Update app.py

app.py

@@ -17,6 +17,7 @@ from tensorflow.keras.models import load_model
 from analyzer import PhylogeneticTreeAnalyzer
 import tempfile
 import shutil
+import sys
 import uuid
 from pathlib import Path
 from huggingface_hub import hf_hub_download
@@ -27,7 +28,7 @@ import stat
 import time
 import asyncio
 from fastapi import FastAPI, File, UploadFile, Form, HTTPException
-from fastapi.responses import FileResponse
+from fastapi.responses import HTMLResponse, FileResponse
 from pydantic import BaseModel
 from typing import Optional
 import uvicorn
@@ -39,14 +40,15 @@ log_handler.setFormatter(log_formatter)
 try:
     file_handler = logging.FileHandler('/tmp/app.log')
     file_handler.setFormatter(log_formatter)
-    logging.basicConfig(level=logging.DEBUG, handlers=[log_handler, file_handler])  # Changed to DEBUG
+    logging.basicConfig(level=logging.INFO, handlers=[log_handler, file_handler])
 except Exception as e:
-    logging.basicConfig(level=logging.DEBUG, handlers=[log_handler])
+    logging.basicConfig(level=logging.INFO, handlers=[log_handler])
     logging.warning(f"Failed to set up file logging: {e}")
+
 logger = logging.getLogger(__name__)
 logger.info(f"Gradio version: {gr.__version__}")
 
-# Set event loop policy
+# Set event loop policy for compatibility with Gradio Spaces
 try:
     asyncio.set_event_loop_policy(asyncio.DefaultEventLoopPolicy())
 except Exception as e:
@@ -61,49 +63,66 @@ TREE_PATH = os.path.join(BASE_DIR, "f_gene_sequences.phy.treefile")
 QUERY_OUTPUT_DIR = os.path.join(BASE_DIR, "queries")
 os.makedirs(QUERY_OUTPUT_DIR, exist_ok=True)
 
+# Model repository and file paths
 MODEL_REPO = "GGproject10/best_boundary_aware_model"
 CSV_PATH = "f cleaned.csv"
 
-# Initialize models
+# Initialize models as None
 boundary_model = None
 keras_model = None
 kmer_to_index = None
 analyzer = None
 
-# --- Model Loading (from Script 2) ---
+# --- Model Loading ---
 def load_models_safely():
     global boundary_model, keras_model, kmer_to_index, analyzer
     logger.info("🔍 Loading models...")
     try:
-        boundary_path = hf_hub_download(repo_id=MODEL_REPO, filename="best_boundary_aware_model.pth", token=None)
+        boundary_path = hf_hub_download(
+            repo_id=MODEL_REPO,
+            filename="best_boundary_aware_model.pth",
+            token=None
+        )
         if os.path.exists(boundary_path):
             boundary_model = EnhancedGenePredictor(boundary_path)
-            logger.info("✅ Boundary model loaded.")
+            logger.info("✅ Boundary model loaded successfully.")
         else:
-            logger.error(f"❌ Boundary model file not found.")
+            logger.error(f"❌ Boundary model file not found after download.")
     except Exception as e:
-        logger.error(f"❌ Failed to load boundary model: {e}", exc_info=True)
+        logger.error(f"❌ Failed to load boundary model: {e}")
         boundary_model = None
     try:
-        keras_path = hf_hub_download(repo_id=MODEL_REPO, filename="best_model.keras", token=None)
-        kmer_path = hf_hub_download(repo_id=MODEL_REPO, filename="kmer_to_index.pkl", token=None)
+        keras_path = hf_hub_download(
+            repo_id=MODEL_REPO,
+            filename="best_model.keras",
+            token=None
+        )
+        kmer_path = hf_hub_download(
+            repo_id=MODEL_REPO,
+            filename="kmer_to_index.pkl",
+            token=None
+        )
         if os.path.exists(keras_path) and os.path.exists(kmer_path):
             keras_model = load_model(keras_path)
             with open(kmer_path, "rb") as f:
                 kmer_to_index = pickle.load(f)
-            logger.info("✅ Keras model loaded.")
+            logger.info("✅ Keras model and k-mer index loaded successfully.")
         else:
-            logger.error(f"❌ Keras model files not found.")
+            logger.error(f"❌ Keras model or k-mer files not found.")
     except Exception as e:
-        logger.error(f"❌ Failed to load Keras model: {e}", exc_info=True)
+        logger.error(f"❌ Failed to load Keras model: {e}")
         keras_model = None
         kmer_to_index = None
     try:
         logger.info("🌳 Initializing tree analyzer...")
         analyzer = PhylogeneticTreeAnalyzer()
         csv_candidates = [
-            CSV_PATH, os.path.join(BASE_DIR, CSV_PATH), os.path.join(BASE_DIR, "app", CSV_PATH),
-            os.path.join(os.path.dirname(__file__), CSV_PATH), "f_cleaned.csv", os.path.join(BASE_DIR, "f_cleaned.csv")
+            CSV_PATH,
+            os.path.join(BASE_DIR, CSV_PATH),
+            os.path.join(BASE_DIR, "app", CSV_PATH),
+            os.path.join(os.path.dirname(__file__), CSV_PATH),
+            "f_cleaned.csv",
+            os.path.join(BASE_DIR, "f_cleaned.csv")
         ]
         csv_loaded = False
         for csv_candidate in csv_candidates:
@@ -115,25 +134,27 @@ def load_models_safely():
                         csv_loaded = True
                         break
                 except Exception as e:
-                    logger.warning(f"CSV load failed for {csv_candidate}: {e}", exc_info=True)
+                    logger.warning(f"CSV load failed for {csv_candidate}: {e}")
+                    continue
         if not csv_loaded:
-            logger.error("❌ Failed to load CSV data.")
+            logger.error("❌ Failed to load CSV data from any candidate location.")
             analyzer = None
         else:
             try:
                 if analyzer.train_ai_model():
-                    logger.info("✅ AI model training completed.")
+                    logger.info("✅ AI model training completed successfully")
                 else:
-                    logger.warning("⚠️ AI model training failed.")
+                    logger.warning("⚠️ AI model training failed; proceeding with basic analysis.")
             except Exception as e:
-                logger.warning(f"⚠️ AI model training failed: {e}", exc_info=True)
+                logger.warning(f"⚠️ AI model training failed: {e}")
     except Exception as e:
-        logger.error(f"❌ Tree analyzer initialization failed: {e}", exc_info=True)
+        logger.error(f"❌ Tree analyzer initialization failed: {e}")
         analyzer = None
 
