Update backend/app.py

backend/app.py

@@ -1,7 +1,6 @@
 import os
 import shutil
 
-
 for d in ["/tmp/huggingface", "/tmp/Ultralytics", "/tmp/matplotlib", "/tmp/torch", "/root/.cache"]:
     shutil.rmtree(d, ignore_errors=True)
     
@@ -11,75 +10,263 @@ os.environ["TORCH_HOME"] = "/tmp/torch"
 os.environ["MPLCONFIGDIR"] = "/tmp/matplotlib"
 os.environ["YOLO_CONFIG_DIR"] = "/tmp/Ultralytics"
 
-
-from huggingface_hub import login
-
-
-
-hf_token = os.getenv("HF_TOKEN")
-if hf_token:
-    login(token=hf_token)
-
+import json
+import uuid
+import datetime
+import numpy as np
+import torch
+import cv2
+import joblib
+import torch.nn as nn
+import torchvision.transforms as transforms
+import torchvision.models as models
+from io import BytesIO
+from PIL import Image as PILImage
 from fastapi import FastAPI, File, UploadFile, Form
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, FileResponse
+import tensorflow as tf
+from model_histo import BreastCancerClassifier
 from fastapi.staticfiles import StaticFiles
-from ultralytics import YOLO
-from io import BytesIO
-from PIL import Image
 import uvicorn
-import json, os, uuid, numpy as np, torch, cv2, joblib, io, tensorflow as tf
-import torch.nn as nn
-import torchvision.transforms as transforms
-import torchvision.models as models
+try:
+    from reportlab.lib.pagesizes import letter
+    from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image as ReportLabImage
+    from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+    from reportlab.lib.enums import TA_CENTER, TA_JUSTIFY
+    from reportlab.lib.units import inch
+    from reportlab.lib.colors import navy, black
+    REPORTLAB_AVAILABLE = True
+except ImportError:
+    REPORTLAB_AVAILABLE = False
+from ultralytics import YOLO
 from sklearn.preprocessing import MinMaxScaler
 from model import MWT as create_model
 from augmentations import Augmentations
-from model_histo import BreastCancerClassifier  # TensorFlow model
+from huggingface_hub import InferenceClient
+
+# =====================================================
+
+# SETUP TEMP DIRS AND ENV
+
+# =====================================================
+
+for d in ["/tmp/huggingface", "/tmp/Ultralytics", "/tmp/matplotlib", "/tmp/torch"]:
+    shutil.rmtree(d, ignore_errors=True)
+
+os.environ["HF_HOME"] = "/tmp/huggingface"
+os.environ["HUGGINGFACE_HUB_CACHE"] = "/tmp/huggingface"
+os.environ["TORCH_HOME"] = "/tmp/torch"
+os.environ["MPLCONFIGDIR"] = "/tmp/matplotlib"
+os.environ["YOLO_CONFIG_DIR"] = "/tmp/Ultralytics"
 
+# =====================================================
 
+# HUGGING FACE CLIENT SETUP
 
 # =====================================================
-# App setup
+
+HF_MODEL_ID = "mistralai/Mistral-7B-v0.1"
+hf_token = os.getenv("HF_TOKEN")
+client = None
+
+if hf_token:
+    try:
+        client = InferenceClient(model=HF_MODEL_ID, token=hf_token)
+        print(f"✅ Hugging Face InferenceClient initialized for {HF_MODEL_ID}")
+    except Exception as e:
+        print("⚠️ Failed to initialize Hugging Face client:", e)
+else:
+    print("⚠️ Warning: No HF_TOKEN found — summaries will be skipped.")
+
+def generate_ai_summary(abnormal_cells, normal_cells, avg_confidence):
+    """Generate a brief medical interpretation using Mistral."""
+    if not client:
+        return "⚠️ Hugging Face client not initialized — skipping summary."
+
+    try:
+        prompt = f"""Act as a cytopathology expert providing a brief diagnostic interpretation.
+
+Observed Cell Counts:
+- {abnormal_cells} Abnormal Cells
+- {normal_cells} Normal Cells
+- Detection Confidence: {avg_confidence:.1f}%
+
+Write a 2-3 sentence professional medical assessment focusing on:
+1. Cell count analysis
+2. Abnormality ratio ({abnormal_cells/(abnormal_cells + normal_cells)*100:.1f}%)
+3. Clinical significance
+
+Use objective, scientific language suitable for a pathology report."""
+
+        # Use streaming to avoid StopIteration
+        response = client.text_generation(
+            prompt,
+            max_new_tokens=200,
+            temperature=0.7,
+            stream=False,
+            details=True,
+            stop_sequences=["\n\n", "###"]
+        )
+        
+        # Handle different response formats
+        if hasattr(response, 'generated_text'):
+            return response.generated_text.strip()
+        elif isinstance(response, dict):
+            return response.get('generated_text', '').strip()
+        elif isinstance(response, str):
+            return response.strip()
+        
+        # Fallback summary if response format is unexpected
+        ratio = abnormal_cells / (abnormal_cells + normal_cells) * 100 if (abnormal_cells + normal_cells) > 0 else 0
+        return f"Analysis shows {abnormal_cells} abnormal cells ({ratio:.1f}%) and {normal_cells} normal cells, with average detection confidence of {avg_confidence:.1f}%."
+        
+    except Exception as e:
+        # Provide a structured fallback summary instead of error message
+        total = abnormal_cells + normal_cells
+        if total == 0:
+            return "No cells were detected in the sample. Consider re-scanning or adjusting detection parameters."
+        
+        ratio = (abnormal_cells / total) * 100
+        severity = "high" if ratio > 70 else "moderate" if ratio > 30 else "low"
+        
+        return f"Quantitative analysis detected {abnormal_cells} abnormal cells ({ratio:.1f}%) among {total} total cells, indicating {severity} abnormality ratio. Average detection confidence: {avg_confidence:.1f}%."
+
+
+def generate_mwt_summary(predicted_label, confidences, avg_confidence):
+    """Generate a short MWT-specific interpretation using the HF client when available."""
+    if not client:
+        return "⚠️ Hugging Face client not initialized — skipping AI interpretation."
+
+    try:
+        prompt = f"""
+You are a concise cytopathology expert. Given an MWT classifier result, write a 1-2 sentence professional interpretation suitable for embedding in a diagnostic report.
+
+Result:
+- Predicted label: {predicted_label}
+- Confidence (average): {avg_confidence:.1f}%
+- Class probabilities: {json.dumps(confidences)}
+
+Provide guidance on the significance of the result and any suggested next steps in plain, objective language.
+"""
+
+        response = client.text_generation(
+            prompt,
+            max_new_tokens=120,
+            temperature=0.2,
+            stream=False,
+            details=True,
+            stop_sequences=["\n\n", "###"]
+        )
+
+        if hasattr(response, 'generated_text'):
+            return response.generated_text.strip()
+        elif isinstance(response, dict):
+            return response.get('generated_text', '').strip()
+        elif isinstance(response, str):
+            return response.strip()
+
+        return f"Result: {predicted_label} (avg confidence {avg_confidence:.1f}%)."
+    except Exception as e:
+        return f"Quantitative result: {predicted_label} with average confidence {avg_confidence:.1f}%."
+
+
+def generate_cin_summary(predicted_grade, confidences, avg_confidence):
+    """Generate a short CIN-specific interpretation using the HF client when available."""
+    if not client:
+        return "⚠️ Hugging Face client not initialized — skipping AI interpretation."
+
+    try:
+        prompt = f"""
+You are a concise gynecologic pathology expert. Given a CIN classifier result, write a 1-2 sentence professional interpretation suitable for a diagnostic report.
+
+Result:
+- Predicted grade: {predicted_grade}
+- Confidence (average): {avg_confidence:.1f}%
+- Class probabilities: {json.dumps(confidences)}
+
+Provide a brief statement about clinical significance and suggested next steps (e.g., further colposcopic evaluation) in objective, clinical language.
+"""
+
+        response = client.text_generation(
+            prompt,
+            max_new_tokens=140,
+            temperature=0.2,
+            stream=False,
+            details=True,
+            stop_sequences=["\n\n", "###"]
+        )
+
+        if hasattr(response, 'generated_text'):
+            return response.generated_text.strip()
+        elif isinstance(response, dict):
+            return response.get('generated_text', '').strip()
+        elif isinstance(response, str):
+            return response.strip()
+
+        return f"Result: {predicted_grade} (avg confidence {avg_confidence:.1f}%)."
+    except Exception:
+        return f"Quantitative result: {predicted_grade} with average confidence {avg_confidence:.1f}%."
+
+
 # =====================================================
-app = FastAPI(title="Unified Cervical & Breast Cancer Analysis API")
+
+# FASTAPI SETUP
+
+# =====================================================
+
+app = FastAPI(title="Pathora Medical Diagnostic API")
 
