Update: Remove damage model and add Docker configuration

.gitignore

@@ -0,0 +1,39 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+ENV/
+
+# IDEs
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Project specific
+tmp/
+.env
+*.log
+
+# Downloaded models (they will be downloaded automatically)
+/tmp/models/

Dockerfile

@@ -2,24 +2,42 @@ FROM python:3.10-slim
 
 WORKDIR /code
 
-# Install system dependencies
-RUN apt-get update && apt-get install -y \
+# Set environment variable to disable pip cache
+ENV PIP_NO_CACHE_DIR=1
+
+# Install system dependencies with cleanup
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
     build-essential \
     libgl1-mesa-glx \
     libglib2.0-0 \
-    && rm -rf /var/lib/apt/lists/*
+    git \
+    && apt-get clean && \
+    rm -rf /var/lib/apt/lists/* && \
+    rm -rf /tmp/* && \
+    mkdir -p /tmp && \
+    chmod 777 /tmp
 
-# Install Python dependencies
+# Copy requirements first
 COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
 
-# Copy app code
+# Install dependencies with improved error handling
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir setuptools wheel && \
+    pip install --no-cache-dir -r requirements.txt || (echo "pip install failed" && exit 1) && \
+    rm -rf ~/.cache/pip && \
+    rm -rf /tmp/* && \
+    mkdir -p /tmp && \
+    chmod 777 /tmp
+
+# Copy application code
 COPY . .
 
-# Expose port
-EXPOSE 7860
+# Create necessary directories with proper permissions
+RUN mkdir -p /tmp/models/parts/weights/weights && \
+    chmod -R 777 /tmp
 
-ENV PATH="/root/.local/bin:$PATH"
+EXPOSE 7860
 
-# Run FastAPI app with uvicorn
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]
+# Added --reload flag for development convenience
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860", "--reload"]

app/.gitignore

@@ -0,0 +1,4 @@
+.env
+*.env
+__pycache__/
+*.pyc

app/main.py

@@ -1,4 +1,3 @@
-
 # --- Model download logic (Hugging Face Hub) ---
 import os
 import requests
@@ -15,13 +14,13 @@ def download_if_missing(url, dest):
         print("Download complete.")
 
 # Hugging Face direct download links
-DAMAGE_MODEL_URL = "https://huggingface.co/AItoolstack/car_damage_detection/resolve/main/yolov8_models/damage/weights/weights/best.pt"
+# DAMAGE_MODEL_URL = "https://huggingface.co/AItoolstack/car_damage_detection/resolve/main/yolov8_models/damage/weights/weights/best.pt"
 PARTS_MODEL_URL = "https://huggingface.co/AItoolstack/car_damage_detection/resolve/main/yolov8_models/parts/weights/weights/best.pt"
 
-DAMAGE_MODEL_PATH = os.path.join("/tmp", "models", "damage", "weights", "weights", "best.pt")
+# DAMAGE_MODEL_PATH = os.path.join("/tmp", "models", "damage", "weights", "weights", "best.pt")
 PARTS_MODEL_PATH = os.path.join("/tmp", "models", "parts", "weights", "weights", "best.pt")
 
-download_if_missing(DAMAGE_MODEL_URL, DAMAGE_MODEL_PATH)
+# download_if_missing(DAMAGE_MODEL_URL, DAMAGE_MODEL_PATH)
 download_if_missing(PARTS_MODEL_URL, PARTS_MODEL_PATH)
 
 from fastapi import FastAPI, File, UploadFile, BackgroundTasks
@@ -35,6 +34,7 @@ import aiofiles
 from pathlib import Path
 import uuid
 from app.routers import inference
+from .utils.model_loader import PARTS_MODEL_PATH
 
 app = FastAPI(title="Car Damage Detection API")
 
@@ -54,5 +54,18 @@ templates = Jinja2Templates(directory="app/templates")
 # Include routers
 app.include_router(inference.router)
 
+# Create required directories
+UPLOAD_DIR = Path("tmp/uploads")
+RESULTS_DIR = Path("tmp/results")
+
+def create_dirs():
+    for dir_path in [UPLOAD_DIR, RESULTS_DIR]:
+        dir_path.mkdir(parents=True, exist_ok=True)
+
+# Add to startup
+@app.on_event("startup")
+async def startup_event():
+    create_dirs()
+
 if __name__ == "__main__":
     uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)

app/routers/inference.py

@@ -1,5 +1,4 @@
-
-from fastapi import APIRouter, Request, UploadFile, File, Form
+from fastapi import APIRouter, Request, UploadFile, File, Form, HTTPException
 from fastapi.responses import HTMLResponse, FileResponse, JSONResponse
 from fastapi.templating import Jinja2Templates
 from starlette.background import BackgroundTask
@@ -13,6 +12,7 @@ import base64
 from ultralytics import YOLO
 import cv2
 import numpy as np
+from ..utils.llm_client import GroqAnalyzer
 
