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<title>Genesis AI — Full Code Flow</title>
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<h1>Genesis AI — Full Code Flow</h1>
<div class="doc-sub">Every button click, API call, and Python function — in execution order</div>

<!-- ══════════════════════════════════════════════════════ -->
<!-- STEP 1 — UPLOAD BUTTON CLICK                          -->
<!-- ══════════════════════════════════════════════════════ -->
<div class="step">
  <div class="step-hdr">
    <div class="step-num">1</div>
    <div>
      <div class="step-title">User clicks ↑ Upload button</div>
      <div style="margin-top:3px"><span class="tag fe">Frontend</span></div>
    </div>
    <div class="step-file">Header.jsx → UploadModal.jsx</div>
  </div>
  <div class="step-body">
    <div>
      <div class="panel-title">Code that runs (Header.jsx)</div>
<pre><span class="kw">const</span> [uploadOpen, setUploadOpen] = <span class="fn">useState</span>(<span class="kw">false</span>)

<span class="cm">// Button in header</span>
&lt;button <span class="fn">onClick</span>={() =&gt; <span class="fn">setUploadOpen</span>(<span class="kw">true</span>)}&gt;
  ↑ Upload
&lt;/button&gt;

<span class="cm">// Opens modal</span>
&lt;<span class="fn">UploadModal</span>
  open={uploadOpen}
  onClose={() =&gt; <span class="fn">setUploadOpen</span>(<span class="kw">false</span>)}
  onSuccess={handleSuccess}
/&gt;</pre>
    </div>
    <div>
      <div class="panel-title">What happens</div>
      <div class="desc">
        <ul>
          <li><code>setUploadOpen(true)</code> triggers React state update</li>
          <li><code>UploadModal</code> component mounts and becomes visible</li>
          <li>On mount, UploadModal calls <code>POST /api/upload/reset</code> to clear any previous job state on the server</li>
          <li>User sees drag-and-drop zone for <code>.xlsx</code> file</li>
        </ul>
      </div>
    </div>
  </div>
</div>

<div class="connector">↓</div>

<!-- ══════════════════════════════════════════════════════ -->
<!-- STEP 2 — RESET OLD JOB                                -->
<!-- ══════════════════════════════════════════════════════ -->
<div class="step">
  <div class="step-hdr">
    <div class="step-num">2</div>
    <div>
      <div class="step-title">Browser resets server job state</div>
      <div style="margin-top:3px"><span class="tag api">API</span><span class="tag py">Python</span></div>
    </div>
    <div class="step-file">UploadModal.jsx → upload_server.py</div>
  </div>
  <div class="step-body">
    <div>
      <div class="panel-title">Frontend call (UploadModal.jsx)</div>
<pre><span class="cm">// Called when modal opens</span>
<span class="kw">useEffect</span>(() =&gt; {
  <span class="kw">if</span> (open) {
    <span class="fn">fetch</span>(<span class="st">'/api/upload/reset'</span>, { method: <span class="st">'POST'</span> })
  }
}, [open])</pre>
    </div>
    <div>
      <div class="panel-title">Python handler (upload_server.py)</div>
<pre><span class="kw">@app</span>.<span class="fn">post</span>(<span class="st">"/api/upload/reset"</span>)
<span class="kw">def</span> <span class="fn">reset_job</span>():
    <span class="kw">with</span> _job_lock:
        _job[<span class="st">"status"</span>]   = <span class="st">"idle"</span>
        _job[<span class="st">"message"</span>]  = <span class="st">""</span>
        _job[<span class="st">"logs"</span>]     = <span class="st">""</span>
        _job[<span class="st">"filename"</span>] = <span class="kw">None</span>
    <span class="kw">return</span> {<span class="st">"ok"</span>: <span class="kw">True</span>}</pre>
    </div>
  </div>
</div>

<div class="connector">↓</div>

<!-- ══════════════════════════════════════════════════════ -->
<!-- STEP 3 — FILE UPLOAD                                  -->
<!-- ══════════════════════════════════════════════════════ -->
<div class="step">
  <div class="step-hdr">
    <div class="step-num">3</div>
    <div>
      <div class="step-title">User drops / picks .xlsx file → POST /api/upload</div>
      <div style="margin-top:3px"><span class="tag api">API</span><span class="tag py">Python</span></div>
    </div>
    <div class="step-file">UploadModal.jsx → upload_server.py</div>
  </div>
  <div class="step-body">
    <div>
      <div class="panel-title">Frontend call (UploadModal.jsx)</div>
<pre><span class="kw">async function</span> <span class="fn">handleFile</span>(file) {
  <span class="kw">const</span> form = <span class="kw">new</span> <span class="fn">FormData</span>()
  form.<span class="fn">append</span>(<span class="st">'file'</span>, file)