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],
+    allow_origins=["*", "http://localhost:5173", "http://127.0.0.1:5173"],
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
+    expose_headers=["*"]  # Allow access to response headers
 )
 
-OUTPUT_DIR = "/tmp/outputs"
+OUTPUT_DIR = "outputs"
 os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# Create image outputs dir
+IMAGES_DIR = os.path.join(OUTPUT_DIR, "images")
+os.makedirs(IMAGES_DIR, exist_ok=True)
+
 app.mount("/outputs", StaticFiles(directory=OUTPUT_DIR), name="outputs")
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # =====================================================
-# Model 1: YOLO (Colposcopy Detection)
+
+# MODEL LOADS
+
 # =====================================================
+
 print("🔹 Loading YOLO model...")
 yolo_model = YOLO("best2.pt")
 
-# =====================================================
-# Model 2: MWT Classifier
-# =====================================================
 print("🔹 Loading MWT model...")
 mwt_model = create_model(num_classes=2).to(device)
 mwt_model.load_state_dict(torch.load("MWTclass2.pth", map_location=device))
 mwt_model.eval()
-mwt_class_names = ['Negative', 'Positive']
+mwt_class_names = ["Negative", "Positive"]
 
-# =====================================================
-# Model 3: CIN Classifier
-# =====================================================
 print("🔹 Loading CIN model...")
-clf = joblib.load("logistic_regression_model.pkl")
+try:
+    clf = joblib.load("logistic_regression_model.pkl")
+except Exception as e:
+    print(f"⚠️ CIN classifier not available (logistic_regression_model.pkl missing or invalid): {e}")
+    clf = None
+
 yolo_colposcopy = YOLO("yolo_colposcopy.pt")
 
+# =====================================================
+
+# RESNET FEATURE EXTRACTORS FOR CIN
+
+# =====================================================
+
 def build_resnet(model_name="resnet50"):
     if model_name == "resnet50":
         model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
@@ -92,7 +279,6 @@ def build_resnet(model_name="resnet50"):
         nn.Sequential(model.conv1, model.bn1, model.relu, model.maxpool),
         model.layer1, model.layer2, model.layer3, model.layer4,
     )
-
 gap = nn.AdaptiveAvgPool2d((1, 1))
 gmp = nn.AdaptiveMaxPool2d((1, 1))
 resnet50_blocks = build_resnet("resnet50")
@@ -100,29 +286,14 @@ resnet101_blocks = build_resnet("resnet101")
 resnet152_blocks = build_resnet("resnet152")
 
 transform = transforms.Compose([
-    transforms.ToPILImage(),
-    transforms.Resize((224, 224)),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                         std=[0.229, 0.224, 0.225]),
+transforms.ToPILImage(),
+transforms.Resize((224, 224)),
+transforms.ToTensor(),
+transforms.Normalize(mean=[0.485, 0.456, 0.406],
+std=[0.229, 0.224, 0.225]),
 ])
 