 
 # Templates directory
@@ -30,12 +30,15 @@ os.makedirs(RESULTS_DIR, exist_ok=True)
 ALLOWED_EXTENSIONS = {"jpg", "jpeg", "png", "tiff", "tif"}
 
 # Model paths
-DAMAGE_MODEL_PATH = os.path.join("/tmp", "models", "damage", "weights", "weights", "best.pt")
+# DAMAGE_MODEL_PATH = os.path.join("/tmp", "models", "damage", "weights", "weights", "best.pt")  # Commented for now
 PARTS_MODEL_PATH = os.path.join("/tmp", "models", "parts", "weights", "weights", "best.pt")
 
 # Class names for parts
 PARTS_CLASS_NAMES = ['headlamp', 'front_bumper', 'hood', 'door', 'rear_bumper']
 
+# Initialize GroqAnalyzer
+groq_analyzer = GroqAnalyzer()
+
 # Helper: Run YOLO inference and return results
 def run_yolo_inference(model_path, image_path, task='segment'):
     model = YOLO(model_path)
@@ -72,12 +75,16 @@ def draw_masks_and_conf(image_path, yolo_result, class_names=None):
 # Helper: Generate JSON output
 def generate_json_output(filename, damage_result, parts_result):
     # Damage severity: use max confidence
-    severity_score = float(max([float(box.conf[0]) for box in damage_result.boxes], default=0))
-    damage_regions = []
-    for box in damage_result.boxes:
-        x1, y1, x2, y2 = map(float, box.xyxy[0])
-        conf = float(box.conf[0])
-        damage_regions.append({"bbox": [x1, y1, x2, y2], "confidence": conf})
+    if damage_result is not None and hasattr(damage_result, 'boxes'):
+        severity_score = float(max([float(box.conf[0]) for box in damage_result.boxes], default=0))
+        damage_regions = []
+        for box in damage_result.boxes:
+            x1, y1, x2, y2 = map(float, box.xyxy[0])
+            conf = float(box.conf[0])
+            damage_regions.append({"bbox": [x1, y1, x2, y2], "confidence": conf})
+    else:
+        severity_score = 0
+        damage_regions = []
     # Parts
     parts = []
     for i, box in enumerate(parts_result.boxes):
@@ -129,96 +136,112 @@ def login_page(request: Request):
     return templates.TemplateResponse("login.html", {"request": request})
 
 @router.post("/upload", response_class=HTMLResponse)
-def upload_image(request: Request, file: UploadFile = File(...)):
-    ext = file.filename.split(".")[-1].lower()
-    if ext not in ALLOWED_EXTENSIONS:
-        return templates.TemplateResponse("index.html", {"request": request, "error": "Unsupported file type."})
-
-    # Save uploaded file
-    session_id = str(uuid.uuid4())
-    upload_path = os.path.join(UPLOAD_DIR, f"{session_id}.{ext}")
-    with open(upload_path, "wb") as buffer:
-        shutil.copyfileobj(file.file, buffer)
+async def upload_image(request: Request, file: UploadFile = File(...)):
+    try:
+        ext = file.filename.split(".")[-1].lower()
+        print(f"[DEBUG] Uploaded file extension: {ext}")
+        if ext not in ALLOWED_EXTENSIONS:
+            print(f"[DEBUG] Unsupported file type: {ext}")
+            return templates.TemplateResponse("index.html", {"request": request, "error": "Unsupported file type."})
 
+        # Save uploaded file
+        session_id = str(uuid.uuid4())
+        upload_filename = f"{session_id}_{file.filename}"
+        upload_path = os.path.join(UPLOAD_DIR, upload_filename)
+        print(f"[DEBUG] Saving uploaded file to: {upload_path}")
+        with open(upload_path, "wb") as buffer:
+            shutil.copyfileobj(file.file, buffer)
+        print(f"[DEBUG] File saved. Running inference...")
 
