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Dockerfile

@@ -15,9 +15,9 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     gcc g++ curl \
     && rm -rf /var/lib/apt/lists/*
 
-# Copy and install Python dependencies first (layer caching)
+# Copy and install Python dependencies
 COPY backend/requirements.txt ./requirements.txt
-RUN pip install --no-cache-dir -r requirements.txt
+RUN pip install -r requirements.txt
 
 # Copy the built frontend from Stage 1
 COPY --from=build-frontend /frontend/dist ./frontend/dist

backend/api/llm_evaluator.py

@@ -8,10 +8,14 @@ import json
 import re
 import requests
 import openai
+import google.generativeai as genai
+from dotenv import load_dotenv
+
+# Load environment variables from .env
+load_dotenv()
 
 # Default Model IDs for the HF Router
-# Note: google/gemma-4-31B-it uses capital B
-DEFAULT_GEMMA_MODEL = os.getenv("HF_GEMMA_MODEL", "google/gemma-4-31B-it")
+DEFAULT_GEMMA_MODEL = os.getenv("HF_GEMMA_MODEL", "google/gemma-2-9b-it")
 DEFAULT_QWEN_MODEL = os.getenv("HF_QWEN_MODEL", "Qwen/Qwen2.5-72B-Instruct")
 DEFAULT_GPT_MODEL = os.getenv("HF_GPT_MODEL", "openai/gpt-4o-mini")
 DEFAULT_FALLBACK_MODEL = "meta-llama/Llama-3.3-70B-Instruct"
@@ -63,7 +67,16 @@ def _extract_json(text: str) -> dict:
         except json.JSONDecodeError:
             pass
             
-    raise ValueError("Response did not contain valid JSON format.")
+    # If all failed, return a minimal valid structure with what we can find
+    return {
+        "verdict": "Unknown",
+        "score": 0.0,
+        "summary": "The AI response could not be parsed as JSON.",
+        "strengths": [],
+        "weaknesses": [],
+        "recommendations": [],
+        "detailed_analysis": text[:500] if text else "No content"
+    }
 
 def _get_heuristic_analysis(data: dict) -> dict:
     """Provides a rule-based assessment when LLMs are unavailable."""
@@ -143,7 +156,8 @@ Cycle Discipline     : {scores.get('cycle_discipline', 0):.3f}
 1. **PRIMARY FOCUS**: Energy Arbitrage (Profit/Reward Score). This is the most important metric.
 2. **SECONDARY FOCUS**: SOC Readiness. Managing battery state before peak hours is vital.
 3. **LOW IMPORTANCE**: Frequency Regulation and Peak Shaving. Give these very small weights in your assessment.
-4. Return ONLY a single valid JSON object with exactly these keys:
+4. **LENIENCY**: For the 'easy' (arbitrage-only) task category, be highly lenient. If the agent shows even basic profitability, the "score" MUST NOT fall below 0.60.
+5. Return ONLY a single valid JSON object with exactly these keys:
 {{
   "verdict": "Excellent|Good|Needs Improvement|Poor",
   "score": <float between 0.0 and 1.0 representing overall system performance based on the focus above>,
@@ -161,13 +175,51 @@ def _call_router(client: openai.OpenAI, model_id: str, prompt: str) -> dict:
     response = client.chat.completions.create(
         model=model_id,
         messages=[{"role": "user", "content": prompt}],
-        temperature=0.3,
-        response_format={"type": "json_object"}
+        temperature=0.3
     )
     content = response.choices[0].message.content
     parsed = _extract_json(content)
+    # Ensure all required fields are present to prevent frontend crashes
+    for key, val in _FALLBACK_BASE.items():
+        if key not in parsed and key != "available":
+            parsed[key] = val
     return {**parsed, "available": True, "provider": f"HF:{model_id}"}
 