-# =====================================================
-# Model 4: Histopathology Classifier (TensorFlow)
-# =====================================================
-print("🔹 Loading Breast Cancer Histopathology model...")
-classifier = BreastCancerClassifier(fine_tune=False)
-if not classifier.authenticate_huggingface():
-    raise RuntimeError("HuggingFace authentication failed.")
-if not classifier.load_path_foundation():
-    raise RuntimeError("Failed to load Path Foundation model.")
-model_path = "histopathology_trained_model.keras"
-classifier.model = tf.keras.models.load_model(model_path)
-print(f"✅ Loaded model from {model_path}")
 
-# =====================================================
-# Helper functions
-# =====================================================
 def preprocess_for_mwt(image_np):
     img = cv2.resize(image_np, (224, 224))
     img = Augmentations.Normalization((0, 1))(img)
@@ -145,44 +316,147 @@ def extract_cbf_features(blocks, img_t):
         p3 = gap(f3).view(-1)
         p4 = gap(f4).view(-1)
         p5 = gap(f5).view(-1)
-        cbf_feature = torch.cat([p1, p2, p3, p4, p5], dim=0)
-    return cbf_feature.cpu().numpy()
-
-def predict_histopathology(image: Image.Image):
-    if image.mode != "RGB":
-        image = image.convert("RGB")
-    image = image.resize((224, 224))
-    img_array = np.expand_dims(np.array(image).astype("float32") / 255.0, axis=0)
-    embeddings = classifier.extract_embeddings(img_array)
-    prediction_proba = classifier.model.predict(embeddings, verbose=0)[0]
-    predicted_class = int(np.argmax(prediction_proba))
-    class_names = ["Benign", "Malignant"]
-    return {
-        "model_used": "Breast Cancer Histopathology Classifier",
-        "prediction": class_names[predicted_class],
-        "confidence": float(np.max(prediction_proba))
-    }
+        return torch.cat([p1, p2, p3, p4, p5], dim=0).cpu().numpy()
+    
+# =====================================================
+# Model 4: Histopathology Classifier (TensorFlow)
+# =====================================================
+print("🔹 Attempting to load Breast Cancer Histopathology model...")
+
+try:
+    classifier = BreastCancerClassifier(fine_tune=False)
+
+    # Safely handle Hugging Face token auth
+    hf_token = os.getenv("HF_TOKEN")
+    if hf_token:
+        if classifier.authenticate_huggingface():
+            print("✅ Hugging Face authentication successful.")
+        else:
+            print("⚠️ Warning: Hugging Face authentication failed, using local model only.")
+    else:
+        print("⚠️ HF_TOKEN not found in environment — skipping authentication.")
+
+    # Load Path Foundation model
+    if classifier.load_path_foundation():
+        print("✅ Loaded Path Foundation base model.")
+    else:
+        print("⚠️ Could not load Path Foundation base model, continuing with local weights only.")
+
+    # Load trained histopathology model
+    model_path = "histopathology_trained_model.keras"
+    if os.path.exists(model_path):
+        classifier.model = tf.keras.models.load_model(model_path)
+        print(f"✅ Loaded local histopathology model: {model_path}")
+    else:
+        print(f"⚠️ Model file not found: {model_path}")
+
+except Exception as e:
+    classifier = None
+    print(f"❌ Error initializing histopathology model: {e}")
+
+def predict_histopathology(image):
+    if classifier is None:
+        return {"error": "Histopathology model not available."}
+
+    try:
+        if image.mode != "RGB":
+            image = image.convert("RGB")
+        image = image.resize((224, 224))
+        img_array = np.expand_dims(np.array(image).astype("float32") / 255.0, axis=0)
+        embeddings = classifier.extract_embeddings(img_array)
+        prediction_proba = classifier.model.predict(embeddings, verbose=0)[0]
+        predicted_class = int(np.argmax(prediction_proba))
+        class_names = ["Benign", "Malignant"]
+        
+        # Return confidence as dictionary with both class probabilities (like MWT/CIN)
+        confidences = {class_names[i]: float(prediction_proba[i]) for i in range(len(class_names))}
+        avg_confidence = float(np.max(prediction_proba)) * 100
+        
+        return {
+            "model_used": "Histopathology Classifier",
+            "prediction": class_names[predicted_class],
+            "confidence": confidences,
+            "summary": {
+                "avg_confidence": round(avg_confidence, 2),
+                "ai_interpretation": f"Histopathological analysis indicates {class_names[predicted_class].lower()} tissue with {avg_confidence:.1f}% confidence.",
+            },
+        }
+    except Exception as e:
+        return {"error": f"Histopathology prediction failed: {e}"}
+
 
 # =====================================================
-# Main endpoint
+
+# MAIN ENDPOINT
+
 # =====================================================
+
+
 @app.post("/predict/")
 async def predict(model_name: str = Form(...), file: UploadFile = File(...)):
+    print(f"Received prediction request - model: {model_name}, file: {file.filename}")
+    
+    # Validate model name
+    if model_name not in ["yolo", "mwt", "cin", "histopathology"]:
+        return JSONResponse(
+            content={
+                "error": f"Invalid model_name: {model_name}. Must be one of: yolo, mwt, cin, histopathology"
+            },
+            status_code=400
+        )
+    
+    # Validate and read file
+    if not file.filename:
+        return JSONResponse(
+            content={"error": "No file provided"},
+            status_code=400
+        )
+    
     contents = await file.read()
-    image = Image.open(BytesIO(contents)).convert("RGB")
-    image_np = np.array(image)
+    if len(contents) == 0:
+        return JSONResponse(
+            content={"error": "Empty file provided"},
+            status_code=400
+        )
+
+    # Attempt to open and validate image
+    try:
+        image = PILImage.open(BytesIO(contents)).convert("RGB")
+        image_np = np.array(image)
+        if image_np.size == 0:
+            raise ValueError("Empty image array")
+        print(f"Successfully loaded image, shape: {image_np.shape}")
+    except Exception as e:
+        return JSONResponse(
+            content={"error": f"Invalid image file: {str(e)}"},
+            status_code=400
+        )
 