-    # Run both inferences
-    try:
-        damage_result = run_yolo_inference(DAMAGE_MODEL_PATH, upload_path)
-        parts_result = run_yolo_inference(PARTS_MODEL_PATH, upload_path)
-
-        # Save annotated images
-        damage_img_path = os.path.join(RESULTS_DIR, f"{session_id}_damage.png")
-        parts_img_path = os.path.join(RESULTS_DIR, f"{session_id}_parts.png")
-        json_path = os.path.join(RESULTS_DIR, f"{session_id}_result.json")
-        # Use custom download endpoints for Hugging Face Spaces
-        damage_img_url = f"/download/result/{session_id}_damage.png"
-        parts_img_url = f"/download/result/{session_id}_parts.png"
-        json_url = f"/download/result/{session_id}_result.json"
-
-        # Defensive: set to None by default
-        damage_img = None
-        parts_img = None
-        json_output = None
-
-        # Only save and set if inference returns boxes
-        if hasattr(damage_result, 'boxes') and len(damage_result.boxes) > 0:
-            damage_img = draw_masks_and_conf(upload_path, damage_result)
-            cv2.imwrite(damage_img_path, damage_img)
-        if hasattr(parts_result, 'boxes') and len(parts_result.boxes) > 0:
-            parts_img = draw_masks_and_conf(upload_path, parts_result, class_names=PARTS_CLASS_NAMES)
-            cv2.imwrite(parts_img_path, parts_img)
-        if (hasattr(damage_result, 'boxes') and len(damage_result.boxes) > 0) or (hasattr(parts_result, 'boxes') and len(parts_result.boxes) > 0):
-            json_output = generate_json_output(file.filename, damage_result, parts_result)
-            with open(json_path, "w") as jf:
-                json.dump(json_output, jf, indent=2)
-
-        # Prepare URLs for download (only if files exist)
-        result = {
-            "filename": file.filename,
-            "damage_image": damage_img_url if damage_img is not None else None,
-            "parts_image": parts_img_url if parts_img is not None else None,
-            "json": json_output,
-            "json_download": json_url if json_output is not None else None
-        }
-        # Debug log
-        print("[DEBUG] Result dict:", result)
-    except Exception as e:
-        result = {
-            "filename": file.filename,
-            "error": f"Inference failed: {str(e)}",
-            "damage_image": None,
-            "parts_image": None,
-            "json": None,
-            "json_download": None
-        }
-        print("[ERROR] Inference failed:", e)
-
-    import threading
-    import time
-    def delayed_cleanup():
-        time.sleep(300)  # 5 minutes
+        warning = None
         try:
-            os.remove(upload_path)
-        except Exception:
-            pass
-        for suffix in ["_damage.png", "_parts.png", "_result.json"]:
+            damage_result = None  # Not used
+            parts_result = run_yolo_inference(PARTS_MODEL_PATH, upload_path)
+            print(f"[DEBUG] YOLO inference result: {parts_result}")
+
+            parts_img = None
+            json_output = None
+            parts_img_url = None
+            json_url = None
+
+            if hasattr(parts_result, 'boxes') and len(parts_result.boxes) > 0:
+                print(f"[DEBUG] Detected {len(parts_result.boxes)} parts.")
+                parts_img = draw_masks_and_conf(upload_path, parts_result, class_names=PARTS_CLASS_NAMES)
+                parts_img_filename = f"{session_id}_parts.png"
+                parts_img_path = os.path.join(RESULTS_DIR, parts_img_filename)
+                cv2.imwrite(parts_img_path, parts_img)
+                print(f"[DEBUG] Parts image saved to: {parts_img_path}")
+                parts_img_url = f"/download/result/{parts_img_filename}"
+
+                json_output = generate_json_output(file.filename, damage_result, parts_result)
+                json_filename = f"{session_id}_result.json"
+                json_path = os.path.join(RESULTS_DIR, json_filename)
+                with open(json_path, "w") as jf:
+                    json.dump(json_output, jf, indent=2)
+                print(f"[DEBUG] JSON output saved to: {json_path}")
+                json_url = f"/download/result/{json_filename}"
+            else:
+                warning = "No parts detected in the image."
+                print("[DEBUG] No parts detected.")
+
+            llm_analysis = groq_analyzer.analyze_damage(upload_path)
+            print(f"[DEBUG] LLM analysis output: {llm_analysis}")
+
+            result = {
+                "filename": file.filename,
+                "parts_image": parts_img_url,
+                "json": json_output,
+                "json_download": json_url,
+                "llm_analysis": llm_analysis,
+                "warning": warning
+            }
+            print("[DEBUG] Result dict:", result)
+        except Exception as e:
+            result = {
+                "filename": file.filename,
+                "error": f"Inference failed: {str(e)}",
+                "parts_image": None,
+                "json": None,
+                "json_download": None,
+                "llm_analysis": None,
+                "warning": None
+            }
+            print("[ERROR] Inference failed:", e)
+
+        import threading
+        import time
+        def delayed_cleanup():
+            time.sleep(300)  # 5 minutes
             try:
-                os.remove(os.path.join(RESULTS_DIR, f"{session_id}{suffix}"))
-            except Exception:
-                pass
-
-    threading.Thread(target=delayed_cleanup, daemon=True).start()
-
-    return templates.TemplateResponse(
-        "index.html",
-        {
-            "request": request,
-            "result": result,
-            "original_image": f"/download/upload/{session_id}.{ext}"
-        }
-    )
-
-# --- Add routes to serve files from /tmp/uploads and /tmp/results ---
+                os.remove(upload_path)
+                print(f"[DEBUG] Cleaned up upload: {upload_path}")
+            except Exception as ce:
+                print(f"[DEBUG] Cleanup error (upload): {ce}")
+            for suffix in ["_parts.png", "_result.json"]:
+                try:
+                    os.remove(os.path.join(RESULTS_DIR, f"{session_id}{suffix}"))
+                    print(f"[DEBUG] Cleaned up result: {os.path.join(RESULTS_DIR, f'{session_id}{suffix}')}" )
+                except Exception as ce:
+                    print(f"[DEBUG] Cleanup error (result): {ce}")
+
+        threading.Thread(target=delayed_cleanup, daemon=True).start()
+
+        return templates.TemplateResponse(
+            "index.html",
+            {
+                "request": request,
+                "result": result,
+                "original_image": f"/download/upload/{upload_filename}"
+            }
+        )
+    except Exception as e:
+        print(f"[ERROR] Inference failed: {str(e)}")
+        return templates.TemplateResponse(
+            "index.html",
+            {"request": request, "error": f"Error processing image: {str(e)}"}
+        )
+
+# --- Serve files from /tmp/uploads and /tmp/results ---
 @router.get("/download/upload/{filename}")
 def download_uploaded_file(filename: str):
     file_path = os.path.join(UPLOAD_DIR, filename)

