Create app.py

app.py

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+import os
+import shutil
+import gradio as gr
+from transformers import ReactCodeAgent, HfEngine, Tool
+import pandas as pd
+
+from gradio import Chatbot
+from transformers.agents import stream_to_gradio
+from huggingface_hub import login
+from gradio.data_classes import FileData
+
+login(os.getenv("HUGGINGFACEHUB_API_TOKEN"))
+
+llm_engine = HfEngine("mistralai/Mistral-Nemo-Instruct-2407")
+
+agent = ReactCodeAgent(
+    tools=[],
+    llm_engine=llm_engine,
+    additional_authorized_imports=["numpy", "pandas", "matplotlib.pyplot", "seaborn", "scipy.stats"],
+    max_iterations=10,
+)
+
+base_prompt = """<task>You are an expert data analyst.
+According to the features you have and the data structure given below, determine which feature should be the target.
+Then list 5 interesting questions that could be asked on this data, for instance about specific correlations with target variable.
+Then answer these questions one by one, by finding the relevant numbers.
+<important>Meanwhile, plot some figures using matplotlib/seaborn and save them to the (already existing) folder './figures/': take care to clear each figure with plt.clf() before doing another plot.
+In your final answer: summarize these correlations and trends
+After each number derive real worlds insights, for instance: "Correlation between is_december and boredness is 1.3453, which suggest people are more bored in winter".
+<important>Your final answer should be a long string with at least 3 numbered, detailed parts and a statement of explaining why you chose that as an answer.
+Structure of the data:
+{structure_notes}
+<important>The data file is passed to you as the variable data_file, it is a pandas dataframe, you can use it directly.
+<important>DO NOT try to load data_file, it is already a dataframe pre-loaded in your python interpreter!
+"""
+
+
+def get_images_in_directory(directory):
+    image_extensions = {'.png', '.jpg', '.jpeg', '.gif', '.bmp', '.tiff'}
+
+    image_files = []
+    for root, dirs, files in os.walk(directory):
+        for file in files:
+            if os.path.splitext(file)[1].lower() in image_extensions:
+                image_files.append(os.path.join(root, file))
+    return image_files
+
+def interact_with_agent(file_input, prompt):
+    shutil.rmtree("./figures")
+    os.makedirs("./figures")
+
+    data_file = pd.read_csv(file_input)
+    data_structure_notes = f"""- Description (output of .describe()):
+    {data_file.describe()}
+    - Columns with dtypes:
+    {data_file.dtypes}"""
+
+    prompt = base_prompt.format(structure_notes=data_structure_notes)
+
+    messages = [gr.ChatMessage(role="user", content=prompt)]
+    yield messages + [
+        gr.ChatMessage(role="assistant", content="⏳ _Starting task..._")
+    ]
+
+    plot_image_paths = {}
+    for msg in stream_to_gradio(agent, prompt, data_file=data_file):
+        messages.append(msg)
+        for image_path in get_images_in_directory("./figures"):
+            if image_path not in plot_image_paths:
+                image_message = gr.ChatMessage(
+                    role="assistant",
+                    content=FileData(path=image_path, mime_type="image/png"),
+                )
+                plot_image_paths[image_path] = True
+                messages.append(image_message)
+        yield messages + [
+            gr.ChatMessage(role="assistant", content="⏳ _Still processing..._")
+        ]
+    yield messages
+
+
+with gr.Blocks(
+    theme=gr.themes.Soft(
+        primary_hue=gr.themes.colors.blue,
+        secondary_hue=gr.themes.colors.gray,
+    )
+) as demo:
+    gr.Markdown("""# Mistral-Nemo Data analyst 📊🤔
+Drop a `.csv` file below, add notes to describe this data if needed, and Mistral-Nemo will analyze the file content and draw figures for you!**""")
+    file_input = gr.File(label="Your file to analyze")
+    text_input = gr.Textbox(
+        label="Additional notes to support the analysis"
+    )
+    submit = gr.Button("Run analysis!", variant="primary")
+    chatbot = gr.Chatbot(
+        label="Data Analyst Agent",
+        type="messages",
+        avatar_images=(
+            None,
+            "https://em-content.zobj.net/source/twitter/53/robot-face_1f916.png",
+        ),
+    )
+
+    submit.click(interact_with_agent, [file_input, text_input], [chatbot])
+
+if __name__ == "__main__":
+    demo.launch()