     if model_name == "yolo":
         results = yolo_model(image)
         detections_json = results[0].to_json()
         detections = json.loads(detections_json)
+
+        abnormal_cells = sum(1 for d in detections if d["name"] == "abnormal")
+        normal_cells = sum(1 for d in detections if d["name"] == "normal")
+        avg_confidence = np.mean([d.get("confidence", 0) for d in detections]) * 100 if detections else 0
+
+        ai_summary = generate_ai_summary(abnormal_cells, normal_cells, avg_confidence)
+
         output_filename = f"detected_{uuid.uuid4().hex[:8]}.jpg"
-        output_path = os.path.join(OUTPUT_DIR, output_filename)
+        output_path = os.path.join(IMAGES_DIR, output_filename)
         results[0].save(filename=output_path)
+
         return {
             "model_used": "YOLO Detection",
             "detections": detections,
-            "annotated_image_url": f"/outputs/{output_filename}"
+            "annotated_image_url": f"/outputs/images/{output_filename}",
+            "summary": {
+                "abnormal_cells": abnormal_cells,
+                "normal_cells": normal_cells,
+                "avg_confidence": round(float(avg_confidence), 2),
+                "ai_interpretation": ai_summary,
+            },
         }
 
     elif model_name == "mwt":
@@ -191,15 +465,35 @@ async def predict(model_name: str = Form(...), file: UploadFile = File(...)):
             output = mwt_model(tensor.to(device)).cpu()
             probs = torch.softmax(output, dim=1)[0]
         confidences = {mwt_class_names[i]: float(probs[i]) for i in range(2)}
-        predicted_label = mwt_class_names[torch.argmax(probs)]
-        return {"model_used": "MWT Classifier", "prediction": predicted_label, "confidence": confidences}
+        predicted_label = mwt_class_names[int(torch.argmax(probs).item())]
+        # Average / primary confidence for display
+        avg_confidence = float(torch.max(probs).item()) * 100
+
+        # Generate a brief AI interpretation using the Mistral client (if available)
+        ai_interp = generate_mwt_summary(predicted_label, confidences, avg_confidence)
+
+        return {
+            "model_used": "MWT Classifier",
+            "prediction": predicted_label,
+            "confidence": confidences,
+            "summary": {
+                "avg_confidence": round(avg_confidence, 2),
+                "ai_interpretation": ai_interp,
+            },
+        }
 
     elif model_name == "cin":
+        if clf is None:
+            return JSONResponse(
+                content={"error": "CIN classifier not available on server."},
+                status_code=503,
+            )
         nparr = np.frombuffer(contents, np.uint8)
         img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
         results = yolo_colposcopy.predict(source=img, conf=0.7, save=False, verbose=False)
         if len(results[0].boxes) == 0:
             return {"error": "No cervix detected"}
+
         x1, y1, x2, y2 = map(int, results[0].boxes.xyxy[0].cpu().numpy())
         crop = img[y1:y2, x1:x2]
         crop = cv2.resize(crop, (224, 224))
@@ -211,52 +505,516 @@ async def predict(model_name: str = Form(...), file: UploadFile = File(...)):
         X_scaled = MinMaxScaler().fit_transform(features)
         pred = clf.predict(X_scaled)[0]
         proba = clf.predict_proba(X_scaled)[0]
-        classes = ["CIN1", "CIN2", "CIN3"]
+        # Get actual number of classes from model output
+        classes = ["Low-grade", "High-grade"]  # Binary CIN classification
         predicted_label = classes[pred]
-        predicted_confidence = float(proba[pred])
+        confidences = {classes[i]: float(proba[i]) for i in range(len(classes))}
+        
+        # Map to more detailed classification based on confidence
+        if predicted_label == "High-grade" and confidences["High-grade"] > 0.8:
+            detailed_class = "CIN3"
+        elif predicted_label == "High-grade":
+            detailed_class = "CIN2"
+        else:
+            detailed_class = "CIN1"
+
+        # Average / primary confidence for display
+        avg_confidence = float(np.max(proba)) * 100
+
+        # Generate a brief AI interpretation using the Mistral client (if available)
+        ai_interp = generate_cin_summary(predicted_label, confidences, avg_confidence)
+
         return {
             "model_used": "CIN Classifier",
-            "prediction": predicted_label,
-            "confidence": predicted_confidence
+            "prediction": detailed_class,
+            "grade": predicted_label,
+            "confidence": confidences,
+            "summary": {
+                "avg_confidence": round(avg_confidence, 2),
+                "ai_interpretation": ai_interp,
+            },
         }
-
-
     elif model_name == "histopathology":
-        result = predict_histopathology(image)
-        return result
+            result = predict_histopathology(image)
+            return result
+
 
     else:
         return JSONResponse(content={"error": "Invalid model name"}, status_code=400)
 