app/static/results/4ea85461-64cd-4c8b-8ef9-3c9635df076b_result.json

@@ -1,86 +0,0 @@
-{
-  "filename": "28.jpg",
-  "damage": {
-    "severity_score": 0.7707259058952332,
-    "regions": [
-      {
-        "bbox": [
-          522.2720947265625,
-          530.5602416992188,
-          728.9972534179688,
-          892.2079467773438
-        ],
-        "confidence": 0.7707259058952332
-      },
-      {
-        "bbox": [
-          660.1974487304688,
-          512.72119140625,
-          927.4169921875,
-          883.8523559570312
-        ],
-        "confidence": 0.6494596600532532
-      },
-      {
-        "bbox": [
-          196.1370086669922,
-          277.27374267578125,
-          366.7127990722656,
-          712.44775390625
-        ],
-        "confidence": 0.32863757014274597
-      }
-    ]
-  },
-  "parts": [
-    {
-      "part": "hood",
-      "damaged": true,
-      "confidence": 0.9392628073692322,
-      "damage_percentage": 0.20169149219339333,
-      "bbox": [
-        411.4404296875,
-        342.9278869628906,
-        952.9636840820312,
-        578.835693359375
-      ]
-    },
-    {
-      "part": "headlamp",
-      "damaged": true,
-      "confidence": 0.9351847171783447,
-      "damage_percentage": 1.7647002993974346,
-      "bbox": [
-        531.9706420898438,
-        553.8896484375,
-        706.9000854492188,
-        656.3943481445312
-      ]
-    },
-    {
-      "part": "door",
-      "damaged": true,
-      "confidence": 0.9011728763580322,
-      "damage_percentage": 0.29901854346557094,
-      "bbox": [
-        143.66470336914062,
-        224.3675537109375,
-        391.3798522949219,
-        737.7504272460938
-      ]
-    },
-    {
-      "part": "front_bumper",
-      "damaged": true,
-      "confidence": 0.8590587377548218,
-      "damage_percentage": 0.03409931158993679,
-      "bbox": [
-        461.2880859375,
-        534.1759033203125,
-        978.0007934570312,
-        874.1959838867188
-      ]
-    }
-  ],
-  "cost_estimate": null
-}

app/static/results/9be81a29-214a-48af-ba99-8cab54a56d66_result.json

@@ -1,83 +0,0 @@
-{
-  "filename": "72.jpg",
-  "damage": {
-    "severity_score": 0.6467820405960083,
-    "regions": [
-      {
-        "bbox": [
-          20.340744018554688,
-          93.4926986694336,
-          112.2962646484375,
-          271.3760986328125
-        ],
-        "confidence": 0.6467820405960083
-      },
-      {
-        "bbox": [
-          129.6874237060547,
-          107.35161590576172,
-          238.290771484375,
-          377.91192626953125
-        ],
-        "confidence": 0.43937909603118896
-      },
-      {
-        "bbox": [
-          127.2110595703125,
-          23.03559684753418,
-          353.25946044921875,
-          420.0022888183594
-        ],
-        "confidence": 0.3795366883277893
-      },
-      {
-        "bbox": [
-          126.8061752319336,
-          38.61054611206055,
-          242.341064453125,
-          433.29034423828125
-        ],
-        "confidence": 0.3542208969593048
-      }
-    ]
-  },
-  "parts": [
-    {
-      "part": "hood",
-      "damaged": true,
-      "confidence": 0.746218204498291,
-      "damage_percentage": 0.2284878057871993,
-      "bbox": [
-        1.409912109375,
-        666.6517944335938,
-        200.2979278564453,
-        1020.8939819335938
-      ]
-    },
-    {
-      "part": "hood",
-      "damaged": true,
-      "confidence": 0.5614964962005615,
-      "damage_percentage": 0.04930226900457474,
-      "bbox": [
-        265.2085876464844,
-        582.6806030273438,
-        1017.8049926757812,
-        1016.5338745117188
-      ]
-    },
-    {
-      "part": "hood",
-      "damaged": true,
-      "confidence": 0.5332340598106384,
-      "damage_percentage": 0.25070920790460405,
-      "bbox": [
-        0.0,
-        501.2841796875,
-        358.5841369628906,
-        680.34912109375
-      ]
-    }
-  ],
-  "cost_estimate": null
-}

