update app

app.py

@@ -1,29 +1,28 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
-import openai
-from fastapi import FastAPI, UploadFile, File
+from fastapi import FastAPI, UploadFile, File, HTTPException
 import io
+from transformers import pipeline
 
 # Initialize FastAPI app
 app = FastAPI()
 
-# Function to generate Python visualization code using Hugging Face model
+# 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 = 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 handle file upload and visualization
-@app.post("/visualize")
+    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))
@@ -47,4 +46,4 @@ def visualize_data(file: UploadFile = File(...), prompt: str = ""):
 # Uncomment below to run standalone FastAPI app
 # if __name__ == "__main__":
 #     import uvicorn
-#     uvicorn.run(app, host="0.0.0.0", port=8000)
+#     uvicorn.run(app, host="0.0.0.0", port=8000)