+# Load models at startup
 load_models_safely()
 
-# --- Tool Detection (from Script 2) ---
+# --- Tool Detection ---
 def setup_binary_permissions():
     for binary in [MAFFT_PATH, IQTREE_PATH]:
         if os.path.exists(binary):
@@ -141,7 +162,7 @@ def setup_binary_permissions():
                 os.chmod(binary, os.stat(binary).st_mode | stat.S_IEXEC)
                 logger.info(f"Set executable permission on {binary}")
             except Exception as e:
-                logger.warning(f"Failed to set permission on {binary}: {e}", exc_info=True)
+                logger.warning(f"Failed to set permission on {binary}: {e}")
 
 def check_tool_availability():
     setup_binary_permissions()
@@ -151,7 +172,12 @@ def check_tool_availability():
     for candidate in mafft_candidates:
         if shutil.which(candidate) or os.path.exists(candidate):
             try:
-                result = subprocess.run([candidate, "--help"], capture_output=True, text=True, timeout=5)
+                result = subprocess.run(
+                    [candidate, "--help"],
+                    capture_output=True,
+                    text=True,
+                    timeout=5
+                )
                 if result.returncode == 0 or "mafft" in result.stderr.lower():
                     mafft_available = True
                     mafft_cmd = candidate
@@ -165,7 +191,12 @@ def check_tool_availability():
     for candidate in iqtree_candidates:
         if shutil.which(candidate) or os.path.exists(candidate):
             try:
-                result = subprocess.run([candidate, "--help"], capture_output=True, text=True, timeout=5)
+                result = subprocess.run(
+                    [candidate, "--help"],
+                    capture_output=True,
+                    text=True,
+                    timeout=5
+                )
                 if result.returncode == 0 or "iqtree" in result.stderr.lower():
                     iqtree_available = True
                     iqtree_cmd = candidate
@@ -177,73 +208,57 @@ def check_tool_availability():
 