app/templates/index.html

@@ -23,7 +23,249 @@
             btn.innerText = 'Show JSON';
         }
     }
+
+    // Add new toggle function for LLM analysis
+    function toggleLLM() {
+        const block = document.getElementById('llm-block');
+        const btn = document.getElementById('toggle-llm-btn');
+        if (block.style.display === 'none') {
+            block.style.display = 'block';
+            btn.innerText = 'Hide Analysis';
+        } else {
+            block.style.display = 'none';
+            btn.innerText = 'Show Analysis';
+        }
+    }
+
+    function toggleBlock(blockId, btnId) {
+        const block = document.getElementById(blockId);
+        const btn = document.getElementById(btnId);
+        if (block.style.display === 'none' || !block.style.display) {
+            block.style.display = 'block';
+            btn.innerText = `Hide ${btnId.includes('json') ? 'JSON' : 'Analysis'}`;
+        } else {
+            block.style.display = 'none';
+            btn.innerText = `Show ${btnId.includes('json') ? 'JSON' : 'Analysis'}`;
+        }
+    }
     </script>
+    <style>
+    /* Add new LLM analysis styles */
+    .llm-section {
+        margin-top: 20px;
+        padding: 15px;
+        background: #f8f9fa;
+        border-radius: 8px;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+    }
+
+    .llm-content {
+        width: 100%;
+        max-width: 800px;
+        margin: 10px auto;
+        display: none;
+    }
+
+    .llm-content pre {
+        white-space: pre-wrap;
+        word-wrap: break-word;
+        background: white;
+        padding: 15px;
+        border-radius: 4px;
+        border: 1px solid #dee2e6;
+    }
+
+    .toggle-llm-btn {
+        background-color: #007bff;
+        color: white;
+        border: none;
+        padding: 8px 16px;
+        border-radius: 4px;
+        cursor: pointer;
+        margin: 10px 0;
+    }
+
+    .toggle-llm-btn:hover {
+        background-color: #0056b3;
+    }
+
+    .warning {
+        background-color: #fff3cd;
+        color: #856404;
+        padding: 1rem;
+        margin: 1rem 0;
+        border: 1px solid #ffeeba;
+        border-radius: 4px;
+    }
+    .llm-analysis {
+        margin-top: 2rem;
+        background: white;  /* White background */
+        border-radius: 12px;
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.2);
+        overflow: hidden;
+        transition: all 0.3s ease;
+    }
+
+    .analysis-header {
+        display: flex;
+        justify-content: space-between;
+        align-items: center;
+        padding: 1.5rem;
+        background: linear-gradient(135deg, #805ad5 0%, #6b46c1 100%);  /* Keep purple gradient header */
+        color: white;
+    }
+
+    .analysis-header h3 {
+        margin: 0;
+        font-size: 1.5rem;
+        display: flex;
+        align-items: center;
+        gap: 10px;
+        color: white;
+    }
+
+    .toggle-btn {
+        display: flex;
+        align-items: center;
+        gap: 8px;
+        background: rgba(255, 255, 255, 0.15);
+        color: white;
+        border: 1px solid rgba(255, 255, 255, 0.2);
+        padding: 8px 16px;
+        border-radius: 6px;
+        cursor: pointer;
+        transition: all 0.2s ease;
+    }
+
+    .toggle-btn:hover {
+        background: rgba(255, 255, 255, 0.25);
+    }
+
+    .toggle-btn i {
+        transition: transform 0.3s ease;
+    }
+
+    .toggle-btn.active i {
+        transform: rotate(180deg);
+    }
+
+    .analysis-content {
+        padding: 1.5rem;
+        background: white;
+        color: #2d3748;  /* Dark gray text */
+    }
+
+    .damage-grid {
+        display: grid;
+        gap: 1.5rem;
+        grid-template-columns: 1fr;
+    }
+
+    @media (min-width: 1024px) {
+        .damage-grid {
+            grid-template-columns: 3fr 2fr;
+        }
+    }
+
+    .table-container {
+        overflow-x: auto;
+        background: white;
+        border-radius: 8px;
+        box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
+    }
+
+    .damage-table {
+        width: 100%;
+        border-collapse: collapse;
+        margin: 0;
+        font-size: 0.95rem;
+        color: #2d3748;
+    }
+
+    .damage-table th, .damage-table td {
+        padding: 12px 16px;
+        text-align: left;
+        border-bottom: 1px solid #e2e8f0;
+    }
+
+    .damage-table th {
+        background: #f7fafc;
+        font-weight: 600;
+        color: #4a5568;
+        text-transform: uppercase;
+        font-size: 0.85rem;
+        letter-spacing: 0.05em;
+    }
+
+    .damage-table tr:last-child td {
+        border-bottom: none;
+    }
+
+    .damage-table tbody tr {
+        transition: background-color 0.2s ease;
+    }
+
+    .damage-table tbody tr:hover {
+        background-color: #f7fafc;
+    }
+
+    .summary-section {
+        background: white;
+        border-radius: 8px;
+        box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
+        padding: 1.5rem;
+    }
+
+    .summary-header {
+        display: flex;
+        align-items: center;
+        gap: 10px;
+        margin-bottom: 1rem;
+        color: #4a5568;
+    }
+
+    .summary-header h4 {
+        margin: 0;
+        font-size: 1.2rem;
+        color: #2d3748;
+    }
+
+    .summary-content {
+        color: #4a5568;
+        line-height: 1.6;
+    }
+
+    .summary-content p {
+        margin: 0 0 1rem;
+    }
+
+    .summary-content p:last-child {
+        margin-bottom: 0;
+    }
+
+    /* Updated Severity colors for light background */
+    .severity-high {
+        color: #e53e3e;  /* Red */
+    }
+
+    .severity-medium {
+        color: #d69e2e;  /* Yellow */
+    }
+
+    .severity-low {
+        color: #38a169;  /* Green */
+    }
+
+    /* Animation for content */
+    @keyframes fadeIn {
+        from { opacity: 0; transform: translateY(-10px); }
+        to { opacity: 1; transform: translateY(0); }
+    }
+
+    .analysis-content.show {
+        animation: fadeIn 0.3s ease-out forwards;
+    }
+    </style>
 </head>
 <body>
     <div class="header" style="position:relative;">
@@ -44,34 +286,308 @@
                     <div class="result-label">Original</div>
                     <img src="{{ original_image }}" alt="Original Image" class="result-img">
                 </div>