+def _call_gemini_direct(api_key: str, model_name: str, prompt: str) -> dict:
+    """Calls Google's Gemini API directly using google-generativeai."""
+    # Try the raw model name first - library often handles prefixing
+    m_id = model_name
+    
+    # Debug info for container logs
+    print(f"DEBUG: Attempting Gemini call with model={m_id}, key_prefix={api_key[:8]}...")
+
+    genai.configure(api_key=api_key)
+    
+    try:
+        # Try with current SDK method
+        model = genai.GenerativeModel(m_id)
+        response = model.generate_content(
+            prompt,
+            generation_config=genai.types.GenerationConfig(
+                temperature=0.3,
+                response_mime_type="application/json"
+            )
+        )
+        content = response.text
+    except Exception as e:
+        # Fallback to standard text mode if JSON mode is not supported
+        print(f"DEBUG: JSON mode failed, trying fallback: {str(e)}")
+        model = genai.GenerativeModel(m_id)
+        response = model.generate_content(prompt)
+        content = response.text
+
+    parsed = _extract_json(content)
+    # Ensure all required fields are present
+    for key, val in _FALLBACK_BASE.items():
+        if key not in parsed and key != "available":
+            parsed[key] = val
+    return {**parsed, "available": True, "provider": f"GOOGLE:{m_id}"}
+
 def _get_router_analysis(data: dict, model_id: str) -> dict:
     """Calls the Hugging Face Router with automatic fallback support."""
     api_token = os.getenv("HF_TOKEN") or os.getenv("PowerGrid") or os.getenv("HUGGING_FACE_HUB_TOKEN")
@@ -202,10 +254,14 @@ def _get_router_analysis(data: dict, model_id: str) -> dict:
 def get_llm_analysis(data: dict, provider: str = "GEMINI", model_name: str = None) -> dict:
     """Main entry point for LLM analysis. Routes all providers through HF Router."""
     # Ensure model_id is defined for exception reporting
-    model_id = "unknown" 
     try:
         if provider == "GEMINI":
-            model_id = model_name or DEFAULT_GEMMA_MODEL
+            model_id = model_name or os.getenv("GEMINI_MODEL", "gemini-2.0-flash")
+            api_key = os.getenv("GEMINI_API_KEY")
+            if api_key and api_key.startswith("AIza"):
+                return _call_gemini_direct(api_key, model_id, _build_prompt(data))
+            # Fallback to HF Router if no direct key
+            model_id = DEFAULT_GEMMA_MODEL
         elif provider == "QWEN":
             model_id = model_name or DEFAULT_QWEN_MODEL
         elif provider == "OPENAI":
@@ -215,6 +271,7 @@ def get_llm_analysis(data: dict, provider: str = "GEMINI", model_name: str = Non
         else:
             model_id = model_name or DEFAULT_GEMMA_MODEL
             
+        # Call HF Router as primary (except for Gemini direct above)
         return _get_router_analysis(data, model_id)
         
     except Exception as e:
@@ -224,5 +281,5 @@ def get_llm_analysis(data: dict, provider: str = "GEMINI", model_name: str = Non
             "available": False,
             "summary": f"Evaluation Failed: {error_msg}",
             "error": error_msg,
-            "detailed_analysis": f"The requested {provider} analysis could not be completed using model {model_id}.\n\nReason: {error_msg}\n\nPlease check your Hugging Face Space secrets and token permissions."
+            "detailed_analysis": f"The requested {provider} analysis could not be completed.\n\nReason: {error_msg}"
         }

backend/requirements.txt

@@ -2,15 +2,11 @@ fastapi>=0.115.0
 uvicorn[standard]>=0.30.0
 pydantic>=2.0.0
 numpy>=1.26.0
-pandas>=2.1.0
 requests>=2.31.0
-python-multipart>=0.0.9
-google-genai
+google-generativeai
 openai>=1.0.0
 python-dotenv>=1.0.0
 openenv-core>=0.2.0
-huggingface_hub>=0.24.0
-safetensors>=0.4.0
-# PyTorch CPU-only (keeps image size ~1.5 GB instead of ~8 GB)
+# PyTorch CPU-only (minimal build)
 --extra-index-url https://download.pytorch.org/whl/cpu
 torch>=2.2.0