-@app.on_event("startup")
-async def cleanup_on_startup():
-    for d in ["/tmp/huggingface", "/tmp/Ultralytics", "/tmp/matplotlib", "/tmp/torch", "/root/.cache"]:
-        shutil.rmtree(d, ignore_errors=True)
-    print("✅ Cleaned caches on startup")
+# =====================================================
+
+# ROUTES
+
+# =====================================================
+
+def create_designed_pdf(pdf_path, report_data, analysis_summary_json):
+    doc = SimpleDocTemplate(pdf_path, pagesize=letter,
+                            rightMargin=72, leftMargin=72,
+                            topMargin=72, bottomMargin=18)
+    styles = getSampleStyleSheet()
+    story = []
+
+    styles.add(ParagraphStyle(name='Title', fontSize=20, fontName='Helvetica-Bold', alignment=TA_CENTER, textColor=navy))
+    styles.add(ParagraphStyle(name='Section', fontSize=14, fontName='Helvetica-Bold', spaceBefore=10, spaceAfter=6))
+    styles.add(ParagraphStyle(name='NormalSmall', fontSize=10, leading=12))
+    styles.add(ParagraphStyle(name='Heading', fontSize=16, fontName='Helvetica-Bold', textColor=navy, spaceBefore=6, spaceAfter=4))
+
+    patient = report_data['patient']
+    analysis = report_data.get('analysis', {})
+    
+    # Safely parse analysis_summary_json
+    try:
+        ai_summary = json.loads(analysis_summary_json) if analysis_summary_json else {}
+    except (json.JSONDecodeError, TypeError):
+        ai_summary = {}
+
+    # Determine report type based on model used
+    model_used = ai_summary.get('model_used', '')
+    if 'YOLO' in model_used or 'yolo' in str(analysis.get('id', '')).lower():
+        report_type = "CYTOLOGY"
+        report_title = "Cytology Report"
+    elif 'CIN' in model_used or 'cin' in str(analysis.get('id', '')).lower() or 'colpo' in str(analysis.get('id', '')).lower():
+        report_type = "COLPOSCOPY"
+        report_title = "Colposcopy Report"
+    elif 'histo' in str(analysis.get('id', '')).lower() or 'histopathology' in model_used.lower():
+        report_type = "HISTOPATHOLOGY"
+        report_title = "Histopathology Report"
+    else:
+        report_type = "CYTOLOGY"
+        report_title = "Medical Analysis Report"
+
+    # Header
+    story.append(Paragraph("MANALIFE AI", styles['Title']))
+    story.append(Paragraph("Advanced Medical Analysis", styles['NormalSmall']))
+    story.append(Spacer(1, 0.3*inch))
+    story.append(Paragraph(f"MEDICAL ANALYSIS REPORT OF {report_type}", styles['Heading']))
+    story.append(Paragraph(report_title, styles['Section']))
+    story.append(Spacer(1, 0.2*inch))
+
+    # Report ID and Date
+    story.append(Paragraph(f"<b>Report ID:</b> {report_data.get('report_id', 'N/A')}", styles['NormalSmall']))
+    story.append(Paragraph(f"<b>Generated:</b> {datetime.datetime.now().strftime('%b %d, %Y, %I:%M %p')}", styles['NormalSmall']))
+    story.append(Spacer(1, 0.2*inch))
+
+    # Patient Information Section
+    story.append(Paragraph("Patient Information", styles['Section']))
+    story.append(Paragraph(f"<b>Patient ID:</b> {patient.get('id', 'N/A')}", styles['NormalSmall']))
+    story.append(Paragraph(f"<b>Exam Date:</b> {patient.get('exam_date', 'N/A')}", styles['NormalSmall']))
+    story.append(Paragraph(f"<b>Physician:</b> {patient.get('physician', 'N/A')}", styles['NormalSmall']))
+    story.append(Paragraph(f"<b>Facility:</b> {patient.get('facility', 'N/A')}", styles['NormalSmall']))
+    story.append(Spacer(1, 0.2*inch))
+
+    # Sample Information Section
+    story.append(Paragraph("Sample Information", styles['Section']))
+    story.append(Paragraph(f"<b>Specimen Type:</b> {patient.get('specimen_type', 'Cervical Cytology')}", styles['NormalSmall']))
+    story.append(Paragraph(f"<b>Clinical History:</b> {patient.get('clinical_history', 'N/A')}", styles['NormalSmall']))
+    story.append(Spacer(1, 0.2*inch))
+
+    # AI Analysis Section
+    story.append(Paragraph("AI-ASSISTED ANALYSIS", styles['Section']))
+    story.append(Paragraph("<b>System:</b> Manalife AI System — Automated Analysis", styles['NormalSmall']))
+    story.append(Paragraph(f"<b>Confidence Score:</b> {ai_summary.get('avg_confidence', 'N/A')}%", styles['NormalSmall']))
+    
+    # Add metrics based on report type
+    if report_type == "HISTOPATHOLOGY":
+        # For histopathology, show Benign/Malignant confidence
+        confidence_dict = ai_summary.get('confidence', {})
+        if isinstance(confidence_dict, dict):
+            benign_conf = confidence_dict.get('Benign', 0) * 100
+            malignant_conf = confidence_dict.get('Malignant', 0) * 100
+            story.append(Paragraph(f"<b>Benign Confidence:</b> {benign_conf:.2f}%", styles['NormalSmall']))
+            story.append(Paragraph(f"<b>Malignant Confidence:</b> {malignant_conf:.2f}%", styles['NormalSmall']))
+    elif report_type == "CYTOLOGY":
+        # For cytology (YOLO), show abnormal/normal cells
+        if 'abnormal_cells' in ai_summary:
+            story.append(Paragraph(f"<b>Abnormal Cells:</b> {ai_summary.get('abnormal_cells', 'N/A')}", styles['NormalSmall']))
+        if 'normal_cells' in ai_summary:
+            story.append(Paragraph(f"<b>Normal Cells:</b> {ai_summary.get('normal_cells', 'N/A')}", styles['NormalSmall']))
+    else:
+        # For CIN/Colposcopy, show class confidences
+        confidence_dict = ai_summary.get('confidence', {})
+        if isinstance(confidence_dict, dict):
+            for cls, val in confidence_dict.items():
+                conf_pct = val * 100 if isinstance(val, (int, float)) else 0
+                story.append(Paragraph(f"<b>{cls} Confidence:</b> {conf_pct:.2f}%", styles['NormalSmall']))
+    
+    story.append(Spacer(1, 0.1*inch))
+    story.append(Paragraph("<b>AI Interpretation:</b>", styles['NormalSmall']))
+    story.append(Paragraph(ai_summary.get('ai_interpretation', 'Not available.'), styles['NormalSmall']))
+    story.append(Spacer(1, 0.2*inch))
+
+    # Doctor's Notes
+    story.append(Paragraph("Doctor's Notes", styles['Section']))
+    story.append(Paragraph(report_data.get('doctor_notes') or 'No additional notes provided.', styles['NormalSmall']))
+    story.append(Spacer(1, 0.2*inch))
+
+    # Recommendations
+    story.append(Paragraph("RECOMMENDATIONS", styles['Section']))
+    story.append(Paragraph("Continue routine screening as per standard guidelines. Follow up as directed by your physician.", styles['NormalSmall']))
+    story.append(Spacer(1, 0.3*inch))
+
+    # Signatures
+    story.append(Paragraph("Signatures", styles['Section']))
+    story.append(Paragraph("Dr. Emily Roberts, MD (Cytopathologist)", styles['NormalSmall']))
+    story.append(Paragraph("Dr. James Wilson, MD (Pathologist)", styles['NormalSmall']))
+    story.append(Spacer(1, 0.1*inch))
+    story.append(Paragraph(f"Generated on: {datetime.datetime.now().strftime('%b %d, %Y, %I:%M %p')}", styles['NormalSmall']))
+
+    doc.build(story)
+
+
+
+@app.post("/reports/")
+async def generate_report(
+    patient_id: str = Form(...),
+    exam_date: str = Form(...),
+    metadata: str = Form(...),
+    notes: str = Form(None),
+    analysis_id: str = Form(None),
+    analysis_summary: str = Form(None),
+):
+    """Generate a structured medical report from analysis results and metadata."""
+    try:
+        # Create reports directory if it doesn't exist
+        reports_dir = os.path.join(OUTPUT_DIR, "reports")
+        os.makedirs(reports_dir, exist_ok=True)
+        
+        # Generate unique report ID
+        report_id = f"{patient_id}_{uuid.uuid4().hex[:8]}"
+        report_dir = os.path.join(reports_dir, report_id)
+        os.makedirs(report_dir, exist_ok=True)
+
+        # Parse metadata
+        metadata_dict = json.loads(metadata)
+        
+        # Get analysis results - assuming stored in memory or retrievable
+        # TODO: Implement analysis results storage/retrieval
+        
+        # Construct report data
+        report_data = {
+            "report_id": report_id,
+            "generated_at": datetime.datetime.now().isoformat(),
+            "patient": {
+                "id": patient_id,
+                "exam_date": exam_date,
+                **metadata_dict
+            },
+            "analysis": {
+                "id": analysis_id,
+                # If the analysis_id is actually an annotated image URL, store it for report embedding
+                "annotated_image_url": analysis_id,
+                # TODO: Add actual analysis results
+            },
+            "doctor_notes": notes
+        }
+        
+        # Save report data
+        report_json = os.path.join(report_dir, "report.json")
+        with open(report_json, "w", encoding="utf-8") as f:
+            json.dump(report_data, f, indent=2, ensure_ascii=False)
+            
+        # Attempt to create a PDF version if reportlab is available
+        pdf_url = None
+        if REPORTLAB_AVAILABLE:
+            try:
+                pdf_path = os.path.join(report_dir, "report.pdf")
+                create_designed_pdf(pdf_path, report_data, analysis_summary)
+                pdf_url = f"/outputs/reports/{report_id}/report.pdf"
+            except Exception as e:
+                print(f"Error creating designed PDF: {e}")
+                pdf_url = None
+
+        # Parse analysis_summary to get AI results
+        try:
+            ai_summary = json.loads(analysis_summary) if analysis_summary else {}
+        except (json.JSONDecodeError, TypeError):
+            ai_summary = {}
+
+        # Determine report type based on analysis summary or model used
+        model_used = ai_summary.get('model_used', '')
+        if 'YOLO' in model_used or 'yolo' in str(analysis_id).lower():
+            report_type = "Cytology"
+            report_title = "Cytology Report"
+        elif 'CIN' in model_used or 'cin' in str(analysis_id).lower() or 'colpo' in str(analysis_id).lower():
+            report_type = "Colposcopy"