  <span class="kw">const</span> res = <span class="kw">await</span> <span class="fn">fetch</span>(<span class="st">'/api/upload'</span>, {
    method: <span class="st">'POST'</span>,
    body: form          <span class="cm">// multipart/form-data</span>
  })
  <span class="kw">const</span> json = <span class="kw">await</span> res.<span class="fn">json</span>()
  <span class="cm">// → { ok: true, filename: "..." }</span>

  <span class="fn">setPhase</span>(<span class="st">'running'</span>)
  <span class="fn">startPolling</span>()     <span class="cm">// begins GET /status every 1.5s</span>
}</pre>
    </div>
    <div>
      <div class="panel-title">Python handler (upload_server.py)</div>
<pre><span class="kw">@app</span>.<span class="fn">post</span>(<span class="st">"/api/upload"</span>)
<span class="kw">async def</span> <span class="fn">upload_excel</span>(file: UploadFile):
    <span class="cm"># Save file to disk</span>
    dest = UPLOAD_DIR / file.filename
    dest.<span class="fn">write_bytes</span>(<span class="kw">await</span> file.<span class="fn">read</span>())

    <span class="cm"># Launch pipeline in background thread</span>
    thread = threading.<span class="fn">Thread</span>(
        target=_run_pipeline,
        args=(dest,), daemon=<span class="kw">True</span>
    )
    thread.<span class="fn">start</span>()

    <span class="cm"># Return immediately — don't wait for pipeline</span>
    <span class="kw">return</span> {<span class="st">"ok"</span>: <span class="kw">True</span>, <span class="st">"filename"</span>: file.filename}</pre>
    </div>
  </div>
</div>

<div class="connector">↓</div>

<!-- ══════════════════════════════════════════════════════ -->
<!-- STEP 4 — SUBPROCESS                                   -->
<!-- ══════════════════════════════════════════════════════ -->
<div class="step">
  <div class="step-hdr">
    <div class="step-num">4</div>
    <div>
      <div class="step-title">Background thread launches Python pipeline subprocess</div>
      <div style="margin-top:3px"><span class="tag py">Python</span></div>
    </div>
    <div class="step-file">upload_server.py → pipeline/run.py</div>
  </div>
  <div class="step-body">
    <div>
      <div class="panel-title">Code (upload_server.py → _run_pipeline)</div>
<pre><span class="kw">def</span> <span class="fn">_run_pipeline</span>(xlsx_path):
    pipeline_python = <span class="fn">_find_pipeline_python</span>()
    <span class="cm"># finds Python 3.13 that has pandas</span>

    cmd = [
        pipeline_python, <span class="st">"-m"</span>, <span class="st">"pipeline.run"</span>,
        <span class="st">"--input"</span>,  str(xlsx_path),
        <span class="st">"--output"</span>, str(OUTPUT_DIR),
    ]
    env = os.environ.<span class="fn">copy</span>()
    env[<span class="st">"PYTHONIOENCODING"</span>] = <span class="st">"utf-8"</span>

    <span class="cm"># Stream output line-by-line to terminal</span>
    proc = subprocess.<span class="fn">Popen</span>(
        cmd, cwd=str(BASE_DIR),
        stdout=subprocess.PIPE,
        stderr=subprocess.STDOUT,
        text=<span class="kw">True</span>, env=env,
    )
    <span class="kw">for</span> line <span class="kw">in</span> proc.stdout:
        <span class="fn">_annotate</span>(line, xlsx_path.name) <span class="cm"># print step headers</span>
        <span class="fn">_tprint</span>(line)                   <span class="cm"># live terminal output</span></pre>
    </div>
    <div>
      <div class="panel-title">While pipeline runs — browser polls</div>
<pre><span class="cm">// UploadModal.jsx polls every 1500ms</span>
<span class="kw">async function</span> <span class="fn">poll</span>() {
  <span class="kw">const</span> s = <span class="kw">await</span> <span class="fn">fetch</span>(<span class="st">'/api/upload/status'</span>)
  <span class="kw">const</span> l = <span class="kw">await</span> <span class="fn">fetch</span>(<span class="st">'/api/upload/logs'</span>)
  <span class="cm">// shows live logs in modal</span>
  <span class="kw">if</span> (s.status === <span class="st">'done'</span>)  stopPolling()
  <span class="kw">if</span> (s.status === <span class="st">'error'</span>) stopPolling()
}</pre>
      <div class="desc" style="margin-top:10px">
        <ul>
          <li><span class="arrow">→</span> <code>GET /api/upload/status</code> returns <code>{ status, message, elapsed_s }</code></li>
          <li><span class="arrow">→</span> <code>GET /api/upload/logs</code> returns <code>{ logs }</code> — live pipeline stdout</li>
        </ul>
      </div>
    </div>
  </div>
</div>

