Add application file

app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+from transformers import pipeline
+
+# Load the Hugging Face pipelines for translation and text generation
+translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
+generator = pipeline("text-generation", model="gpt3.5-turbo")
+
+# 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
+    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()

requirements.txt

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+gradio
+matplotlib
+textstat
+transformers
+langdetect
+torch
+tensorflow