |
| # Handle the query and generate a response | |
| response = handle_query(query, df) | |
| # If the query is suitable for generating a chart, do so | |
| if "chart" in query.lower() or "graph" in query.lower(): | |
| chart_path = draw_chart(query, df) | |
| return chart_path, response | |
| # Return the query response | |
| return None, response | |
| except Exception as e: | |
| return None, str(e) | |
| # Create a Gradio interface | |
| iface = gr.Interface( | |
| fn=chatbot, | |
| inputs=[gr.File(type="filepath", label="Upload File"), gr.Textbox(lines=2, placeholder="Enter your query here...")], | |
| outputs=["image", "html"], | |
| title="Data Analyst Chatbot", | |
| description="Upload a file and enter a query to get responses based on the data." | |
| ) | |
| # Launch the interface | |
| iface.launch() |