 # --- Pipeline Functions ---
 def phylogenetic_placement(sequence: str, mafft_cmd: str, iqtree_cmd: str):
-    query_fasta = None
     try:
         if len(sequence.strip()) < 100:
             return False, "Sequence too short (<100 bp).", None, None
         query_id = f"QUERY_{uuid.uuid4().hex[:8]}"
         query_fasta = os.path.join(QUERY_OUTPUT_DIR, f"{query_id}.fa")
         aligned_with_query = os.path.join(QUERY_OUTPUT_DIR, f"{query_id}_aligned.fa")
-        output_prefix = os.path.join(QUERY_OUTPUT_DIR, f"{query_id}_")
+        output_prefix = os.path.join(QUERY_OUTPUT_DIR, f"{query_id}_placed_tree")
         if not os.path.exists(ALIGNMENT_PATH) or not os.path.exists(TREE_PATH):
-            logger.error(f"Reference alignment or tree not found: {ALIGNMENT_PATH}, {TREE_PATH}")
             return False, "Reference alignment or tree not found.", None, None
-        logger.debug(f"Writing query FASTA to: {query_fasta}")
         query_record = SeqRecord(Seq(sequence.upper()), id=query_id, description="")
-        SeqIO.write(query_fasta, [query_fasta], "write([query_record])")
-        logger.debug("Running MAFFT alignment...")
-        with open(aligned_with_query, "subprocess") as subprocess:
+        SeqIO.write([query_record], query_fasta, "fasta")
+        with open(aligned_with_query, "w") as output_file:
             subprocess.run([
-                open, mafft_cmd, "--add", "--reorder", "subprocess.PIPEALIGNMENT_PATH",
-                query_f, aligned_with_query, query_fasta
-            ], "subprocess.PIPE=stdout", text=True, timeout_ms=600000, check=True)
-        if not os.path.exists("aligned_with_query") or not os.path.getsize(aligned_with_query):
-            logger.error(f"MAFFT alignment failed: {aligned_with_query}")
+                mafft_cmd, "--add", query_fasta, "--reorder", ALIGNMENT_PATH
+            ], stdout=output_file, stderr=subprocess.PIPE, text=True, timeout=600, check=True)
+        if not os.path.exists(aligned_with_query) or os.path.getsize(aligned_with_query) == 0:
             return False, "MAFFT alignment failed.", None, None
-        logger.debug("Running IQ-TREE placement...")
         subprocess.run([
             iqtree_cmd, "-s", aligned_with_query, "-g", TREE_PATH,
             "-m", "GTR+G", "-pre", output_prefix, "-redo"
         ], capture_output=True, text=True, timeout=1200, check=True)
         treefile = f"{output_prefix}.treefile"
         if not os.path.exists(treefile):
-            logger.error(f"IQ-TREE placement failed: {treefile} not found")
             return False, "IQ-TREE placement failed.", aligned_with_query, None
         success_msg = f"Placement completed!\nQuery ID: {query_id}\nAlignment: {os.path.basename(aligned_with_query)}\nTree: {os.path.basename(treefile)}"
-        logger.info(success_msg)
         return True, success_msg, aligned_with_query, treefile
     except Exception as e:
         logger.error(f"Phylogenetic placement failed: {e}", exc_info=True)
         return False, f"Error: {str(e)}", None, None
     finally:
-        if query_fasta and os.path.exists(query_fasta):
+        if 'query_fasta' in locals() and os.path.exists(query_fasta):
             try:
                 os.unlink(query_fasta)
-                logger.debug(f"Cleaned up {query_fasta}")
-            except Exception as e:
-                logger.warning(f"Failed to clean up {query_fasta}: {e}", exc_info=True)
+            except Exception as cleanup_error:
+                logger.warning(f"Failed to clean up {query_fasta}: {cleanup_error}")
 
 def analyze_sequence_for_tree(sequence: str, matching_percentage: float):
     try:
         logger.debug("Starting tree analysis...")
         if not analyzer:
-            logger.error("Tree analyzer not initialized")
             return "❌ Tree analyzer not initialized.", None, None
-        logger.debug("Validating sequence...")
         if not sequence or len(sequence.strip()) < 10:
-            logger.error("Invalid sequence: too short or empty")
             return "❌ Invalid sequence.", None, None
         if not (1 <= matching_percentage <= 99):
-            logger.error(f"Invalid matching percentage: {matching_percentage}")
             return "❌ Matching percentage must be 1-99.", None, None
         logger.debug("Finding query sequence...")
         if not analyzer.find_query_sequence(sequence):
-            logger.error("Sequence not accepted by analyzer")
             return "❌ Sequence not accepted.", None, None
         logger.debug("Finding similar sequences...")
         matched_ids, actual_percentage = analyzer.find_similar_sequences(matching_percentage)
         if not matched_ids:
-            logger.warning(f"No similar sequences found at {matching_percentage}% threshold")
             return f"❌ No similar sequences at {matching_percentage}% threshold.", None, None
         logger.debug("Building tree structure...")
         analyzer.build_tree_structure_with_ml_safe(matched_ids)
@@ -257,7 +272,7 @@ def analyze_sequence_for_tree(sequence: str, matching_percentage: float):
         logger.debug("Generating detailed report...")
         report_success = analyzer.generate_detailed_report(matched_ids, actual_percentage)
         report_html_path = os.path.join("/tmp", f'detailed_report_{query_id}.html') if report_success else None
-        logger.debug(f"Tree analysis completed: {len(matched_ids)} matches at {actual_percentage:.2f}%")
+        logger.debug(f"Tree analysis completed: {len(matched_ids)} matches")
         return f"✅ Found {len(matched_ids)} sequences at {actual_percentage:.2f}% similarity.", tree_html_path, report_html_path
     except Exception as e:
         logger.error(f"Tree analysis failed: {e}", exc_info=True)
@@ -265,22 +280,16 @@ def analyze_sequence_for_tree(sequence: str, matching_percentage: float):
 