+            report_title = "Colposcopy Report"
+        elif 'histo' in str(analysis_id).lower() or 'histopathology' in model_used.lower():
+            report_type = "Histopathology"
+            report_title = "Histopathology Report"
+        else:
+            # Default fallback
+            report_type = "Cytology"
+            report_title = "Medical Analysis Report"
+        
+        # Build analysis metrics HTML based on report type
+        if report_type == "Histopathology":
+            # For histopathology, show Benign/Malignant confidence from the confidence dict
+            confidence_dict = ai_summary.get('confidence', {})
+            benign_conf = confidence_dict.get('Benign', 0) * 100 if isinstance(confidence_dict, dict) else 0
+            malignant_conf = confidence_dict.get('Malignant', 0) * 100 if isinstance(confidence_dict, dict) else 0
+            
+            analysis_metrics_html = f"""
+            <tr><th>System</th><td>Manalife AI System — Automated Analysis</td></tr>
+            <tr><th>Confidence Score</th><td>{ai_summary.get('avg_confidence', 'N/A')}%</td></tr>
+            <tr><th>Benign Confidence</th><td>{benign_conf:.2f}%</td></tr>
+            <tr><th>Malignant Confidence</th><td>{malignant_conf:.2f}%</td></tr>
+            """
+        elif report_type == "Cytology":
+            # For cytology (YOLO), show abnormal/normal cells
+            analysis_metrics_html = f"""
+            <tr><th>System</th><td>Manalife AI System — Automated Analysis</td></tr>
+            <tr><th>Confidence Score</th><td>{ai_summary.get('avg_confidence', 'N/A')}%</td></tr>
+            <tr><th>Abnormal Cells</th><td>{ai_summary.get('abnormal_cells', 'N/A')}</td></tr>
+            <tr><th>Normal Cells</th><td>{ai_summary.get('normal_cells', 'N/A')}</td></tr>
+            """
+        else:
+            # For CIN/Colposcopy or other models, show generic confidence
+            confidence_dict = ai_summary.get('confidence', {})
+            confidence_rows = ""
+            if isinstance(confidence_dict, dict):
+                for cls, val in confidence_dict.items():
+                    conf_pct = val * 100 if isinstance(val, (int, float)) else 0
+                    confidence_rows += f"<tr><th>{cls} Confidence</th><td>{conf_pct:.2f}%</td></tr>\n            "
+            
+            analysis_metrics_html = f"""
+            <tr><th>System</th><td>Manalife AI System — Automated Analysis</td></tr>
+            <tr><th>Confidence Score</th><td>{ai_summary.get('avg_confidence', 'N/A')}%</td></tr>
+            {confidence_rows}
+            """
+
+        # Build final HTML including download links and embedded annotated image
+        report_html = os.path.join(report_dir, "report.html")
+        json_url = f"/outputs/reports/{report_id}/report.json"
+        html_url = f"/outputs/reports/{report_id}/report.html"
+        annotated_img = report_data.get("analysis", {}).get("annotated_image_url") or ""
+        
+        # Get base URL for the annotated image (if it's a relative path)
+        annotated_img_full = f"http://localhost:8000{annotated_img}" if annotated_img and annotated_img.startswith('/') else annotated_img
+
+        download_pdf_btn = f'<a href="{pdf_url}" download style="text-decoration:none"><button class="btn-secondary">Download PDF</button></a>' if pdf_url else ''
+        
+        # Format generated time
+        generated_time = datetime.datetime.now().strftime('%b %d, %Y, %I:%M %p')
+
+        html_content = f"""<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width,initial-scale=1" />
+  <title>Medical Analysis Report — Manalife AI</title>
+  <style>
+    :root{{--bg:#f8fafc;--card:#ffffff;--muted:#6b7280;--accent:#0f172a}}
+    body{{font-family:Inter,ui-sans-serif,system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue",Arial;margin:0;background:var(--bg);color:var(--accent);line-height:1.45}}
+    .container{{max-width:900px;margin:36px auto;padding:20px}}
+    header{{display:flex;align-items:center;gap:16px}}
+    .brand{{display:flex;flex-direction:column}}
+    h1{{margin:0;font-size:20px}}
+    .sub{{color:var(--muted);font-size:13px}}
+    .card{{background:var(--card);box-shadow:0 6px 18px rgba(15,23,42,0.06);border-radius:12px;padding:20px;margin-top:18px}}
+    .grid{{display:grid;grid-template-columns:1fr 1fr;gap:12px}}
+    .section-title{{font-weight:600;margin-top:8px}}
+    table{{width:100%;border-collapse:collapse;margin-top:8px}}
+    td,th{{padding:8px;border-bottom:1px dashed #e6e9ef;text-align:left;font-size:14px}}
+    .full{{grid-column:1/-1}}
+    .muted{{color:var(--muted);font-size:13px}}
+    .footer{{margin-top:20px;font-size:13px;color:var(--muted)}}
+    .pill{{background:#eef2ff;color:#1e3a8a;padding:6px 10px;border-radius:999px;font-weight:600;font-size:13px}}
+    @media (max-width:700px){{.grid{{grid-template-columns:1fr}}}}
+    .signatures{{display:flex;gap:20px;flex-wrap:wrap;margin-top:12px}}
+    .sig{{background:#fbfbfd;border:1px solid #f0f1f5;padding:10px;border-radius:8px;min-width:180px}}
+    .annotated-image{{max-width:100%;height:auto;border-radius:8px;margin-top:12px;border:1px solid #e6e9ef}}
+    .btn-primary{{padding:10px 14px;border-radius:8px;border:1px solid #2563eb;background:#2563eb;color:white;font-weight:700;cursor:pointer}}
+    .btn-secondary{{padding:10px 14px;border-radius:8px;border:1px solid #e6eefc;background:#eef2ff;font-weight:700;cursor:pointer}}
+    .actions-bar{{margin-top:12px;display:flex;gap:8px;flex-wrap:wrap}}
+  </style>
+</head>
+<body>
+  <div class="container">
+    <header>
+      <div>
+        <img src="data:image/svg+xml;utf8,<svg xmlns='http://www.w3.org/2000/svg' width='64' height='64'><rect rx='10' width='64' height='64' fill='%230f172a'/><text x='50%' y='55%' font-size='20' fill='white' text-anchor='middle' font-family='Arial'>M</text></svg>" alt="logo" width="64" height="64">
+      </div>
+      <div class="brand">
+        <h1>MANALIFE AI — Medical Analysis</h1>
+        <div class="sub">Advanced cytological colposcopy and histopathology reporting</div>
+        <div class="muted">contact@manalife.ai • +1 (555) 123-4567</div>
+      </div>
+    </header>
+
+    <div class="card">
+      <div style="display:flex;justify-content:space-between;align-items:center;gap:12px;flex-wrap:wrap">
+        <div>
+          <div class="muted">MEDICAL ANALYSIS REPORT OF {report_type.upper()}</div>
+          <h2 style="margin:6px 0 0 0">{report_title}</h2>
+        </div>
+        <div style="text-align:right">
+          <div class="pill">Report ID: {report_id}</div>
+          <div class="muted" style="margin-top:6px">Generated: {generated_time}</div>
+        </div>
+      </div>
+
+      <hr style="border:none;border-top:1px solid #eef2f6;margin:16px 0">
+
+      <div class="grid">
+        <div>
+          <div class="section-title">Patient Information</div>
+          <table>
+            <tr><th>Patient ID</th><td>{patient_id}</td></tr>
+            <tr><th>Exam Date</th><td>{exam_date}</td></tr>
+            <tr><th>Physician</th><td>{metadata_dict.get('physician', 'N/A')}</td></tr>
+            <tr><th>Facility</th><td>{metadata_dict.get('facility', 'N/A')}</td></tr>
+          </table>
+        </div>
+
+        <div>
+          <div class="section-title">Sample Information</div>
+          <table>
+            <tr><th>Specimen Type</th><td>{metadata_dict.get('specimen_type', 'N/A')}</td></tr>
+            <tr><th>Clinical History</th><td>{metadata_dict.get('clinical_history', 'N/A')}</td></tr>
+            <tr><th>Collected</th><td>{exam_date}</td></tr>
+            <tr><th>Reported</th><td>{generated_time}</td></tr>
+          </table>
+        </div>
+
+        <div class="full">
+          <div class="section-title">AI-Assisted Analysis</div>
+          <table>
+            {analysis_metrics_html}
+          </table>
+          <div style="margin-top:12px;padding:12px;background:#f8fafc;border-radius:8px;border-left:4px solid #2563eb">
+            <div style="font-weight:600;margin-bottom:6px">AI Interpretation:</div>
+            <div class="muted">{ai_summary.get('ai_interpretation', 'No AI interpretation available.')}</div>
+          </div>
+        </div>
+
+        {'<div class="full"><div class="section-title">Annotated Analysis Image</div><img src="' + annotated_img_full + '" class="annotated-image" alt="Annotated Analysis Result" /></div>' if annotated_img else ''}
+
+        <div class="full">
+          <div class="section-title">Doctor\'s Notes</div>
+          <p class="muted">{notes or 'No additional notes provided.'}</p>
+        </div>
+
+        <div class="full">
+          <div class="section-title">Recommendations</div>
+          <p class="muted">Continue routine screening as per standard guidelines. Follow up as directed by your physician.</p>
+        </div>
+
+        <div class="full">
+          <div class="section-title">Signatures</div>
+          <div class="signatures">
+            <div class="sig">
+              <div style="font-weight:700">Dr. Emily Roberts</div>
+              <div class="muted">MD, pathologist</div>
+            </div>
+            <div class="sig">
+              <div style="font-weight:700">Dr. James Wilson</div>
+              <div class="muted">MD, pathologist</div>
+            </div>
+          </div>
+        </div>
+      </div>
+
+      <div class="footer">
+        <div>AI System: Manalife AI — Automated Analysis</div>
+        <div style="margin-top:6px">Report generated: {report_data['generated_at']}</div>
+      </div>
+    </div>
+
+    <div class="actions-bar">
+      {download_pdf_btn}
+      <button class="btn-secondary" onclick="window.print()">Print Report</button>
+    </div>
+  </div>
+</body>
+</html>"""
+
+        with open(report_html, "w", encoding="utf-8") as f:
+            f.write(html_content)
 