-                {% if result.damage_image is defined and result.damage_image %}
-                <div>
-                    <div class="result-label">Damage Prediction</div>
-                    <img src="{{ result.damage_image }}" alt="Damage Prediction" class="result-img">
-                </div>
-                {% endif %}
-                {% if result.parts_image is defined and result.parts_image %}
+                {% if result.parts_image %}
                 <div>
                     <div class="result-label">Parts Prediction</div>
                     <img src="{{ result.parts_image }}" alt="Parts Prediction" class="result-img">
                 </div>
                 {% endif %}
             </div>
-            {% if result.json is defined and result.json %}
+            {% if result.warning %}
+            <div class="warning">{{ result.warning }}</div>
+            {% endif %}
+            {% if result.json %}
                 <h4 style="margin-bottom:8px;">JSON Output</h4>
-                <button class="copy-btn" onclick="copyJSON()" type="button">Copy JSON</button>
-                <button class="copy-btn" id="toggle-json-btn" onclick="toggleJSON()" type="button" style="margin-left:10px;">Show JSON</button>
+                <button class="copy-btn" onclick="copyJSON()">Copy JSON</button>
+                <button class="toggle-btn" id="toggle-json-btn" onclick="toggleBlock('json-block', 'toggle-json-btn')">Show JSON</button>
                 <div id="json-block" style="display:none;">
                     <pre class="json-output" id="json-pre">{{ result.json | tojson(indent=2) }}</pre>
                 </div>
                 <a class="download-btn" href="{{ result.json_download }}" download>Download JSON</a>
             {% endif %}
-            {% if result.damage_image is defined and result.damage_image %}
-                <a class="download-btn" href="{{ result.damage_image }}" download>Download Damage Image</a>
-            {% endif %}
-            {% if result.parts_image is defined and result.parts_image %}
+            {% if result.parts_image %}
                 <a class="download-btn" href="{{ result.parts_image }}" download>Download Parts Image</a>
             {% endif %}
+            {% if result.llm_analysis %}
+            <div class="llm-analysis">
+                <div class="analysis-header">
+                    <h3><i class="fas fa-robot"></i> AI Damage Assessment Report</h3>
+                    <button class="toggle-btn" id="toggle-analysis-btn" onclick="toggleBlock('analysis-block', 'toggle-analysis-btn')">
+                        <span class="btn-text">Show Analysis</span>
+                        <i class="fas fa-chevron-down"></i>
+                    </button>
+                </div>
+                <div id="analysis-block" class="analysis-content" style="display:none;">
+                    <div class="damage-grid">
+                        <div class="table-container">
+                            <table class="damage-table">
+                                <thead>
+                                    <tr>
+                                        <th>Component</th>
+                                        <th>Status</th>
+                                        <th>Severity</th>
+                                        <th>Action Needed</th>
+                                    </tr>
+                                </thead>
+                                <tbody id="damageTbody">
+                                </tbody>
+                            </table>
+                        </div>
+                        <div class="summary-section">
+                            <div class="summary-header">
+                                <i class="fas fa-clipboard-list"></i>
+                                <h4>Assessment Summary</h4>
+                            </div>
+                            <div id="summaryText" class="summary-content"></div>
+                        </div>
+                    </div>
+                </div>
+            </div>
+
+            <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.4/css/all.min.css">
+
+            <style>
+            .llm-analysis {
+                margin-top: 2rem;
+                background: white;  /* White background */
+                border-radius: 12px;
+                box-shadow: 0 4px 6px rgba(0, 0, 0, 0.2);
+                overflow: hidden;
+                transition: all 0.3s ease;
+            }
+
+            .analysis-header {
+                display: flex;
+                justify-content: space-between;
+                align-items: center;
+                padding: 1.5rem;
+                background: linear-gradient(135deg, #805ad5 0%, #6b46c1 100%);  /* Keep purple gradient header */
+                color: white;
+            }
+
+            .analysis-header h3 {
+                margin: 0;
+                font-size: 1.5rem;
+                display: flex;
+                align-items: center;
+                gap: 10px;
+                color: white;
+            }
+
+            .toggle-btn {
+                display: flex;
+                align-items: center;
+                gap: 8px;
+                background: rgba(255, 255, 255, 0.15);
+                color: white;
+                border: 1px solid rgba(255, 255, 255, 0.2);
+                padding: 8px 16px;
+                border-radius: 6px;
+                cursor: pointer;
+                transition: all 0.2s ease;
+            }
+
+            .toggle-btn:hover {
+                background: rgba(255, 255, 255, 0.25);
+            }
+
+            .toggle-btn i {
+                transition: transform 0.3s ease;
+            }
+
+            .toggle-btn.active i {
+                transform: rotate(180deg);
+            }
+
+            .analysis-content {
+                padding: 1.5rem;
+                background: white;
+                color: #2d3748;  /* Dark gray text */
+            }
+
+            .damage-grid {
+                display: grid;
+                gap: 1.5rem;
+                grid-template-columns: 1fr;
+            }
+
+            @media (min-width: 1024px) {
+                .damage-grid {
+                    grid-template-columns: 3fr 2fr;
+                }
+            }
+
+            .table-container {
