app.py

import gradio as gr
import matplotlib.pyplot as plt
from transformers import pipeline

# Load the Hugging Face pipeline for translation
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")

# Load the Hugging Face pipeline for text generation using GPT-J
generator = pipeline("text-generation", model="EleutherAI/gpt-j-6B")

# Define the function to generate the graph based on the translated prompt
def generate_graph(prompt):
    # Translate the prompt to the desired language (e.g., from English to Spanish)
    translated_prompt = translator(prompt, max_length=100, src_lang="en", tgt_lang="es")
    translated_text = translated_prompt[0]['translation_text']

    # Generate text using the Hugging Face pipeline with GPT-J
    response = generator(translated_text, max_length=100)
    text = response[0]['generated_text']

    # Generate the graph using Matplotlib
    # Replace this code with your specific graph generation logic
    x = [1, 2, 3, 4, 5]
    y = [2, 4, 6, 8, 10]
    plt.plot(x, y)
    plt.xlabel('X')
    plt.ylabel('Y')
    plt.title('Generated Graph')

    # Save the generated graph to a file
    graph_path = '/path/to/generated_graph.png'
    plt.savefig(graph_path)

    return graph_path

# Create the Gradio interface
iface = gr.Interface(
    fn=generate_graph,
    inputs="text",
    outputs="file",
    title="Graph Generator",
    description="Generate a graph based on a translated prompt",
    examples=[
        ["Translate and generate a graph"],
        ["Translate and graph the relationship between X and Y"],
    ]
)

# Launch the Gradio interface
iface.launch()