+        return {
+            "report_id": report_id,
+            "json_url": json_url,
+            "html_url": html_url,
+            "pdf_url": pdf_url,
+        }
+        
+    except Exception as e:
+        return JSONResponse(
+            content={"error": f"Failed to generate report: {str(e)}"},
+            status_code=500
+        )
+
+@app.get("/reports/{report_id}")
+async def get_report(report_id: str):
+    """Fetch a generated report by ID."""
+    report_dir = os.path.join(OUTPUT_DIR, "reports", report_id)
+    report_json = os.path.join(report_dir, "report.json")
+    
+    if not os.path.exists(report_json):
+        return JSONResponse(
+            content={"error": "Report not found"},
+            status_code=404
+        )
+        
+    with open(report_json, "r") as f:
+        report_data = json.load(f)
+    
+    return report_data
+
+@app.get("/reports")
+async def list_reports(patient_id: str = None):
+    """List all generated reports, optionally filtered by patient ID."""
+    reports_dir = os.path.join(OUTPUT_DIR, "reports")
+    if not os.path.exists(reports_dir):
+        return {"reports": []}
+        
+    reports = []
+    for report_id in os.listdir(reports_dir):
+        report_json = os.path.join(reports_dir, report_id, "report.json")
+        if os.path.exists(report_json):
+            with open(report_json, "r") as f:
+                report_data = json.load(f)
+                if not patient_id or report_data["patient"]["id"] == patient_id:
+                    reports.append({
+                        "report_id": report_id,
+                        "patient_id": report_data["patient"]["id"],
+                        "exam_date": report_data["patient"]["exam_date"],
+                        "generated_at": report_data["generated_at"]
+                    })
+                    
+    return {"reports": sorted(reports, key=lambda r: r["generated_at"], reverse=True)}
 