<div class="connector">↓</div>

<!-- ══════════════════════════════════════════════════════ -->
<!-- STEP 5 — LOADER                                       -->
<!-- ══════════════════════════════════════════════════════ -->
<div class="step">
  <div class="step-hdr">
    <div class="step-num">5</div>
    <div>
      <div class="step-title">Load Excel data — pipeline/loader.py</div>
      <div style="margin-top:3px"><span class="tag py">Python</span></div>
    </div>
    <div class="step-file">pipeline/loader.py</div>
  </div>
  <div class="step-body">
    <div>
      <div class="panel-title">load_source_data( )</div>
<pre><span class="kw">def</span> <span class="fn">load_source_data</span>(path):
    <span class="cm"># Opens sheet 1_Source_Data</span>
    raw = pd.<span class="fn">read_excel</span>(path,
        sheet_name=<span class="st">"1_Source_Data"</span>,
        header=<span class="kw">None</span>)

    <span class="cm"># Finds header row (where col[1] == "Brand")</span>
    hdr_row = <span class="fn">next</span>(i <span class="kw">for</span> i, r <span class="kw">in</span> raw.<span class="fn">iterrows</span>()
        <span class="kw">if</span> str(r.iloc[1]).<span class="fn">strip</span>().<span class="fn">lower</span>() == <span class="st">"brand"</span>)

    df = pd.<span class="fn">read_excel</span>(path,
        sheet_name=<span class="st">"1_Source_Data"</span>,
        skiprows=hdr_row, header=<span class="st">0</span>)

    <span class="cm"># Computes Price = Value / Volume</span>
    df[<span class="st">"Price"</span>] = df[<span class="st">"Value"</span>] / df[<span class="st">"Volume"</span>]
    df[<span class="st">"Date"</span>]  = pd.<span class="fn">to_datetime</span>(df[<span class="st">"Date"</span>])
    df[<span class="st">"Month"</span>] = df[<span class="st">"Date"</span>].dt.<span class="fn">to_period</span>(<span class="st">"M"</span>)
    <span class="kw">return</span> df  <span class="cm"># 3,207 rows</span></pre>
    </div>
    <div>
      <div class="panel-title">read_brand_config( )</div>
<pre><span class="kw">def</span> <span class="fn">read_brand_config</span>(path):
    <span class="cm"># Opens sheet 2_Brand_Config</span>
    raw = pd.<span class="fn">read_excel</span>(path,
        sheet_name=<span class="st">"2_Brand_Config"</span>,
        header=<span class="kw">None</span>)

    <span class="cm"># Reads focal brand name</span>
    focal1 = <span class="fn">_get</span>(<span class="st">"Focal Brand Name"</span>)

    <span class="cm"># Reads competitor brand names</span>
    <span class="kw">for</span> _, row <span class="kw">in</span> raw.<span class="fn">iterrows</span>():
        slot = str(row.iloc[1]).<span class="fn">strip</span>()
        <span class="kw">if</span> slot.<span class="fn">startswith</span>(<span class="st">"Comp"</span>):
            config[<span class="st">"competitors"</span>].<span class="fn">append</span>(row.iloc[2])

    <span class="kw">return</span> config
    <span class="cm"># → { focal_brands: ['DABUR SARSON AMLA'],</span>
    <span class="cm">#     competitors: ['NIHAR...','PARACHUTE'] }</span></pre>
      <div class="desc" style="margin-top:10px">
        <ul>
          <li>Reads <b>sheet 1</b>: 3,207 rows of monthly brand/channel/pack sales data</li>
          <li>Reads <b>sheet 2</b>: focal brand name + competitor list</li>
          <li>Output: clean <code>df</code> DataFrame passed to all subsequent steps</li>
        </ul>
      </div>
    </div>
  </div>
</div>

<div class="connector">↓</div>

<!-- ══════════════════════════════════════════════════════ -->
<!-- STEP 6 — WIDE PIVOT                                   -->
<!-- ══════════════════════════════════════════════════════ -->
<div class="step">
  <div class="step-hdr">
    <div class="step-num">6</div>
    <div>
      <div class="step-title">Build wide pivot tables — pipeline/modelling.py</div>
      <div style="margin-top:3px"><span class="tag py">Python</span></div>
    </div>
    <div class="step-file">pipeline/modelling.py → build_wide_pivot()</div>
  </div>
  <div class="step-body">
    <div>
      <div class="panel-title">build_wide_pivot( ) — called per Channel × Region</div>
<pre><span class="kw">def</span> <span class="fn">build_wide_pivot</span>(df, focal, competitors,
                     channel, region, pack_order):
    sub = df[(df[<span class="st">"Channel"</span>] == channel)
           & (df[<span class="st">"Region"</span>]  == region)]

