Upload app.py

app.py

@@ -0,0 +1,138 @@
+from fastapi import FastAPI, File, UploadFile, HTTPException, Form
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import HTMLResponse
+from fastapi.staticfiles import StaticFiles
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import os
+import logging
+from huggingface_hub import InferenceClient
+from dotenv import load_dotenv
+import hashlib
+import textwrap
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Load environment variables (adjust path if needed)
+load_dotenv("../.env")  # Assuming .env is in project/
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+
+API_TOKEN = os.getenv("HF_TOKEN")
+if not API_TOKEN:
+    raise ValueError("HUGGINGFACE_API_TOKEN environment variable not set.")
+
+#the model is use here using the huggingface inference client
+MODEL_NAME = "bigcode/starcoder"
+client = InferenceClient(model=MODEL_NAME, token=API_TOKEN)  #for the Api token -->create it in the huggingface account in  settings copy it and then  create a secret in the space an name it HF_TOKEN and pass it in the value 
+
+#for using the model with transformers(its a big model)
+# from transformers import pipeline
+# generator = pipeline("text-generation", model=MODEL_NAME, token=API_TOKEN)
+
+UPLOAD_DIR = "uploads"
+os.makedirs(UPLOAD_DIR, exist_ok=True)
+
+IMAGES_DIR = os.path.join("static", "images")
+os.makedirs(IMAGES_DIR, exist_ok=True)
+
+@app.post("/upload/")
+async def upload_file(file: UploadFile = File(...)):
+    if not file.filename.endswith(".xlsx"):
+        raise HTTPException(status_code=400, detail="File must be an Excel file (.xlsx)")
+
+    file_path = os.path.join(UPLOAD_DIR, file.filename)
+    with open(file_path, "wb") as buffer:
+        buffer.write(await file.read())
+
+    logger.info(f"File uploaded: {file.filename}")
+    return {"filename": file.filename}
+
+@app.post("/generate-visualization/")
+async def generate_visualization(prompt: str = Form(...), filename: str = Form(...)):
+    file_path = os.path.join(UPLOAD_DIR, filename)
+
+    if not os.path.exists(file_path):
+        raise HTTPException(status_code=404, detail="File not found on server.")
+
+    try:
+        df = pd.read_excel(file_path)
+        if df.empty:
+            raise ValueError("Excel file is empty.")
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Error reading Excel file: {str(e)}")
+
+    input_text = f"""
+    Given the DataFrame 'df =pd.read_excel({filename})' with columns {', '.join(df.columns)} and preview:
+    {df.head().to_string()}
+    Write Python code to: {prompt}
+    - Use ONLY 'df' (no external data loading like pd.read_csv).
+    - Use pandas (pd), matplotlib.pyplot (plt), or seaborn (sns).
+    - Include axis labels and a title.
+    - Output ONLY executable code (no comments, functions, print, or triple quotes).
+    """
+
+    try:
+        generated_code = client.text_generation(input_text, max_new_tokens=500)
+        logger.info(f"Generated code:\n{generated_code}")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error querying model: {str(e)}")
+
+    if not generated_code.strip():
+        raise HTTPException(status_code=500, detail="No code generated by the AI model.")
+
+    generated_code = generated_code.strip()
+    if generated_code.startswith('"""') or generated_code.startswith("'''"):
+        generated_code = generated_code.split('"""')[1] if '"""' in generated_code else generated_code.split("'''")[1]
+    if generated_code.endswith('"""') or generated_code.endswith("'''"):
+        generated_code = generated_code.rsplit('"""')[0] if '"""' in generated_code else generated_code.rsplit("'''")[0]
+    generated_code = generated_code.strip()
+
+   
+    lines = generated_code.splitlines()
+    executable_code = "\n".join(
+        line.strip() for line in lines
+        if line.strip() and not line.strip().startswith(('#', 'def', 'class', '"""', "'''"))
+        and not any(kw in line for kw in ["pd.read_csv", "pd.read_excel", "http", "raise", "print"])
+    ).strip()
+
+    executable_code = executable_code.replace("plt.show()", "").strip()
+
+    logger.info(f"Executable code:\n{executable_code}")
+
+    plot_hash = hashlib.md5(f"{filename}_{prompt}".encode()).hexdigest()[:8]
+    plot_filename = f"plot_{plot_hash}.png"
+    plot_path = os.path.join(IMAGES_DIR, plot_filename)
+
+    try:
+        exec_globals = {"pd": pd, "plt": plt, "sns": sns, "df": df}
+        exec(executable_code, exec_globals)
+        plt.savefig(plot_path, bbox_inches="tight")
+        plt.close()
+    except Exception as e:
+        logger.error(f"Error executing code:\n{executable_code}\nException: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Error executing code: {str(e)}")
+
+    if not os.path.exists(plot_path):
+        raise HTTPException(status_code=500, detail="Plot file was not created.")
+
+    return {"plot_url": f"/static/images/{plot_filename}", "generated_code": generated_code}
+
+@app.get("/")
+async def serve_frontend():
+    with open("static/index.html", "r") as f:
+        return HTMLResponse(content=f.read())