 def predict_with_keras(sequence):
     try:
-        logger.debug("Starting Keras prediction...")
         if not keras_model or not kmer_to_index:
-            logger.error("Keras model or kmer index not available")
             return "❌ Keras model not available."
         if len(sequence) < 6:
-            logger.error("Sequence too short for Keras prediction")
             return "❌ Sequence too short (<6 bp)."
-        logger.debug("Generating kmers...")
         kmers = [sequence[i:i+6] for i in range(len(sequence)-5)]
         indices = [kmer_to_index.get(kmer, 0) for kmer in kmers]
         input_arr = np.array([indices])
-        logger.debug("Running Keras prediction...")
         prediction = keras_model.predict(input_arr, verbose=0)[0]
         f_gene_prob = prediction[-1]
         percentage = min(100, max(0, int(f_gene_prob * 100 + 5)))
-        logger.debug(f"Keras prediction completed: {percentage}% confidence")
         return f"✅ {percentage}% F gene confidence"
     except Exception as e:
         logger.error(f"Keras prediction failed: {e}", exc_info=True)
@@ -288,9 +297,7 @@ def predict_with_keras(sequence):
 
 def read_fasta_file(file_obj):
     try:
-        logger.debug("Reading FASTA file...")
         if file_obj is None:
-            logger.error("No file object provided")
             return ""
         if isinstance(file_obj, str):
             with open(file_obj, "r") as f:
@@ -299,26 +306,20 @@ def read_fasta_file(file_obj):
             content = file_obj.read().decode("utf-8")
         lines = content.strip().split("\n")
         seq_lines = [line.strip() for line in lines if not line.startswith(">")]
-        sequence = ''.join(seq_lines)
-        logger.debug(f"FASTA file read successfully: {len(sequence)} bp")
-        return sequence
+        return ''.join(seq_lines)
     except Exception as e:
         logger.error(f"Failed to read FASTA file: {e}", exc_info=True)
         return ""
 