    <span class="cm"># Focal brand columns</span>
    focal_df = sub[sub[<span class="st">"Brand"</span>] == focal].<span class="fn">rename</span>(columns={
        <span class="st">"Price"</span>: <span class="st">"Price_F"</span>, <span class="st">"Volume"</span>: <span class="st">"Vol_F"</span>,
        <span class="st">"Distribution"</span>: <span class="st">"Dist_F"</span>, <span class="st">"Value"</span>: <span class="st">"Val_F"</span>,
    })

    <span class="cm"># Merge competitor columns</span>
    <span class="kw">for</span> comp <span class="kw">in</span> competitors:
        short = comp[:8].<span class="fn">replace</span>(<span class="st">" "</span>, <span class="st">"_"</span>)
        comp_df = sub[sub[<span class="st">"Brand"</span>] == comp].<span class="fn">rename</span>(columns={
            <span class="st">"Price"</span>: <span class="st">f"Price_{short}"</span>,
            <span class="st">"Volume"</span>: <span class="st">f"Vol_{short}"</span>,
        })
        m = m.<span class="fn">merge</span>(comp_df, on=[<span class="st">"Month"</span>,<span class="st">"Pack Size Group"</span>])

    <span class="cm"># Category volume = sum of all brands</span>
    cat = sub.<span class="fn">groupby</span>([<span class="st">"Month"</span>,<span class="st">"Pack Size Group"</span>])[<span class="st">"Volume"</span>]
            .<span class="fn">sum</span>().<span class="fn">rename</span>(<span class="st">"Cat_Vol"</span>)

    <span class="cm"># Adjacent pack volumes (for cannibalization)</span>
    m[<span class="st">"Vol_Up"</span>]   = <span class="cm"># volume of next larger pack</span>
    m[<span class="st">"Vol_Down"</span>] = <span class="cm"># volume of next smaller pack</span>
    <span class="kw">return</span> m  <span class="cm"># 674 rows, 33 grains total</span></pre>
    </div>
    <div>
      <div class="panel-title">Columns in the output DataFrame</div>
      <div class="desc">
        <ul>
          <li><b>Focal brand:</b> <code>Vol_F, Price_F, Dist_F, Val_F</code></li>
          <li><b>Per competitor:</b> <code>Price_NIHAR__, Vol_NIHAR__, Dist_NIHAR__</code> etc.</li>
          <li><b>Category:</b> <code>Cat_Vol</code> — sum of ALL brands at same CH/RG/Pack/Month</li>
          <li><b>Cannibalization:</b> <code>Vol_Up, Vol_Down</code> — adjacent pack volumes</li>
          <li><b>Result:</b> 33 grains × 24 months = 674 rows total</li>
        </ul>
      </div>
    </div>
  </div>
</div>

<div class="connector">↓</div>

<!-- ══════════════════════════════════════════════════════ -->
<!-- STEP 7 — OLS MODELS                                   -->
<!-- ══════════════════════════════════════════════════════ -->
<div class="step">
  <div class="step-hdr">
    <div class="step-num">7</div>
    <div>
      <div class="step-title">Run OLS elasticity models — pipeline/modelling.py</div>
      <div style="margin-top:3px"><span class="tag py">Python</span></div>
    </div>
    <div class="step-file">pipeline/modelling.py → run_elasticity_models()</div>
  </div>
  <div class="step-body">
    <div>
      <div class="panel-title">run_elasticity_models( ) — tests 343 spec combinations</div>
<pre><span class="kw">def</span> <span class="fn">run_elasticity_models</span>(all_data, competitors, pack_order):
    <span class="kw">for</span> (fb, ch, rg, ps), g <span class="kw">in</span> grains:
        <span class="cm"># Build log-transformed variables</span>
        log_vol  = np.<span class="fn">log</span>(g[<span class="st">"Vol_F"</span>].<span class="fn">clip</span>(1e-9))
        log_p    = np.<span class="fn">log</span>(g[<span class="st">"Price_F"</span>].<span class="fn">clip</span>(1e-9))
        log_dist = np.<span class="fn">log</span>(g[<span class="st">"Dist_F"</span>].<span class="fn">clip</span>(1e-9))
        log_cat  = np.<span class="fn">log</span>(g[<span class="st">"Cat_Vol"</span>].<span class="fn">clip</span>(1e-9))

