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| <title>Genesis AI — Full Code Flow</title> | |
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| </style> | |
| </head> | |
| <body> | |
| <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> | |
| <button <span class="fn">onClick</span>={() => <span class="fn">setUploadOpen</span>(<span class="kw">true</span>)}> | |
| ↑ Upload | |
| </button> | |
| <span class="cm">// Opens modal</span> | |
| <<span class="fn">UploadModal</span> | |
| open={uploadOpen} | |
| onClose={() => <span class="fn">setUploadOpen</span>(<span class="kw">false</span>)} | |
| onSuccess={handleSuccess} | |
| /></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>(() => { | |
| <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 < <span class="st">0</span> and res.dist_e > <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} & {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 >= <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> | |
| <div style="text-align:center;font-size:11px;color:#aaa;margin-top:24px;padding-top:14px;border-top:1px solid #e0e0e0"> | |
| Genesis AI · Price Elasticity Intelligence · Dabur Sarson Amla | Generated by Claude Code | |
| </div> | |
| </body> | |
| </html> | |