Update app.py

app.py

@@ -1,49 +1,7 @@
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-from fastapi import FastAPI, UploadFile, File, HTTPException
-import io
-from transformers import pipeline
+from fastapi import FastAPI
 
-# Initialize FastAPI app
 app = FastAPI()
 
-# Load Hugging Face model for text-to-code generation
-generator = pipeline("text-generation", model="Salesforce/codegen-350M-mono")
-
-def generate_viz_code(prompt: str) -> str:
-    """Generate Python code for visualization based on user prompt."""
-    response = generator(prompt, max_length=200)
-    return response[0]["generated_text"]
-
-@app.post("/visualizeHuggingFace")
-def visualize_data(file: UploadFile = File(...), prompt: str = ""):
-    try:
-        # Ensure the file is an Excel file
-        if not file.filename.endswith(('.xls', '.xlsx')):
-            raise HTTPException(status_code=400, detail="Only Excel files (.xls, .xlsx) are supported.")
-
-        # Read the uploaded Excel file
-        contents = file.file.read()
-        df = pd.read_excel(io.BytesIO(contents))
-
-        # Generate visualization code
-        code = generate_viz_code(prompt)
-        print("Generated Code:\n", code)  # Debug output
-
-        # Execute the generated code
-        exec_globals = {"plt": plt, "sns": sns, "pd": pd, "df": df}
-        exec(code, exec_globals)
-
-        # 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=8000)
+@app.get("/")
+def greet_json():
+    return {"Hello": "World!"}