 def run_pipeline(dna_input, similarity_score=95.0, build_ml_tree=False):
     try:
-        logger.debug("Starting pipeline...")
         dna_input = dna_input.upper().strip()
         if not dna_input:
-            logger.error("Empty input sequence")
             return "❌ Empty input", "", "", "", "", None, None, None, None, "No input", "No input", None, None
         if not re.match('^[ACTGN]+$', dna_input):
-            logger.debug("Cleaning invalid characters from input")
             dna_input = ''.join(c if c in 'ACTGN' else 'N' for c in dna_input)
         processed_sequence = dna_input
         boundary_output = ""
-        logger.debug("Running boundary detection...")
         if boundary_model:
             try:
                 result = boundary_model.predict_sequence(dna_input)
@@ -326,25 +327,19 @@ def run_pipeline(dna_input, similarity_score=95.0, build_ml_tree=False):
                 if regions:
                     processed_sequence = regions[0]["sequence"]
                     boundary_output = f"✅ F gene region found: {len(processed_sequence)} bp"
-                    logger.debug(f"Boundary detection: F gene found, {len(processed_sequence)} bp")
                 else:
                     boundary_output = "⚠️ No F gene regions found."
                     processed_sequence = dna_input
-                    logger.debug("Boundary detection: No F gene regions found")
             except Exception as e:
                 boundary_output = f"❌ Boundary prediction error: {str(e)}"
                 processed_sequence = dna_input
-                logger.error(f"Boundary prediction error: {e}", exc_info=True)
         else:
             boundary_output = f"⚠️ Boundary model not available. Using full input: {len(dna_input)} bp"
-            logger.warning("Boundary model not available")
-        logger.debug("Running Keras validation...")
         keras_output = predict_with_keras(processed_sequence) if processed_sequence and len(processed_sequence) >= 6 else "❌ Sequence too short."
         aligned_file = None
         phy_file = None
         ml_tree_output = ""
         if build_ml_tree and processed_sequence and len(processed_sequence) >= 100:
-            logger.debug("Running phylogenetic placement...")
             try:
                 mafft_available, iqtree_available, mafft_cmd, iqtree_cmd = check_tool_availability()
                 if mafft_available and iqtree_available:
@@ -352,53 +347,41 @@ def run_pipeline(dna_input, similarity_score=95.0, build_ml_tree=False):
                     ml_tree_output = ml_message
                     aligned_file = ml_aligned
                     phy_file = ml_tree
-                    logger.debug(f"Phylogenetic placement: {ml_message}")
                 else:
                     ml_tree_output = "❌ MAFFT or IQ-TREE not available"
-                    logger.error("MAFFT or IQ-TREE not available")
             except Exception as e:
                 ml_tree_output = f"❌ ML tree error: {str(e)}"
-                logger.error(f"ML tree error: {e}", exc_info=True)
         elif build_ml_tree:
             ml_tree_output = "❌ Sequence too short for placement (<100 bp)."
-            logger.error("Sequence too short for phylogenetic placement")
         else:
             ml_tree_output = "⚠️ Phylogenetic placement skipped."
-            logger.debug("Phylogenetic placement skipped")
         tree_html_content = "No tree generated."
         report_html_content = "No report generated."
         tree_html_path = None
         report_html_path = None
         simplified_ml_output = ""
         if analyzer and processed_sequence and len(processed_sequence) >= 10:
-            logger.debug("Running tree analysis...")
             try:
                 tree_result, tree_html_path, report_html_path = analyze_sequence_for_tree(processed_sequence, similarity_score)
                 simplified_ml_output = tree_result
                 if tree_html_path and os.path.exists(tree_html_path):
                     with open(tree_html_path, 'r', encoding='utf-8') as f:
                         tree_html_content = f.read()
-                    logger.debug(f"Tree HTML generated: {tree_html_path}")
                 else:
                     tree_html_content = f"<div style='color: red;'>{tree_result}</div>"
-                    logger.debug("No tree HTML generated")
                 if report_html_path and os.path.exists(report_html_path):
                     with open(report_html_path, 'r', encoding='utf-8') as f:
                         report_html_content = f.read()
-                    logger.debug(f"Report HTML generated: {report_html_path}")
                 else:
                     report_html_content = f"<div style='color: red;'>{tree_result}</div>"
-                    logger.debug("No report HTML generated")
             except Exception as e:
                 simplified_ml_output = f"❌ Tree analysis error: {str(e)}"
                 tree_html_content = f"<div style='color: red;'>{simplified_ml_output}</div>"
                 report_html_content = f"<div style='color: red;'>{simplified_ml_output}</div>"
-                logger.error(f"Tree analysis error: {e}", exc_info=True)
         else:
             simplified_ml_output = "❌ Tree analyzer not available." if not analyzer else "❌ Sequence too short (<10 bp)."
             tree_html_content = f"<div style='color: orange;'>{simplified_ml_output}</div>"
             report_html_content = f"<div style='color: orange;'>{simplified_ml_output}</div>"
-            logger.error(simplified_ml_output)
         summary_output = f"""
 📊 ANALYSIS SUMMARY:
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
@@ -409,7 +392,6 @@ Placement: {'✅ OK' if '✅' in ml_tree_output else '⚠️ Skipped' if 'skippe
 Tree Analysis: {'✅ OK' if 'Found' in simplified_ml_output else '❌ Failed'}
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 """
-        logger.debug("Pipeline completed successfully")
         return (
             boundary_output, keras_output, ml_tree_output, simplified_ml_output, summary_output,
             aligned_file, phy_file, None, None, tree_html_content, report_html_content,
@@ -423,9 +405,7 @@ Tree Analysis: {'✅ OK' if 'Found' in simplified_ml_output else '❌ Failed'}
 async def run_pipeline_from_file(fasta_file_obj, similarity_score, build_ml_tree):
     temp_file_path = None
     try:
-        logger.debug("Starting pipeline from file...")
         if fasta_file_obj is None:
-            logger.error("No file provided")
             return "❌ No file provided", "", "", "", "", None, None, None, None, "No input", "No input", None, None
         with tempfile.NamedTemporaryFile(delete=False, suffix=".fasta", dir="/tmp") as temp_file:
             if isinstance(fasta_file_obj, UploadFile):
@@ -436,12 +416,9 @@ async def run_pipeline_from_file(fasta_file_obj, similarity_score, build_ml_tree
                     content = f.read()
                 temp_file.write(content)
             temp_file_path = temp_file.name
-        logger.debug(f"Reading FASTA file: {temp_file_path}")
         dna_input = read_fasta_file(temp_file_path)
         if not dna_input:
-            logger.error("Failed to read FASTA file")
             return "❌ Failed to read FASTA file", "", "", "", "", None, None, None, None, "No input", "No input", None, None
-        logger.debug("Running pipeline with FASTA input...")
         return run_pipeline(dna_input, similarity_score, build_ml_tree)
     except Exception as e:
         logger.error(f"Pipeline from file error: {e}", exc_info=True)
@@ -451,9 +428,8 @@ async def run_pipeline_from_file(fasta_file_obj, similarity_score, build_ml_tree
         if temp_file_path and os.path.exists(temp_file_path):
             try:
                 os.unlink(temp_file_path)
-                logger.debug(f"Cleaned up temp file: {temp_file_path}")
             except Exception as e:
-                logger.warning(f"Failed to delete temp file {temp_file_path}: {e}", exc_info=True)
+                logger.warning(f"Failed to delete temp file {temp_file_path}: {e}")
 