+                overflow-x: auto;
+                background: white;
+                border-radius: 8px;
+                box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
+            }
+
+            .damage-table {
+                width: 100%;
+                border-collapse: collapse;
+                margin: 0;
+                font-size: 0.95rem;
+                color: #2d3748;
+            }
+
+            .damage-table th, .damage-table td {
+                padding: 12px 16px;
+                text-align: left;
+                border-bottom: 1px solid #e2e8f0;
+            }
+
+            .damage-table th {
+                background: #f7fafc;
+                font-weight: 600;
+                color: #4a5568;
+                text-transform: uppercase;
+                font-size: 0.85rem;
+                letter-spacing: 0.05em;
+            }
+
+            .damage-table tr:last-child td {
+                border-bottom: none;
+            }
+
+            .damage-table tbody tr {
+                transition: background-color 0.2s ease;
+            }
+
+            .damage-table tbody tr:hover {
+                background-color: #f7fafc;
+            }
+
+            .summary-section {
+                background: white;
+                border-radius: 8px;
+                box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
+                padding: 1.5rem;
+            }
+
+            .summary-header {
+                display: flex;
+                align-items: center;
+                gap: 10px;
+                margin-bottom: 1rem;
+                color: #4a5568;
+            }
+
+            .summary-header h4 {
+                margin: 0;
+                font-size: 1.2rem;
+                color: #2d3748;
+            }
+
+            .summary-content {
+                color: #4a5568;
+                line-height: 1.6;
+            }
+
+            .summary-content p {
+                margin: 0 0 1rem;
+            }
+
+            .summary-content p:last-child {
+                margin-bottom: 0;
+            }
+
+            /* Updated Severity colors for light background */
+            .severity-high {
+                color: #e53e3e;  /* Red */
+            }
+
+            .severity-medium {
+                color: #d69e2e;  /* Yellow */
+            }
+
+            .severity-low {
+                color: #38a169;  /* Green */
+            }
+
+            /* Animation for content */
+            @keyframes fadeIn {
+                from { opacity: 0; transform: translateY(-10px); }
+                to { opacity: 1; transform: translateY(0); }
+            }
+
+            .analysis-content.show {
+                animation: fadeIn 0.3s ease-out forwards;
+            }
+            </style>
+
+            <script>
+            function parseAndDisplayDamageReport() {
+                const block = document.getElementById('analysis-block');
+                const tbody = document.getElementById('damageTbody');
+                const summaryDiv = document.getElementById('summaryText');
+                const analysisText = `{{ result.llm_analysis | safe }}`;
+                
+                tbody.innerHTML = '';
+                summaryDiv.innerHTML = '';
+                
+                const lines = analysisText.split('\n');
+                let summaryText = [];
+                
+                lines.forEach(line => {
+                    if (line.includes('|')) {
+                        const parts = line.split('|').filter(part => part.trim() !== '');
+                        if (parts.length === 4 && !line.includes('Component') && !line.includes('---')) {
+                            const row = document.createElement('tr');
+                            parts.forEach((part, index) => {
+                                const td = document.createElement('td');
+                                const text = part.trim();
+                                td.textContent = text;
+                                
+                                // Add severity class
+                                if (index === 2) { // Severity column
+                                    if (text.toLowerCase().includes('high')) {
+                                        td.classList.add('severity-high');
+                                    } else if (text.toLowerCase().includes('medium')) {
+                                        td.classList.add('severity-medium');
+                                    } else if (text.toLowerCase().includes('low')) {
+                                        td.classList.add('severity-low');
+                                    }
+                                }
+                                row.appendChild(td);
+                            });
+                            tbody.appendChild(row);
+                        }
+                    } else if (line.trim() !== '' && !line.includes('**')) {
+                        if (line.length > 10) {
+                            summaryText.push(line.trim());
+                        }
+                    }
+                });
+                
+                summaryText.forEach(text => {
+                    const p = document.createElement('p');
+                    p.textContent = text;
+                    summaryDiv.appendChild(p);
+                });
+            }
+
+            function toggleBlock(blockId, btnId) {
+                const block = document.getElementById(blockId);
+                const btn = document.getElementById(btnId);
+                const isHidden = block.style.display === 'none' || !block.style.display;
+                
+                block.style.display = isHidden ? 'block' : 'none';
+                btn.classList.toggle('active');
+                
+                const btnText = btn.querySelector('.btn-text');
+                btnText.textContent = isHidden ? 'Hide Analysis' : 'Show Analysis';
+                
+                if (isHidden) {
+                    block.classList.add('show');
+                    parseAndDisplayDamageReport();
+                } else {
+                    block.classList.remove('show');
+                }
+            }
+            </script>
+            {% endif %}
         </div>
         {% endif %}
     </div>