        <span class="cm"># Test every combination of specs</span>
        <span class="kw">for</span> use_seasonal <span class="kw">in</span> (<span class="kw">False</span>, <span class="kw">True</span>):
          <span class="kw">for</span> combo <span class="kw">in</span> comp_combos:  <span class="cm"># none, 1 comp, 2 comps, 3 comps</span>
            X = [log_p, log_dist, log_cat, *combo]
            res = <span class="fn">ols</span>(log_vol, X)

            <span class="cm"># Sign guardrail: own_e < 0 AND dist_e > 0</span>
            sign_ok = res.own_e &lt; <span class="st">0</span> and res.dist_e &gt; <span class="st">0</span>

        <span class="cm"># Select best Adj-R² with correct sign</span>
        best = ok_specs.<span class="fn">loc</span>[ok_specs[<span class="st">"AdjR2"</span>].<span class="fn">idxmax</span>()]</pre>
    </div>
    <div>
      <div class="panel-title">ols( ) — core OLS estimator</div>
<pre><span class="kw">def</span> <span class="fn">ols</span>(y, X):
    Xc = np.<span class="fn">column_stack</span>([np.<span class="fn">ones</span>(n), X])

    <span class="cm"># Solve via numpy least squares</span>
    betas, _, _, _ = np.linalg.<span class="fn">lstsq</span>(Xc, y)

    <span class="cm"># Compute R², Adj-R², t-stats, p-values</span>
    y_hat  = Xc @ betas
    sse    = ((y - y_hat)**2).<span class="fn">sum</span>()
    sst    = ((y - y.<span class="fn">mean</span>())**2).<span class="fn">sum</span>()
    r2     = <span class="st">1</span> - sse / sst
    adj_r2 = <span class="st">1</span> - (<span class="st">1</span>-r2) * (n-<span class="st">1</span>) / (n-k-<span class="st">1</span>)

    <span class="kw">return</span> {<span class="st">"betas"</span>: betas, <span class="st">"adj_r2"</span>: adj_r2,
            <span class="st">"t"</span>: t, <span class="st">"p"</span>: p}
    <span class="cm"># own-price elasticity = betas[1]  (log-log → direct elasticity)</span></pre>
      <div class="desc" style="margin-top:10px">
        <ul>
          <li>343 spec combos tested per grain</li>
          <li>19 / 26 grains get direct model with correct sign</li>
          <li>7 grains flagged as <code>Forced=True</code> → go to proxies</li>
        </ul>
      </div>
    </div>
  </div>
</div>

<div class="connector">↓</div>

<!-- ══════════════════════════════════════════════════════ -->
<!-- STEP 8 — PROXIES                                      -->
<!-- ══════════════════════════════════════════════════════ -->
<div class="step">
  <div class="step-hdr">
    <div class="step-num">8</div>
    <div>
      <div class="step-title">Assign proxy elasticities — pipeline/proxies.py</div>
      <div style="margin-top:3px"><span class="tag py">Python</span></div>
    </div>
    <div class="step-file">pipeline/proxies.py → assign_proxies()</div>
  </div>
  <div class="step-body">
    <div>
      <div class="panel-title">assign_proxies( ) — fills 7 forced grains</div>
<pre><span class="kw">def</span> <span class="fn">assign_proxies</span>(best_df, pack_order):
    <span class="kw">for</span> _, row <span class="kw">in</span> best_df.<span class="fn">iterrows</span>():
        <span class="kw">if not</span> row[<span class="st">"Forced"</span>]:
            <span class="cm"># Direct model — use as-is</span>
            row[<span class="st">"Final_OwnE"</span>]  = row[<span class="st">"OwnE"</span>]
            row[<span class="st">"IsProxy"</span>]     = <span class="kw">False</span>
            <span class="kw">continue</span>

        <span class="cm"># Priority 1: interpolate adjacent packs</span>
        <span class="kw">if</span> lower <span class="kw">and</span> upper:
            row[<span class="st">"Final_OwnE"</span>] = (lo_e + hi_e) / <span class="st">2</span>
            row[<span class="st">"ProxyMethod"</span>] = <span class="st">f"Interp {lower} &amp; {upper}"</span>

        <span class="cm"># Priority 2: borrow from nearest valid pack</span>
        <span class="kw">else</span>:
            row[<span class="st">"Final_OwnE"</span>] = closest[<span class="st">"OwnE"</span>]
            row[<span class="st">"ProxyMethod"</span>] = <span class="st">f"Borrowed from {closest['Pack']}"</span>

