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.gitattributes

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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
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+*.xz filter=lfs diff=lfs merge=lfs -text
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Dockerfile

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+FROM python:3.12-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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----
-title: Chatplot
-emoji: ⚡
-colorFrom: red
-colorTo: indigo
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Chatplot
+emoji: ⚡
+colorFrom: red
+colorTo: indigo
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

main.py

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+from fastapi import FastAPI, Request
+from fastapi.responses import HTMLResponse, JSONResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from io import BytesIO
+import base64
+import matplotlib.pyplot as plt
+import pandas as pd
+from google import genai
+from google.genai import types
+import os
+
+# ---- User configuration ----
+API_KEY = os.getenv("GEMINI_API_KEY", "YOUR_API_KEY")
+EXCEL_FILE = "/content/heart_failure_clinical_records_dashboard.xlsx"
+
+client = genai.Client(api_key=API_KEY)
+MODEL = "gemini-2.5-flash-lite"
+
+# FastAPI setup
+app = FastAPI()
+app.mount("/static", StaticFiles(directory="static"), name="static")
+templates = Jinja2Templates(directory="templates")
+
+# Load dataset
+df = pd.read_excel(EXCEL_FILE)
+
+def get_metadata(df):
+    return {
+        "columns": list(df.columns),
+        "dtypes": df.dtypes.apply(lambda x: str(x)).to_dict(),
+        "num_rows": df.shape[0],
+        "num_cols": df.shape[1],
+        "null_counts": df.isnull().sum().to_dict(),
+        "unique_counts": df.nunique().to_dict(),
+        "sample_rows": df.head(3).to_dict(orient="records")
+    }
+
+def generate_plot_code(user_query, metadata):
+    system_prompt = f"""
+You are a Python plotting assistant. 
+Use the existing DataFrame named df.
+Do NOT load any files. 
+Use only matplotlib or pandas plotting.
+Use only the following columns: {metadata['columns']}.
+Do NOT explain, do NOT add extra text.
+Only produce executable code for plotting the requested chart.
+"""
+    user_prompt = f"""
+Dataset metadata:
+Columns: {metadata['columns']}
+Data types: {metadata['dtypes']}
+Null counts: {metadata['null_counts']}
+Unique counts: {metadata['unique_counts']}
+Sample rows: {metadata['sample_rows']}
+
+User request: {user_query}
+"""
+    contents = [types.Content(role="user", parts=[types.Part.from_text(text=user_prompt)])]
+    config = types.GenerateContentConfig(
+        temperature=0,
+        max_output_tokens=1000,
+        thinking_config=types.ThinkingConfig(thinking_budget=0),
+        system_instruction=[types.Part.from_text(text=system_prompt)]
+    )
+
+    code = ""
+    for chunk in client.models.generate_content_stream(model=MODEL, contents=contents, config=config):
+        code += chunk.text
+    return code.replace("```python", "").replace("```", "").strip()
+
+@app.get("/", response_class=HTMLResponse)
+async def home(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
+@app.post("/generate_plot")
+async def generate_plot(request: Request):
+    data = await request.json()
+    user_query = data.get("query", "")
+    metadata = get_metadata(df)
+
+    # Generate code
+    code = generate_plot_code(user_query, metadata)
+
+    # Execute code and generate plot
+    try:
+        exec_globals = {"df": df, "plt": plt}
+        exec(code, exec_globals)
+        buf = BytesIO()
+        plt.savefig(buf, format="png")
+        plt.close()
+        buf.seek(0)
+        img_base64 = base64.b64encode(buf.read()).decode("utf-8")
+        success = True
+    except Exception as e:
+        img_base64 = ""
+        success = False
+        code += f"\n\n# ERROR: {str(e)}"
+
+    return JSONResponse({"success": success, "plot": img_base64, "code": code})

requirements.txt

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+fastapi
+uvicorn
+jinja2
+pandas
+matplotlib
+google-genai

static/tailwind.css

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+@import url('https://cdn.jsdelivr.net/npm/tailwindcss@3.3.3/dist/tailwind.min.css');

templates/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>AI Plotting App</title>
+<link href="/static/tailwind.css" rel="stylesheet">
+</head>
+<body class="bg-gray-100 min-h-screen flex flex-col items-center p-4">
+
+<h1 class="text-2xl font-bold mb-4">AI Plotting App</h1>
+
+<div class="w-full max-w-2xl bg-white p-4 rounded shadow mb-4">
+    <input id="userQuery" type="text" placeholder="Enter plot request..." class="w-full border p-2 rounded mb-2">
+    <button onclick="generatePlot()" class="bg-blue-600 text-white px-4 py-2 rounded">Generate Plot</button>
+</div>
+
+<div class="w-full max-w-2xl bg-white p-4 rounded shadow mb-4">
+    <h2 class="font-bold mb-2">Plot Output</h2>
+    <img id="plotImg" class="w-full" />
+</div>
+
+<div class="w-full max-w-2xl bg-white p-4 rounded shadow">
+    <h2 class="font-bold mb-2">Generated Code & Console</h2>
+    <pre id="consoleOutput" class="overflow-x-auto"></pre>
+</div>
+
+<script>
+async function generatePlot() {
+    const query = document.getElementById("userQuery").value;
+    const response = await fetch("/generate_plot", {
+        method: "POST",
+        headers: {"Content-Type": "application/json"},
+        body: JSON.stringify({query})
+    });
+    const data = await response.json();
+    if(data.success){
+        document.getElementById("plotImg").src = "data:image/png;base64," + data.plot;
+    }
+    document.getElementById("consoleOutput").textContent = data.code;
+}
+</script>
+
+</body>
+</html>