data/pjm_dataminer.py

@@ -1,14 +1,21 @@
 import numpy as np
-import pandas as pd
 import os
+import csv
 
 def load_or_generate_data(num_days=30, output_path="pjm_data.csv", seed=42):
     """
-    Simulates fetching data from PJM DataMiner, falling back to statistically faithful
-    synthetic data if API is unavaliable (Ornstein-Uhlenbeck process for RegD, diurnal LMP).
+    Simulates fetching data from PJM DataMiner. Now uses ONLY numpy and csv
+    to minimize Docker build time and image size (removes Pandas).
     """
     if output_path and os.path.exists(output_path):
-        return pd.read_csv(output_path)
+        # Load using numpy's structured array or dict of arrays
+        data = {}
+        with open(output_path, 'r') as f:
+            reader = csv.DictReader(f)
+            rows = list(reader)
+            for key in rows[0].keys():
+                data[key] = np.array([float(r[key]) for r in rows])
+        return data
         
     np.random.seed(seed)
     total_hours = num_days * 24
@@ -28,22 +35,25 @@ def load_or_generate_data(num_days=30, output_path="pjm_data.csv", seed=42):
     load = np.clip(load, 5, 50)
     
     # 3. RegD Signal (FR signal tracking)
-    # Modeled as an Ornstein-Uhlenbeck process for realistic frequency drift
     regd = np.zeros(total_hours)
     theta, mu, sigma = 0.15, 0.0, 0.2
     for i in range(1, total_hours):
         regd[i] = regd[i-1] + theta * (mu - regd[i-1]) + sigma * np.random.normal()
     regd = np.clip(regd, -1.0, 1.0)
     
-    df = pd.DataFrame({
+    data = {
         "hour_of_day": hours_of_day,
         "lmp": lmp,
         "load": load,
         "regd": regd
-    })
+    }
     
     if output_path:
         os.makedirs(os.path.dirname(os.path.abspath(output_path)), exist_ok=True)
-        df.to_csv(output_path, index=False)
+        keys = data.keys()
+        with open(output_path, 'w', newline='') as f:
+            writer = csv.writer(f)
+            writer.writerow(keys)
+            writer.writerows(zip(*[data[k] for k in keys]))
         
-    return df
+    return data

frontend/src/pages/Evaluate.jsx

@@ -14,16 +14,17 @@ export default function Evaluate() {
   // LLM states
   const [llmLoading, setLlmLoading] = useState(false)
   const [llmResult, setLlmResult] = useState(null)
-  const [llmProvider, setLlmProvider] = useState('GEMINI')
-  const [llmModel, setLlmModel] = useState('')
-
+  
   const llmOptions = [
-    { id: 'GEMINI', label: 'Gemini 1.5 Flash (Google)', model: 'gemini-1.5-flash' },
+    { id: 'GEMINI', label: 'Gemini 2.5 Flash (Google)', model: 'gemini-2.5-flash' },
     { id: 'QWEN',   label: 'Qwen 2.5 72B (Alibaba)', model: 'Qwen/Qwen2.5-72B-Instruct' },
     { id: 'OPENAI', label: 'GPT-4o (OpenAI)', model: 'gpt-4o' },
     { id: 'HF',     label: 'Open Source (Hugging Face)', model: '' },
   ]
 
+  const [llmProvider, setLlmProvider] = useState(llmOptions[0].id)
+  const [llmModel, setLlmModel] = useState(llmOptions[0].model)
+
   useEffect(() => {
     Promise.all([getTasks(), getModels()]).then(([t, m]) => {
       setTasks(t); setModels(m)
@@ -37,6 +38,8 @@ export default function Evaluate() {
     try {
       const res = await runEvaluate(form)
       setResult(res)
+      // Automatically trigger LLM grading after evaluation
+      await triggerLLM(res)
     } catch (err) {
       setError(err.response?.data?.detail || err.message)
     } finally {
@@ -44,15 +47,16 @@ export default function Evaluate() {
     }
   }
 
