message

app.py

@@ -1,33 +1,50 @@
-import gradio as gr
-import fastapi
-from fastapi import FastAPI
-from transformers import pipeline
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import openai
+from fastapi import FastAPI, UploadFile, File
+import io
 
 # Initialize FastAPI app
 app = FastAPI()
 
-# Load Hugging Face model (text generation)
-generator = pipeline("text-generation", model="gpt2")
+# Function to generate Python visualization code using Hugging Face model
+def generate_viz_code(prompt: str) -> str:
+    """Generate Python code for visualization based on user prompt."""
+    response = openai.ChatCompletion.create(
+        model="mistralai/Mistral-7B",  # Replace with the actual Hugging Face model
+        messages=[
+            {"role": "system", "content": "You are an AI assistant for data visualization."},
+            {"role": "user", "content": prompt}
+        ]
+    )
+    return response["choices"][0]["message"]["content"]
 
-# Function to generate text using HF model
-def generate_text(prompt: str):
-    result = generator(prompt, max_length=100, do_sample=True)
-    return result[0]["generated_text"]
+# Function to handle file upload and visualization
+@app.post("/visualize")
+def visualize_data(file: UploadFile = File(...), prompt: str = ""):
+    try:
+        # Read the uploaded Excel file
+        contents = file.file.read()
+        df = pd.read_excel(io.BytesIO(contents))
 
-# Define Gradio interface
-gui = gr.Interface(
-    fn=generate_text,
-    inputs=gr.Textbox(label="Enter your prompt"),
-    outputs=gr.Textbox(label="Generated Text"),
-    title="Hugging Face Text Generator",
-    description="Enter a prompt and the AI will generate text using GPT-2."
-)
+        # Generate visualization code
+        code = generate_viz_code(prompt)
+        print("Generated Code:\n", code)  # Debug output
 
-# Integrate Gradio with FastAPI
-app = gr.mount_gradio_app(FastAPI(), gui, path="/")
+        # Execute the generated code
+        exec_globals = {"plt": plt, "sns": sns, "pd": pd, "df": df}
+        exec(code, exec_globals)
 
-# Uncomment to run standalone FastAPI server
+        # Save the generated plot
+        img_path = "visualization.png"
+        plt.savefig(img_path)
+        plt.close()
+        return {"image_path": img_path}
+    except Exception as e:
+        return {"error": str(e)}
+
+# Uncomment below to run standalone FastAPI app
 # if __name__ == "__main__":
 #     import uvicorn
-#     uvicorn.run(app, host="0.0.0.0", port=7860)
-
+#     uvicorn.run(app, host="0.0.0.0", port=8000)

dockerfile

@@ -1,15 +1,16 @@
-# Use a lightweight Python image
+# Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
 FROM python:3.9
 
-# Set the working directory
-WORKDIR /app
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
 
-# Copy the requirements file and install dependencies
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
+WORKDIR /app
 
-# Copy the app files
-COPY . .
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
-# Run the FastAPI application
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8080"]