 # --- Pydantic Models ---
 class AnalysisRequest(BaseModel):
@@ -515,9 +491,7 @@ async def health_check():
 @app.post("/analyze", response_model=AnalysisResponse)
 async def analyze_sequence(request: AnalysisRequest):
     try:
-        logger.debug("Starting sequence analysis via API...")
         result = run_pipeline(request.sequence, request.similarity_score, request.build_ml_tree)
-        logger.debug("API analysis completed")
         return AnalysisResponse(
             boundary_output=result[0] or "",
             keras_output=result[1] or "",
@@ -545,13 +519,11 @@ async def analyze_file(
 ):
     temp_file_path = None
     try:
-        logger.debug("Starting file analysis via API...")
         with tempfile.NamedTemporaryFile(delete=False, suffix=".fasta", dir="/tmp") as temp_file:
             content = await file.read()
             temp_file.write(content)
             temp_file_path = temp_file.name
         result = await run_pipeline_from_file(temp_file_path, similarity_score, build_ml_tree)
-        logger.debug("API file analysis completed")
         return AnalysisResponse(
             boundary_output=result[0] or "",
             keras_output=result[1] or "",
@@ -574,23 +546,18 @@ async def analyze_file(
         if temp_file_path and os.path.exists(temp_file_path):
             try:
                 os.unlink(temp_file_path)
-                logger.debug(f"Cleaned up API temp file: {temp_file_path}")
             except Exception as e:
-                logger.warning(f"Failed to clean up {temp_file_path}: {e}", exc_info=True)
+                logger.warning(f"Failed to clean up {temp_file_path}: {e}")
 
 @app.get("/download/{file_type}/{query_id}")
 async def download_file(file_type: str, query_id: str):
     try:
-        logger.debug(f"Downloading file: {file_type}/{query_id}")
         if file_type not in ["tree", "report"]:
-            logger.error(f"Invalid file type: {file_type}")
             raise HTTPException(status_code=400, detail="Invalid file type. Use 'tree' or 'report'.")
         file_name = f"phylogenetic_tree_{query_id}.html" if file_type == "tree" else f"detailed_report_{query_id}.html"
         file_path = os.path.join("/tmp", file_name)
         if not os.path.exists(file_path):
-            logger.error(f"File not found: {file_path}")
             raise HTTPException(status_code=404, detail="File not found.")
-        logger.debug(f"Serving file: {file_path}")
         return FileResponse(file_path, filename=file_name, media_type="text/html")
     except Exception as e:
         logger.error(f"Download error: {e}", exc_info=True)
@@ -599,7 +566,6 @@ async def download_file(file_type: str, query_id: str):
 # --- Gradio Interface ---
 def create_gradio_interface():
     try:
-        logger.debug("Creating Gradio interface...")
         with gr.Blocks(
             title="🧬 Gene Analysis Pipeline",
             theme=gr.themes.Soft(),
@@ -630,53 +596,63 @@ def create_gradio_interface():
                         with gr.Column(scale=2):
                             dna_input = gr.Textbox(
                                 label="🧬 DNA Sequence",
-                                placeholder="Enter DNA sequence (ATCG format)...",
+                                placeholder="Enter your DNA sequence (ATCG format)...",
                                 lines=5,
-                                description="Paste your DNA sequence here"
+                                max_lines=10
                             )
                         with gr.Column(scale=1):
-                            similarity_score = gr.Slider(
+                            similarity_slider = gr.Slider(
                                 minimum=1,
                                 maximum=99,
-                                value=95.0,
-                                step=1.0,
+                                value=95,
+                                step=1,
                                 label="🎯 Similarity Threshold (%)",
-                                description="Minimum similarity for tree analysis"
+                                info="Minimum similarity for phylogenetic analysis"
                             )
                             build_ml_tree = gr.Checkbox(
-                                label="🌲 Build ML Tree",
+                                label="🌳 Build ML Tree",
                                 value=False,
-                                description="Generate phylogenetic placement (slower)"
+                                info="Perform phylogenetic placement (slower)"
+                            )
+                            analyze_btn = gr.Button(
+                                "🔬 Analyze Sequence",
+                                variant="primary",
+                                size="lg"
                             )
-                            analyze_btn = gr.Button("🔬 Analyze Sequence", variant="primary")
+
                 with gr.TabItem("📁 File Upload"):
                     with gr.Row():
                         with gr.Column(scale=2):
                             file_input = gr.File(
                                 label="📄 Upload FASTA File",
                                 file_types=[".fasta", ".fa", ".fas", ".txt"],
-                                description="Upload a FASTA file containing your sequence"
+                                type="filepath"
                             )
                         with gr.Column(scale=1):
-                            file_similarity_score = gr.Slider(
+                            file_similarity_slider = gr.Slider(
                                 minimum=1,
                                 maximum=99,
-                                value=95.0,
-                                step=1.0,
-                                label="🎯 Similarity Threshold (%)",
-                                description="Minimum similarity for tree analysis"
+                                value=95,
+                                step=1,
+                                label="🎯 Similarity Threshold (%)"
                             )
                             file_build_ml_tree = gr.Checkbox(
-                                label="🌲 Build ML Tree",
-                                value=False,
-                                description="Generate phylogenetic placement (slower)"
+                                label="🌳 Build ML Tree",
+                                value=False
+                            )
+                            file_analyze_btn = gr.Button(
+                                "🔬 Analyze File",
+                                variant="primary",
+                                size="lg"
                             )
-                            analyze_file_btn = gr.Button("🔬 Analyze File", variant="primary")
+
+            # Results Section
             gr.Markdown("## 📊 Analysis Results")
+            
             with gr.Row():
                 with gr.Column():
                     boundary_output = gr.Textbox(
-                        label="🎯 Boundary Detection",
+                        label="🎯 Boundary Prediction",
                         interactive=False,
                         lines=2
                     )
@@ -687,145 +663,203 @@ def create_gradio_interface():
                     )
                 with gr.Column():
                     ml_tree_output = gr.Textbox(
-                        label="🌲 Phylogenetic Placement",
+                        label="🌳 Phylogenetic Placement",
                         interactive=False,
                         lines=2
                     )
                     tree_analysis_output = gr.Textbox(
-                        label="🌳 Tree Analysis",
+                        label="📈 Tree Analysis",
                         interactive=False,
                         lines=2
                     )
+
             summary_output = gr.Textbox(
                 label="📋 Summary",
                 interactive=False,
                 lines=8
             )
-            with gr.Row():
-                aligned_file = gr.File(label="📄 Alignment File", visible=False)
-                tree_file = gr.File(label="🌲 Tree File", visible=False)
-                tree_html_file = gr.File(label="🌳 Simplified Tree HTML", visible=False)
-                report_html_file = gr.File(label="📊 Detailed Report HTML", visible=False)
+
+            # Interactive Visualizations
             with gr.Tabs():
-                with gr.TabItem("🌳 Interactive Tree"):
+                with gr.TabItem("🌳 Phylogenetic Tree"):
                     tree_html = gr.HTML(
-                        value="<div style='text-align: center; color: #666; padding: 20px;'>No tree generated yet. Run analysis to see results.</div>"
+                        label="Interactive Tree Visualization",
+                        value="<div style='text-align: center; padding: 20px; color: #666;'>Run analysis to see phylogenetic tree</div>"
                     )
+                    
                 with gr.TabItem("📊 Detailed Report"):
                     report_html = gr.HTML(
-                        value="<div style='text-align: center; color: #666; padding: 20px;'>No report generated yet. Run analysis to see results.</div>"
+                        label="Comprehensive Analysis Report",
+                        value="<div style='text-align: center; padding: 20px; color: #666;'>Run analysis to see detailed report</div>"
                     )
 