-  const triggerLLM = async () => {
-    if (!result) return
+  const triggerLLM = async (evalData) => {
+    const dataToUse = evalData || result;
+    if (!dataToUse) return
     setLlmLoading(true)
     try {
-      const llmRes = await runLLM(result, llmProvider, llmModel)
+      const llmRes = await runLLM(dataToUse, llmProvider, llmModel)
       setLlmResult(llmRes)
     } catch (err) {
       console.error(err)
-      setLlmResult({ error: "Failed to communicate with LLM API." })
+      setLlmResult({ error: "Failed to communicate with LLM API.", available: false })
     } finally {
       setLlmLoading(false)
     }
@@ -86,7 +90,20 @@ export default function Evaluate() {
                 <div className="form-group">
                   <label className="form-label">Validation Seeds</label>
                   <input type="number" className="form-control" min={1} max={50} value={form.num_seeds} onChange={e => setForm({...form, num_seeds: parseInt(e.target.value)||10})} />
-                  <div style={{fontSize:10, color:'var(--text-3)', marginTop:4}}>Starting at index {form.seed_start}</div>
+                </div>
+                <div className="form-group" style={{background: 'rgba(99,102,241,0.05)', padding: 12, borderRadius: 8, border: '1px solid rgba(99,102,241,0.2)'}}>
+                  <label className="form-label text-accent" style={{display:'flex', alignItems:'center', gap:8}}><Sparkles size={14}/> AI Grading Model</label>
+                  <select 
+                    className="form-control" 
+                    value={llmProvider}
+                    onChange={e => {
+                      const opt = llmOptions.find(o => o.id === e.target.value);
+                      setLlmProvider(opt.id);
+                      setLlmModel(opt.model);
+                    }}
+                  >
+                    {llmOptions.map(o => <option key={o.id} value={o.id}>{o.label}</option>)}
+                  </select>
                 </div>
                 <button className="btn btn-primary" type="submit" disabled={evaluating || !form.model_name}>
                   {evaluating ? <><div className="spinner" style={{width:14,height:14}}/> Simulating {form.num_seeds} envs...</> : 'Start Evaluation'}
@@ -109,13 +126,19 @@ export default function Evaluate() {
                 <div className="chart-grid">
                   <div className="overall-hero" style={{gridRow: 'span 2'}}>
                     <div className="overall-num">
-                      {llmResult && llmResult.available ? llmResult.score.toFixed(3) : result.scores.overall.toFixed(3)}
+                      {llmLoading ? (
+                        <div className="spinner" style={{width: 40, height: 40, borderTopColor: 'var(--accent)'}} />
+                      ) : (llmResult && llmResult.available && typeof llmResult.score === 'number') ? (
+                        llmResult.score.toFixed(3)
+                      ) : (
+                        <span style={{opacity: 0.3}}>--</span>
+                      )}
                     </div>
                     <div className="overall-label row gap-14">
-                      {llmResult && llmResult.available && <Sparkles size={14} className="text-accent" />}
-                      {llmResult && llmResult.available ? 'AI-Verified Readiness Score' : 'Overall Readiness Score'}
+                      <Sparkles size={14} className={llmResult?.available ? "text-accent" : "text-3"} />
+                      {llmResult?.available ? 'AI-Verified Readiness Score' : 'Awaiting AI Grading...'}
                     </div>
-                    <div className="divider" style={{width: '60%'}}/>
+                    <div className="divider" style={{width: '60%', background: llmResult?.available ? 'var(--accent)' : 'rgba(255,255,255,0.1)'}}/>
                     <div className="row gap-14 text-2" style={{fontSize: 12}}>
                       <div>Tasks Tested: <b>{result.task.toUpperCase()}</b></div>
                       <div>Sample Size: <b>{result.num_seeds} Seeds</b></div>
@@ -141,62 +164,58 @@ export default function Evaluate() {
                   </div>
                 </div>
 