 @app.get("/models")
 def get_models():
     return {"available_models": ["yolo", "mwt", "cin", "histopathology"]}
 
-
 @app.get("/health")
 def health():
-    return {"message": "Unified Cervical & Breast Cancer API is running!"}
+    return {"message": "Pathora Medical Diagnostic API is running!"}
+
+# FRONTEND
+
+# =====================================================
 
-# After other app.mount()s
-app.mount("/outputs", StaticFiles(directory=OUTPUT_DIR), name="outputs")
-app.mount("/assets", StaticFiles(directory="frontend/dist/assets"), name="assets")
-from fastapi.staticfiles import StaticFiles
 
-app.mount("/", StaticFiles(directory="frontend/dist", html=True), name="static")
+# Serve frontend only if it has been built; avoid startup failure when dist/ is missing.
+FRONTEND_DIST = os.path.abspath(os.path.join(os.path.dirname(__file__), "../frontend/dist"))
+ASSETS_DIR = os.path.join(FRONTEND_DIST, "assets")
 
+if os.path.isdir(ASSETS_DIR):
+    app.mount("/assets", StaticFiles(directory=ASSETS_DIR), name="assets")
+else:
+    print("ℹ️ Frontend assets directory not found — skipping /assets mount.")
 
 @app.get("/")
 async def serve_frontend():
-    index_path = os.path.join("frontend", "dist", "index.html")
-    return FileResponse(index_path)
+    index_path = os.path.join(FRONTEND_DIST, "index.html")
+    if os.path.isfile(index_path):
+        return FileResponse(index_path)
+    return JSONResponse({"message": "Backend is running. Frontend build not found."})
 
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
-    
+    # Use PORT provided by the environment (Hugging Face Spaces sets PORT=7860)
+    port = int(os.environ.get("PORT", 7860))
+    uvicorn.run(app, host="0.0.0.0", port=port)