Upload app.py

src/app.py

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+import streamlit as st
+import pandas as pd
+import plotly.graph_objects as go
+import torch
+from src.backend import ModelResearcher, ModelManager
+from src.benchmarks import BenchmarkSuite
+from src.model_diagnostics import ModelDiagnostics
+import src.ablation_lab as ab # Import the new module
+
+# --- Styling & Config ---
+st.set_page_config(page_title="DeepBench: AI Researcher Workbench", layout="wide", page_icon="🧪")
+
+st.markdown("""
+<style>
+    .stApp { background-color: #0e1117; color: #FAFAFA; }
+    h1, h2, h3 { color: #00d4ff; }
+    .metric-card {
+        background-color: #262730; border: 1px solid #41424C;
+        border-radius: 8px; padding: 15px; margin-bottom: 10px;
+        text-align: center;
+    }
+    .metric-val { font-size: 24px; font-weight: bold; }
+    .stButton>button { width: 100%; border-radius: 5px; }
+</style>
+""", unsafe_allow_html=True)
+
+# --- State Management ---
+if 'manager' not in st.session_state:
+    st.session_state['manager'] = ModelManager(device="cuda" if torch.cuda.is_available() else "cpu")
+
+# --- Sidebar ---
+with st.sidebar:
+    st.title("🧪 DeepBench")
+    st.markdown("### Researcher Control Panel")
+    task = st.selectbox("Domain", ["Language", "Vision"])
+    arch = st.radio("Architecture", ["All", "Transformer", "RNN/RWKV"])
+    st.markdown("---")
+    st.info(f"Device: {st.session_state['manager'].device.upper()}")
+    st.caption("v4.0 Full Suite | Ablation Lab Active")
+
+# --- Tabs ---
+# We add "✂️ Ablation Lab" as the last tab
+tab_names = ["🔍 Discovery", "⚔️ Battle Arena", "💬 Playground", "💾 Hardware Forecast", "🩻 Model X-Ray", "✂️ Ablation Lab"]
+tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(tab_names)
+
+# ================= TAB 1: DISCOVERY =================
+with tab1:
+    researcher = ModelResearcher()
+    col_search, col_res = st.columns([1, 4])
+    with col_search:
+        if st.button("Fetch Models", use_container_width=True):
+            st.session_state['models'] = researcher.search_models(task_domain=task, architecture_type=arch)
+    with col_res:
+        if 'models' in st.session_state:
+            st.dataframe(st.session_state['models'], column_config={"downloads": st.column_config.ProgressColumn("Downloads", format="%d", min_value=0, max_value=1000000)}, use_container_width=True)
+
+# ================= TAB 2: BATTLE ARENA =================
+with tab2:
+    if 'models' in st.session_state:
+        all_ids = st.session_state['models']['model_id'].tolist()
+        select_options = ["None"] + all_ids
+        c1, c2 = st.columns(2)
+        
+        with c1:
+            st.subheader("Champion (Model A)")
+            model_a = st.selectbox("Select Model A", select_options, index=1 if len(all_ids)>0 else 0)
+            quant_a = st.selectbox("Quantization A", ["None (FP16)", "8-bit (Int8)"], key="q_a")
+        with c2:
+            st.subheader("Challenger (Model B)")
+            model_b = st.selectbox("Select Model B", select_options, index=0)
+            quant_b = st.selectbox("Quantization B", ["None (FP16)", "8-bit (Int8)"], key="q_b")
+
+        bench_opts = ["Perplexity", "MMLU", "GSM8K", "ARC-C", "ARC-E", "HellaSwag", "PIQA"]
+        selected_bench = st.multiselect("Benchmarks", bench_opts, default=["Perplexity", "MMLU"])
+        
+        if st.button("⚔️ Run Comparison"):
+            col_a, col_mid, col_b = st.columns([1, 0.1, 1])
+            results_a, results_b = {}, {}
+            q_map_a = "8-bit" if "8-bit" in quant_a else "None"
+            q_map_b = "8-bit" if "8-bit" in quant_b else "None"
+
+            with col_a:
+                if model_a != "None":
+                    succ, msg = st.session_state['manager'].load_model(model_a, quantization=q_map_a)
+                    if succ:
+                        mod, tok = st.session_state['manager'].get_components(model_a, quantization=q_map_a)
+                        suite = BenchmarkSuite(mod, tok, model_id=f"{model_a}_{q_map_a}")
+                        for b in selected_bench:
+                            res = suite.run_benchmark(b, simulation_mode=True)
+                            results_a[b] = res
+                            st.markdown(f"""<div class='metric-card'><div style='color:#aaa;'>{b}</div><div class='metric-val'>{res['score']:.2f}</div><div>{res['rating']}</div></div>""", unsafe_allow_html=True)
+            with col_b:
+                if model_b != "None":
+                    succ, msg = st.session_state['manager'].load_model(model_b, quantization=q_map_b)
+                    if succ:
+                        mod, tok = st.session_state['manager'].get_components(model_b, quantization=q_map_b)
+                        suite = BenchmarkSuite(mod, tok, model_id=f"{model_b}_{q_map_b}")
+                        for b in selected_bench:
+                            res = suite.run_benchmark(b, simulation_mode=True)
+                            results_b[b] = res
+                            st.markdown(f"""<div class='metric-card'><div style='color:#aaa;'>{b}</div><div class='metric-val'>{res['score']:.2f}</div><div>{res['rating']}</div></div>""", unsafe_allow_html=True)
+            
+            if results_a and results_b:
+                st.markdown("### 🕸️ Comparison Map")
+                categories = list(results_a.keys())
+                vals_a = [r['score'] if r['unit'] == "%" else (100-r['score']) for r in results_a.values()]
+                vals_b = [r['score'] if r['unit'] == "%" else (100-r['score']) for r in results_b.values()]
+                fig = go.Figure()
+                fig.add_trace(go.Scatterpolar(r=vals_a, theta=categories, fill='toself', name=f"{model_a} ({q_map_a})", line_color="#00d4ff"))
+                fig.add_trace(go.Scatterpolar(r=vals_b, theta=categories, fill='toself', name=f"{model_b} ({q_map_b})", line_color="#ff0055"))
+                fig.update_layout(polar=dict(radialaxis=dict(visible=True, range=[0, 100])), paper_bgcolor="rgba(0,0,0,0)", font_color="white")
+                st.plotly_chart(fig, use_container_width=True)
+    else:
+        st.warning("Go to Discovery tab first.")