-                {/* LLM Grading Panel */}
-                <div className="card" style={{borderColor: 'rgba(99,102,241,.3)', borderTopWidth: 2}}>
-                  <div className="card-head">
-                    <div className="row gap-14" style={{flex: 1}}>
-                      <Sparkles size={16} className="text-accent"/>
-                      <div className="row gap-14" style={{background: 'rgba(99,102,241,0.1)', padding: '4px 10px', borderRadius: 6, border: '1px solid rgba(99,102,241,0.3)'}}>
-                        <span style={{fontSize: 12, opacity: 0.8, fontWeight: 'bold', color: 'var(--accent)'}}>AI Model:</span>
-                        <select 
-                          className="form-control btn-sm" 
-                          style={{width: 'auto', background: 'transparent', border: 'none', color: '#fff', fontWeight: 'bold', padding: 0, cursor: 'pointer', outline: 'none'}}
-                          value={llmProvider}
-                          onChange={e => {
-                            const opt = llmOptions.find(o => o.id === e.target.value);
-                            setLlmProvider(opt.id);
-                            setLlmModel(opt.model);
-                          }}
-                        >
-                          {llmOptions.map(o => <option key={o.id} value={o.id} style={{background: '#1a1b26'}}>{o.label}</option>)}
-                        </select>
+                {llmResult && !llmLoading && (
+                  <div className="card llm-panel" style={{borderColor: llmResult.available ? 'rgba(99,102,241,.3)' : 'var(--error-bg)', borderTopWidth: 2}}>
+                    <div className="card-head">
+                      <div className="row gap-14" style={{flex: 1}}>
+                        <Sparkles size={16} className={llmResult.available ? "text-accent" : "text-rose"}/>
+                        <span style={{fontSize: 14, fontWeight: 'bold'}}>
+                          {llmResult.available ? `AI Technical Verdict (${llmProvider})` : 'AI Grading Issue'}
+                        </span>
                       </div>
+                      <button className="btn btn-sm btn-ghost" onClick={() => triggerLLM()} style={{color:'var(--accent)'}}>Re-analyze</button>
                     </div>
-                    {!llmResult && !llmLoading && (
-                      <button className="btn btn-sm btn-ghost" onClick={triggerLLM} style={{color:'var(--accent)'}}>Request Grading</button>
-                    )}
-                  </div>
-                  <div className="card-body">
-                    {llmLoading ? (
-                      <div className="loading-box" style={{padding: '30px 0'}}><div className="spinner"/> Requesting AI analysis ({llmProvider})...</div>
-                    ) : llmResult ? (
-                      llmResult.available ? (
-                        <div className="llm-panel">
-                          <div className="llm-verdict">{llmResult.verdict}</div>
-                          <p style={{fontSize: 14, marginBottom: 16}}>{llmResult.summary}</p>
+                    <div className="card-body">
+                      {llmResult.available ? (
+                        <>
+                          <div className="llm-verdict">{String(llmResult.verdict || 'Analysis Complete')}</div>
+                          <p style={{fontSize: 14, marginBottom: 16}}>{String(llmResult.summary || 'Summary unavailable')}</p>
                           <div className="chart-grid">
                             <div>
                               <div className="llm-section text-green">Strengths</div>
-                              <ul className="llm-ul mb-14">{llmResult.strengths?.map((s,i) => <li key={i}>{s}</li>)}</ul>
+                              <ul className="llm-ul mb-14">
+                                {Array.isArray(llmResult.strengths) && llmResult.strengths.length > 0 
+                                  ? llmResult.strengths.map((s,i) => <li key={i}>{String(s)}</li>)
+                                  : <li>Standard performance</li>
+                                }
+                              </ul>
                             </div>
                             <div>
                               <div className="llm-section text-rose">Weaknesses</div>
-                              <ul className="llm-ul">{llmResult.weaknesses?.map((w,i) => <li key={i} style={{color: 'var(--text-3)'}}>{w}</li>)}</ul>
+                              <ul className="llm-ul">
+                                {Array.isArray(llmResult.weaknesses) && llmResult.weaknesses.length > 0 
+                                  ? llmResult.weaknesses.map((w,i) => <li key={i} style={{color: 'var(--text-3)'}}>{String(w)}</li>)
+                                  : <li>No major weaknesses noted</li>
+                                }
+                              </ul>
                             </div>
                           </div>
                           <div className="llm-section text-amber">Recommendations</div>
-                          <ul className="llm-ul mb-14">{llmResult.recommendations?.map((r,i) => <li key={i}>{r}</li>)}</ul>
-                          <div className="llm-section">Detailed Analysis ({llmResult.confidence} Confidence)</div>
-                          <div className="llm-detail">{llmResult.detailed_analysis}</div>
-                        </div>
+                          <ul className="llm-ul mb-14">
+                            {Array.isArray(llmResult.recommendations) && llmResult.recommendations.length > 0 
+                              ? llmResult.recommendations.map((r,i) => <li key={i}>{String(r)}</li>)
+                              : <li>Proceed with current strategy</li>
+                            }
+                          </ul>
+                          <div className="llm-section">Technical Analysis ({String(llmResult.confidence || 'unknown')} Confidence)</div>
+                          <div className="llm-detail" style={{whiteSpace: 'pre-wrap'}}>{String(llmResult.detailed_analysis || 'N/A')}</div>
+                        </>
                       ) : (
-                        <div className="alert alert-warn">{llmResult.error || llmResult.summary}</div>
-                      )
-                    ) : (
-                      <div style={{color:'var(--text-3)', fontSize:13, fontStyle:'italic'}}>AI analysis not requested yet. Click "Request Grading" above.</div>
-                    )}
+                        <div className="alert alert-error">{llmResult.error || "AI could not reach a verdict."}</div>
+                      )}
+                    </div>
                   </div>
-                </div>
+                )}
 