-            # Event handlers
-            def handle_analysis_output(*outputs):
-                boundary_output, keras_output, ml_tree_output, simplified_ml_output, summary_output, aligned_file, phy_file, _, _, tree_html_content, report_html_content, tree_html_path, report_html_path = outputs
-                logger.debug("Handling Gradio output...")
+            # Download Section
+            with gr.Row():
+                with gr.Column():
+                    aligned_file = gr.File(
+                        label="📄 Download Alignment",
+                        visible=False
+                    )
+                    tree_file = gr.File(
+                        label="🌳 Download Tree File",
+                        visible=False
+                    )
+                    
+            # Event Handlers
+            def process_text_input(dna_seq, similarity, build_tree):
+                if not dna_seq or not dna_seq.strip():
+                    return (
+                        "❌ Please enter a DNA sequence", "", "", "", "",
+                        None, None, 
+                        "<div style='color: red;'>No input provided</div>",
+                        "<div style='color: red;'>No input provided</div>"
+                    )
+                
+                results = run_pipeline(dna_seq, similarity, build_tree)
                 return (
-                    boundary_output, keras_output, ml_tree_output, simplified_ml_output, summary_output,
-                    gr.File.update(value=aligned_file, visible=aligned_file is not None),
-                    gr.File.update(value=phy_file, visible=phy_file is not None),
-                    gr.File.update(value=tree_html_path, visible=tree_html_path is not None),
-                    gr.File.update(value=report_html_path, visible=report_html_path is not None),
-                    tree_html_content,
-                    report_html_content
+                    results[0], results[1], results[2], results[3], results[4],
+                    results[5], results[6], results[9], results[10]
                 )
 
+            def process_file_input(file_path, similarity, build_tree):
+                if not file_path:
+                    return (
+                        "❌ Please upload a file", "", "", "", "",
+                        None, None,
+                        "<div style='color: red;'>No file provided</div>",
+                        "<div style='color: red;'>No file provided</div>"
+                    )
+                
+                # Read the FASTA file
+                try:
+                    sequence = read_fasta_file(file_path)
+                    if not sequence:
+                        return (
+                            "❌ Failed to read sequence from file", "", "", "", "",
+                            None, None,
+                            "<div style='color: red;'>Invalid file format</div>",
+                            "<div style='color: red;'>Invalid file format</div>"
+                        )
+                    
+                    results = run_pipeline(sequence, similarity, build_tree)
+                    return (
+                        results[0], results[1], results[2], results[3], results[4],
+                        results[5], results[6], results[9], results[10]
+                    )
+                except Exception as e:
+                    error_msg = f"❌ Error processing file: {str(e)}"
+                    return (
+                        error_msg, "", "", "", "",
+                        None, None,
+                        f"<div style='color: red;'>{error_msg}</div>",
+                        f"<div style='color: red;'>{error_msg}</div>"
+                    )
+
+            # Wire up the event handlers
             analyze_btn.click(
-                fn=run_pipeline,
-                inputs=[dna_input, similarity_score, build_ml_tree],
+                fn=process_text_input,
+                inputs=[dna_input, similarity_slider, build_ml_tree],
                 outputs=[
-                    boundary_output, keras_output, ml_tree_output, tree_analysis_output, summary_output,
-                    aligned_file, tree_file, tree_html_file, report_html_file, tree_html, report_html
-                ],
-                _js="""(outputs) => { return outputs; }"""
+                    boundary_output, keras_output, ml_tree_output, 
+                    tree_analysis_output, summary_output,
+                    aligned_file, tree_file, tree_html, report_html
+                ]
             )
 