+
+# ================= TAB 3: PLAYGROUND =================
+with tab3:
+    st.subheader("💬 Generation Playground")
+    if 'models' in st.session_state:
+        all_ids = st.session_state['models']['model_id'].tolist()
+        select_options_play = ["None"] + all_ids
+        pc1, pc2 = st.columns(2)
+        with pc1:
+            pm_a = st.selectbox("Generator A", select_options_play, index=1 if len(all_ids)>0 else 0, key="pm_a")
+            pq_a = st.selectbox("Quant A", ["None (FP16)", "8-bit (Int8)"], key="pq_a")
+        with pc2:
+            pm_b = st.selectbox("Generator B", select_options_play, index=0, key="pm_b")
+            pq_b = st.selectbox("Quant B", ["None (FP16)", "8-bit (Int8)"], key="pq_b")
+        user_prompt = st.text_area("Prompt", value="Explain quantum computing like I'm 5.")
+        if st.button("Generate Text"):
+            c1, c2 = st.columns(2)
+            pq_map_a = "8-bit" if "8-bit" in pq_a else "None"
+            pq_map_b = "8-bit" if "8-bit" in pq_b else "None"
+            with c1:
+                if pm_a != "None":
+                    succ, msg = st.session_state['manager'].load_model(pm_a, quantization=pq_map_a)
+                    if succ:
+                        out = st.session_state['manager'].generate_text(pm_a, pq_map_a, user_prompt)
+                        st.info(out)
+            with c2:
+                if pm_b != "None":
+                    succ, msg = st.session_state['manager'].load_model(pm_b, quantization=pq_map_b)
+                    if succ:
+                        out = st.session_state['manager'].generate_text(pm_b, pq_map_b, user_prompt)
+                        st.success(out)
+    else: st.warning("Please fetch models in Tab 1 first.")
+
+# ================= TAB 4: HARDWARE FORECAST =================
+with tab4:
+    st.header("💾 Hardware Forecast")
+    col1, col2 = st.columns(2)
+    with col1:
+        vram_input = st.text_input("Enter Model Size (e.g., 7B, 13B)", value="7B")
+        if st.button("Calculate VRAM"):
+            res = ModelDiagnostics.estimate_vram(vram_input)
+            if res: st.session_state['vram_res'] = res
+            else: st.error("Invalid format.")
+    with col2:
+        if 'vram_res' in st.session_state:
+            res = st.session_state['vram_res']
+            st.success(f"**Results for {res['params_in_billions']}B Params**")
+            st.markdown(f"- **Training (FP32):** `{res['FP32 (Training/Full)']}`\n- **Inference (FP16):** `{res['FP16 (Inference)']}`\n- **Quantized (Int8):** `{res['INT8 (Quantized)']}`")
+
+# ================= TAB 5: MODEL X-RAY =================
+with tab5:
+    st.header("🔍 Model X-Ray")
+    if 'models' in st.session_state:
+        all_ids = st.session_state['models']['model_id'].tolist()
+        xray_model = st.selectbox("Select Model to Inspect", all_ids)
+        if st.button("Scan Layers"):
+            succ, msg = st.session_state['manager'].load_model(xray_model, quantization="None")
+            if succ:
+                mod, _ = st.session_state['manager'].get_components(xray_model, quantization="None")
+                structure = ModelDiagnostics.get_layer_structure(mod)
+                st.text_area("Raw PyTorch Structure", value=structure, height=400)
+    else: st.warning("Go to Discovery tab first.")
+
+# ================= TAB 6: ABLATION LAB (NEW) =================
+with tab6:
+    # We delegate the entire rendering to the specialized module
+    ab.render_ablation_dashboard()