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main.py

@@ -1,100 +1,96 @@
-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", "AIzaSyB1jgGCuzg7ELPwNEEwaluQZoZhxhgLmAs")
-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})
+from fastapi import FastAPI, Request, File, UploadFile, Form
+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
+
+# ---- Configuration ----
+API_KEY = os.getenv("GEMINI_API_KEY", "YOUR_API_KEY")
+MODEL = "gemini-2.5-flash-lite"
+
+client = genai.Client(api_key=API_KEY)
+
+# FastAPI setup
+app = FastAPI()
+app.mount("/static", StaticFiles(directory="static"), name="static")
+templates = Jinja2Templates(directory="templates")
+
+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_file")
+async def generate_plot_file(file: UploadFile = File(...), query: str = Form(...)):
+    # Read uploaded Excel
+    df = pd.read_excel(file.file)
+    metadata = get_metadata(df)
+
+    # Generate AI plotting code
+    code = generate_plot_code(query, metadata)
+
+    # Execute code
+    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

@@ -3,4 +3,5 @@ uvicorn
 jinja2
 pandas
 matplotlib
-google-genai
+google-genai
+python-multipart

templates/index.html

@@ -11,6 +11,7 @@
 <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="excelFile" type="file" accept=".xlsx" class="w-full mb-2" />
     <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>
@@ -27,11 +28,17 @@
 
 <script>
 async function generatePlot() {
-    const query = document.getElementById("userQuery").value;
-    const response = await fetch("/generate_plot", {
+    const fileInput = document.getElementById("excelFile");
+    const queryInput = document.getElementById("userQuery");
+    if (!fileInput.files.length) { alert("Please upload a file."); return; }
+
+    const formData = new FormData();
+    formData.append("file", fileInput.files[0]);
+    formData.append("query", queryInput.value);
+
+    const response = await fetch("/generate_plot_file", {
         method: "POST",
-        headers: {"Content-Type": "application/json"},
-        body: JSON.stringify({query})
+        body: formData
     });
     const data = await response.json();
     if(data.success){