    <span class="cm"># Clamp to [-6.0, 0]  (config: ELASTICITY_ABS_CAP = 6.0)</span>
    final_df[<span class="st">"Final_OwnE"</span>] = final_df[<span class="st">"Final_OwnE"</span>].<span class="fn">clip</span>(lower=<span class="st">-6.0</span>)
    <span class="kw">return</span> final_df  <span class="cm"># 26 rows, all sign-OK</span></pre>
    </div>
    <div>
      <div class="panel-title">compute_freq_anchors( )</div>
<pre><span class="kw">def</span> <span class="fn">compute_freq_anchors</span>(df, focal):
    <span class="kw">for</span> ch <span class="kw">in</span> channels:
      <span class="kw">for</span> rg <span class="kw">in</span> regions:
        <span class="cm"># Vol salience per pack</span>
        vs = { ps: vol / brand_tot * <span class="st">100</span>
               <span class="kw">for</span> ps <span class="kw">in</span> packs }

        <span class="cm"># Anchor = dominant pack  </span>
        <span class="cm"># (must be ≥ 1.20x next largest)</span>
        <span class="kw">if</span> top &gt;= <span class="st">1.20</span> * second:
            anchor = <span class="fn">max</span>(vs, key=vs.get)

        anchors[<span class="st">f"{ch}|{rg}"</span>] = {
            <span class="st">"anchor"</span>: anchor,
            <span class="st">"vol_sal"</span>: vs[anchor]
        }
    <span class="kw">return</span> anchors
    <span class="cm"># e.g. TT|All India → 33-80  (72.5% vol)</span></pre>
    </div>
  </div>
</div>

<div class="connector">↓</div>

<!-- ══════════════════════════════════════════════════════ -->
<!-- STEP 9 — EXPORTERS                                    -->
<!-- ══════════════════════════════════════════════════════ -->
<div class="step">
  <div class="step-hdr">
    <div class="step-num">9</div>
    <div>
      <div class="step-title">Write JSON outputs — pipeline/exporters/</div>
      <div style="margin-top:3px"><span class="tag out">Output</span></div>
    </div>
    <div class="step-file">5 exporter modules → 16 JSON files</div>
  </div>
  <div class="step-body full">
    <div style="display:grid;grid-template-columns:1fr 1fr;gap:16px">
      <div>
        <div class="panel-title">exporters/stats.py</div>
<pre><span class="fn">build_model_export</span>(final_df, grain_metrics)
<span class="cm"># → models.json  (elasticity per grain)</span>

<span class="fn">build_stats_json</span>(df, focal, final_df, anchors)
<span class="cm"># → stats.json   (KPIs: vol, val, growth%)</span>

<span class="cm"># → freq_anchors.json  (dominant packs)</span></pre>

        <div class="panel-title" style="margin-top:12px">exporters/market.py</div>
<pre><span class="fn">build_trend_json</span>(df, focal, competitors)
<span class="cm"># → trend.json   (monthly time-series)</span>

<span class="fn">build_ms_json</span>(df, focal)
<span class="cm"># → ms.json      (market share yr25 vs yr24)</span>

<span class="fn">build_comp_ms_json</span>(df, focal, competitors)
<span class="cm"># → comp_ms.json (all-brand share)</span>

<span class="fn">build_vtm_json</span>(df, focal, competitors)
<span class="cm"># → vtm.json     (volume-to-market)</span>

<span class="fn">build_vol_salience_json</span>(df, focal)
<span class="cm"># → vol_salience.json</span>

<span class="fn">build_val_share_json</span>(df, focal)
<span class="cm"># → val_share.json</span></pre>
      </div>
      <div>
        <div class="panel-title">exporters/ppa.py</div>
<pre><span class="fn">build_ppa_json</span>(df, focal, competitors,
    channel=mt_ch, excel_sheet=<span class="st">"3_PPA_MT"</span>)
<span class="cm"># reads PPA sheet from Excel</span>
<span class="cm"># → ppa_mt.json</span>

<span class="fn">build_ppa_json</span>(df, focal, competitors,
    channel=tt_ch, excel_sheet=<span class="st">"4_PPA_TT"</span>)
<span class="cm"># → ppa_tt.json</span></pre>

        <div class="panel-title" style="margin-top:12px">exporters/analytics.py</div>
<pre><span class="fn">build_interaction_json</span>(df, focal, competitors)
<span class="cm"># Pearson correlation per brand×pack pair</span>
<span class="cm"># → interaction.json</span>

<span class="fn">build_growth_decomp_json</span>(df, focal)
<span class="cm"># pp-contribution to brand vol growth</span>
<span class="cm"># → growth_decomp.json</span>

<span class="fn">build_pgi_json</span>(df, focal, pack_order)
<span class="cm"># price gradient index per channel</span>
<span class="cm"># → pgi.json</span></pre>