app/utils/config.py

@@ -0,0 +1,13 @@
+import os
+from dotenv import load_dotenv
+
+# Simple load of environment variables, works both locally and on Spaces
+load_dotenv()
+
+class Config:
+    GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+    
+    @classmethod
+    def validate(cls):
+        if not cls.GROQ_API_KEY:
+            raise ValueError("GROQ_API_KEY environment variable is not set")

app/utils/llm_client.py

@@ -0,0 +1,86 @@
+from groq import Groq
+import base64
+from PIL import Image
+import io
+from .config import Config
+
+class GroqAnalyzer:
+    def __init__(self):
+        Config.validate()
+        self.client = Groq(api_key=Config.GROQ_API_KEY)
+        self.model = "meta-llama/llama-4-maverick-17b-128e-instruct"
+
+    def analyze_damage(self, image_path):
+        try:
+            with open(image_path, "rb") as image_file:
+                base64_image = base64.b64encode(image_file.read()).decode()
+            
+            system_message = """You are a professional car damage assessment expert. Your task is to analyze car images and provide structured, consistent damage reports.
+            
+Please follow these exact guidelines:
+1. Use only these severity levels: High, Medium, Low
+2. For status, use these terms: Damaged, Intact, Partially Visible, Potentially damaged
+3. For actions, use these categories: Replacement/Repair, Inspection, Inspection/Repair
+4. Always assess these components if visible: Front Bumper, Hood, Grille, Headlights, Fenders
+5. Provide a consistent summary format focusing on:
+   - Main visible damage
+   - Recommended immediate actions
+   - Secondary inspection points
+"""
+
+            analysis_template = """Please analyze the car damage and provide the assessment in this exact format:
+
+[TABLE]
+Component | Status | Severity | Action Needed
+Follow with a structured table using | as separators.
+
+[SUMMARY]
+Start with "The image shows..." and describe:
+1. Primary damage location and severity
+2. Key components affected
+3. Required immediate actions
+4. Additional inspection recommendations
+
+Keep the format consistent and use only the predefined terms for severity, status, and actions."""
+
+            completion = self.client.chat.completions.create(
+                model=self.model,
+                messages=[
+                    {
+                        "role": "system",
+                        "content": system_message
+                    },
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "type": "text",
+                                "text": analysis_template
+                            },
+                            {
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": f"data:image/jpeg;base64,{base64_image}"
+                                }
+                            }
+                        ]
+                    }
+                ],
+                temperature=0.3,  # Lower temperature for more consistent outputs
+                max_completion_tokens=1024,
+                top_p=0.9,  # Slightly lower top_p for more focused outputs
+                stream=True,
+                stop=None
+            )
+
+            # Handle streaming response
+            full_response = ""
+            for chunk in completion:
+                if chunk.choices[0].delta.content:
+                    full_response += chunk.choices[0].delta.content
+            
+            return full_response
+
+        except Exception as e:
+            print(f"Error in Groq analysis: {e}")
+            return f"Error analyzing image: {str(e)}"

app/utils/model_loader.py

@@ -0,0 +1,16 @@
+import os
+from huggingface_hub import hf_hub_download
+
+def download_model_from_hub(repo_id, filename):
+    """Download model from Hugging Face Hub"""
+    return hf_hub_download(
+        repo_id=repo_id,
+        filename=filename,
+        token=os.getenv("HF_TOKEN")  # Optional: if models are private
+    )
+
+# Model paths
+PARTS_MODEL_PATH = download_model_from_hub(
+    "AItoolstack/car_damage_detection",
+    "yolov8_models/parts/weights/weights/best.pt"
+)

requirements.txt

@@ -10,4 +10,7 @@ jinja2
 starlette
 requests
 aiofiles
-python-multipart
+python-multipart
+groq
+python-dotenv
+huggingface-hub