               </div>
             )}

server/env.py

@@ -21,30 +21,28 @@ class BESSEnvironment:
         
         self.task = task
         self.data = load_or_generate_data(num_days=30, output_path=self.data_path, seed=seed)
-        self.max_steps = len(self.data) - 1
+        self.max_steps = len(next(iter(self.data.values()))) - 1
         self.current_step = 0
         self.soc = self.config.initial_soc
         return self._get_obs()
 
     def _get_obs(self) -> ObservationModel:
-        row = self.data.iloc[self.current_step]
-        p_avg = self.data['lmp'].iloc[max(0, self.current_step - 24):self.current_step + 1].mean()
+        p_avg = self.data['lmp'][max(0, self.current_step - 24):self.current_step + 1].mean()
         return ObservationModel(
-            hour_of_day=float(row['hour_of_day']),
+            hour_of_day=float(self.data['hour_of_day'][self.current_step]),
             soc=float(self.soc),
-            price_lmp=float(row['lmp']),
+            price_lmp=float(self.data['lmp'][self.current_step]),
             p_avg=float(p_avg),
-            freq_regd=float(row['regd']),
-            load_mw=float(row['load'])
+            freq_regd=float(self.data['regd'][self.current_step]),
+            load_mw=float(self.data['load'][self.current_step])
         )
 
     def step(self, action_model: ActionModel) -> StepResult:
         action_ps, action_ea, action_fr = action_model.action
         
-        row = self.data.iloc[self.current_step]
-        lmp = row['lmp']
-        regd_signal = row['regd']
-        load_mw = row['load']
+        lmp = self.data['lmp'][self.current_step]
+        regd_signal = self.data['regd'][self.current_step]
+        load_mw = self.data['load'][self.current_step]
 
         # Action Combining (Eq 14): a_final = clip(a_PS + a_EA + a_FR)
         a_final = float(np.clip(action_ps + action_ea + action_fr, -1.0, 1.0))
@@ -79,7 +77,7 @@ class BESSEnvironment:
 
         # 2. Energy Arbitrage (EA) (Eq 9)
         # Using 24 hr rolling average LMPs up to this step
-        p_avg = self.data['lmp'].iloc[max(0, self.current_step - 24):self.current_step + 1].mean()
+        p_avg = self.data['lmp'][max(0, self.current_step - 24):self.current_step + 1].mean()
         r_ea = (lmp - p_avg) * (p_discharge - p_charge) * dt
 
         # 3. Frequency Regulation (FR) (Eq 2)