        <div class="panel-title" style="margin-top:12px">exporters/recommendations.py</div>
<pre><span class="fn">build_recs_json</span>(df, focal, competitors,
    final_df, pack_order, excel_path)
<span class="cm"># pricing rec cards per CH × Pack</span>
<span class="cm"># → recs_full.json</span></pre>
      </div>
    </div>
  </div>
  <div class="out-grid">
    <div class="out-card"><div class="fname">models.json</div><div class="fused">→ Elasticity Results tab</div></div>
    <div class="out-card"><div class="fname">stats.json</div><div class="fused">→ Header KPIs</div></div>
    <div class="out-card"><div class="fname">freq_anchors.json</div><div class="fused">→ Methodology tab</div></div>
    <div class="out-card"><div class="fname">trend.json</div><div class="fused">→ Trends tab</div></div>
    <div class="out-card"><div class="fname">ms.json</div><div class="fused">→ shared</div></div>
    <div class="out-card"><div class="fname">comp_ms.json</div><div class="fused">→ shared</div></div>
    <div class="out-card"><div class="fname">vtm.json</div><div class="fused">→ Brand Interaction tab</div></div>
    <div class="out-card"><div class="fname">vol_salience.json</div><div class="fused">→ shared</div></div>
    <div class="out-card"><div class="fname">val_share.json</div><div class="fused">→ shared</div></div>
    <div class="out-card"><div class="fname">ppa_mt.json</div><div class="fused">→ PPA tab MT</div></div>
    <div class="out-card"><div class="fname">ppa_tt.json</div><div class="fused">→ PPA tab TT</div></div>
    <div class="out-card"><div class="fname">interaction.json</div><div class="fused">→ Brand Interaction tab</div></div>
    <div class="out-card"><div class="fname">growth_decomp.json</div><div class="fused">→ Growth Decomp tab</div></div>
    <div class="out-card"><div class="fname">pgi.json</div><div class="fused">→ Price Gradient tab</div></div>
    <div class="out-card"><div class="fname">recs_full.json</div><div class="fused">→ Recommendations tab</div></div>
    <div class="out-card"><div class="fname">recs.json</div><div class="fused">→ alias of recs_full</div></div>
  </div>
</div>

<div class="connector">↓</div>

<!-- ══════════════════════════════════════════════════════ -->
<!-- STEP 10 — SERVE DATA                                  -->
<!-- ══════════════════════════════════════════════════════ -->
<div class="step">
  <div class="step-hdr">
    <div class="step-num">10</div>
    <div>
      <div class="step-title">User clicks Reload Dashboard → React fetches all data</div>
      <div style="margin-top:3px"><span class="tag api">API</span><span class="tag py">Python</span><span class="tag fe">Frontend</span></div>
    </div>
    <div class="step-file">DataContext.jsx → upload_server.py → React tabs</div>
  </div>
  <div class="step-body">
    <div>
      <div class="panel-title">Python endpoint (upload_server.py)</div>
<pre><span class="kw">@app</span>.<span class="fn">get</span>(<span class="st">"/api/data/{key}"</span>)
<span class="kw">def</span> <span class="fn">get_data</span>(key: str):
    fname    = DATA_KEY_MAP[key]
    <span class="cm"># e.g. "models" → "models.json"</span>

    json_file = OUTPUT_DIR / fname
    <span class="cm"># e.g. output/models.json</span>

    <span class="fn">_tprint</span>(<span class="st">f"[DATA] GET /api/data/{key}"</span>
            <span class="st">f" → output/{fname}"</span>
            <span class="st">f" → React"</span>)

    <span class="kw">with</span> <span class="fn">open</span>(json_file) <span class="kw">as</span> fh:
        data = json.<span class="fn">load</span>(fh)

    <span class="kw">return</span> <span class="fn">JSONResponse</span>(content=data)</pre>
    </div>
    <div>
      <div class="panel-title">Frontend fetch (DataContext.jsx)</div>
<pre><span class="kw">async function</span> <span class="fn">fetchKey</span>(key) {
  <span class="cm">// 1st: try FastAPI backend</span>
  <span class="kw">try</span> {
    <span class="kw">const</span> res = <span class="kw">await</span> <span class="fn">fetch</span>(<span class="st">`/api/data/${key}`</span>)
    <span class="kw">if</span> (res.ok) <span class="kw">return</span> res.<span class="fn">json</span>()
  } <span class="kw">catch</span> (_) {}

  <span class="cm">// fallback: Vite static file</span>
  <span class="kw">const</span> res = <span class="kw">await</span> <span class="fn">fetch</span>(<span class="st">`/data/${key}.json`</span>)
  <span class="kw">return</span> res.<span class="fn">json</span>()
}

<span class="cm">// Called for all 16 keys</span>
<span class="kw">for</span> (<span class="kw">const</span> key <span class="kw">of</span> KEYS) {
  results[key] = <span class="kw">await</span> <span class="fn">fetchKey</span>(key)
}
<span class="cm">// → React context updated</span>
<span class="cm">// → All 9 tabs re-render</span></pre>
      <div class="desc" style="margin-top:10px">
        <ul>
          <li>Terminal shows: <code>[DATA] GET /api/data/models → output/models.json (15.2 KB) → React</code></li>
          <li>× 16 calls total</li>
          <li>All tabs receive fresh pipeline-computed data</li>
        </ul>
      </div>
    </div>
  </div>
</div>

<hr class="divider"/>

<!-- ══════════════════════════════════════════════════════ -->
<!-- API SUMMARY                                           -->
<!-- ══════════════════════════════════════════════════════ -->
<div class="step">
  <div class="step-hdr">
    <div class="step-num" style="background:#e65100">⚡</div>
    <div><div class="step-title">All API calls — in order of execution</div></div>
  </div>
  <div class="step-body full">
    <table style="width:100%;border-collapse:collapse;font-size:12px">
      <thead>
        <tr style="background:#1a6b2f;color:#fff">
          <th style="padding:8px 12px;text-align:left">Order</th>
          <th style="padding:8px 12px;text-align:left">Method</th>
          <th style="padding:8px 12px;text-align:left">Endpoint</th>
          <th style="padding:8px 12px;text-align:left">Caller</th>
          <th style="padding:8px 12px;text-align:left">Python handler</th>
          <th style="padding:8px 12px;text-align:left">Response</th>
        </tr>
      </thead>
      <tbody>
        <tr style="background:#f8faf8"><td style="padding:7px 12px;font-weight:700;color:#1a6b2f">1</td><td style="padding:7px 12px">POST</td><td style="padding:7px 12px"><code>/api/upload/reset</code></td><td style="padding:7px 12px">UploadModal (on open)</td><td style="padding:7px 12px"><code>reset_job()</code></td><td style="padding:7px 12px"><code>{ ok: true }</code></td></tr>
        <tr><td style="padding:7px 12px;font-weight:700;color:#1a6b2f">2</td><td style="padding:7px 12px">POST</td><td style="padding:7px 12px"><code>/api/upload</code></td><td style="padding:7px 12px">UploadModal (file pick)</td><td style="padding:7px 12px"><code>upload_excel()</code></td><td style="padding:7px 12px"><code>{ ok: true, filename }</code></td></tr>
        <tr style="background:#f8faf8"><td style="padding:7px 12px;font-weight:700;color:#1a6b2f">3…N</td><td style="padding:7px 12px">GET</td><td style="padding:7px 12px"><code>/api/upload/status</code></td><td style="padding:7px 12px">UploadModal (every 1.5s)</td><td style="padding:7px 12px"><code>get_status()</code></td><td style="padding:7px 12px"><code>{ status, elapsed_s }</code></td></tr>
        <tr><td style="padding:7px 12px;font-weight:700;color:#1a6b2f">3…N</td><td style="padding:7px 12px">GET</td><td style="padding:7px 12px"><code>/api/upload/logs</code></td><td style="padding:7px 12px">UploadModal (every 1.5s)</td><td style="padding:7px 12px"><code>get_logs()</code></td><td style="padding:7px 12px"><code>{ logs }</code></td></tr>
        <tr style="background:#f8faf8"><td style="padding:7px 12px;font-weight:700;color:#1a6b2f">N+1</td><td style="padding:7px 12px">GET</td><td style="padding:7px 12px"><code>/api/data/models</code></td><td style="padding:7px 12px">DataContext (reload)</td><td style="padding:7px 12px"><code>get_data("models")</code></td><td style="padding:7px 12px">models.json contents</td></tr>
        <tr><td style="padding:7px 12px;font-weight:700;color:#1a6b2f">N+2</td><td style="padding:7px 12px">GET</td><td style="padding:7px 12px"><code>/api/data/stats</code></td><td style="padding:7px 12px">DataContext (reload)</td><td style="padding:7px 12px"><code>get_data("stats")</code></td><td style="padding:7px 12px">stats.json contents</td></tr>
        <tr style="background:#f8faf8"><td style="padding:7px 12px;font-weight:700;color:#1a6b2f">…</td><td style="padding:7px 12px">GET</td><td style="padding:7px 12px"><code>/api/data/{key}</code> ×14 more</td><td style="padding:7px 12px">DataContext (reload)</td><td style="padding:7px 12px"><code>get_data(key)</code></td><td style="padding:7px 12px">JSON for each key</td></tr>
      </tbody>
    </table>
  </div>
</div>

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  Genesis AI · Price Elasticity Intelligence · Dabur Sarson Amla &nbsp;|&nbsp; Generated by Claude Code
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