-            analyze_file_btn.click(
-                fn=run_pipeline_from_file,
-                inputs=[file_input, file_similarity_score, file_build_ml_tree],
+            file_analyze_btn.click(
+                fn=process_file_input,
+                inputs=[file_input, file_similarity_slider, file_build_ml_tree],
                 outputs=[
-                    boundary_output, keras_output, ml_tree_output, tree_analysis_output, summary_output,
-                    aligned_file, tree_file, tree_html_file, report_html_file, tree_html, report_html
-                ],
-                _js="""(outputs) => { return outputs; }"""
+                    boundary_output, keras_output, ml_tree_output,
+                    tree_analysis_output, summary_output,
+                    aligned_file, tree_file, tree_html, report_html
+                ]
             )
 
-            gr.Examples(
-                examples=[
-                    ["ATCG" * 250, 85.0, False],
-                    ["CGATCG" * 150, 90.0, True]
-                ],
-                inputs=[dna_input, similarity_score, build_ml_tree],
-                label="Example Sequences"
-            )
+            # Example sequences for quick testing
+            gr.Markdown("### 🧪 Example Sequences")
+            with gr.Row():
+                example1_btn = gr.Button("Example 1: Short F Gene", size="sm")
+                example2_btn = gr.Button("Example 2: Full Length", size="sm")
+                clear_btn = gr.Button("🗑️ Clear", size="sm")
+
+            def load_example1():
+                return "ATGGAGTTGCTAATCCTCAAACTTCTGCTTGAAGGGTCACAGTACACACCCTGTGCAAAGAGACAAGCAACAATAATGATCTGGATTCGTACGACGTGGCTGAGGGGAACCTGTATGTGAACAGTCTAGCCAGAGGTTACTATGCAACGGTCACTAGGGCCGGAATCCCTCCCAATGCACCAGGACGCTCTGATCACGTAAGACGAACTTACAGATCCAAAGTGGGAAACGGGGAACGGCTGGGTACCCTGAGACAGCCTGGACAAGACCTCAGGTGTCACATACGACGGGGACTATAATATGGACGCCTGCAGCGGTGGAACAAATAGCAACAGACCT"
+
+            def load_example2():
+                return "ATGGAGTTGCTAATCCTCAAACTTCTGCTTGAAGGGTCACAGTACACACCCTGTGCAAAGAGACAAGCAACAATAATGATCTGGATTCGTACGACGTGGCTGAGGGGAACCTGTATGTGAACAGTCTAGCCAGAGGTTACTATGCAACGGTCACTAGGGCCGGAATCCCTCCCAATGCACCAGGACGCTCTGATCACGTAAGACGAACTTACAGATCCAAAGTGGGAAACGGGGAACGGCTGGGTACCCTGAGACAGCCTGGACAAGACCTCAGGTGTCACATACGACGGGGACTATAATATGGACGCCTGCAGCGGTGGAACAAATAGCAACAGACCTCATGTGGGCAGTGGCCACAATCTACAATTTGGATACAGTGGAATTTGGAGAAGCGACCTTCAGAACCTGGGTCATGGTGCCGTCCTACGGTGGGGCCGCCGAAGCAACTCTCGACTACGTGGTGGAAAGCCTGGGCTTCGGAGGCGCAGTTATCGGAAAAAGCAAAGAACTCACAGGAAAGCTGTTCAAGAACGACACCTACTATGGAAAGATGGGTCACTATCTAAAAATTGATTCCTGTACCAGCCAACTTTAA"
+
+            def clear_inputs():
+                return ""
+
+            example1_btn.click(fn=load_example1, outputs=dna_input)
+            example2_btn.click(fn=load_example2, outputs=dna_input)
+            clear_btn.click(fn=clear_inputs, outputs=dna_input)
 
-            gr.Markdown("""
-            ## 📚 Instructions
-            1. **Input**: Enter a DNA sequence (ATCG format) or upload a FASTA file
-            2. **Parameters**: 
-               - Set similarity threshold for phylogenetic analysis (1-99%)
-               - Choose whether to build ML tree (slower but more accurate)
-            3. **Analysis**: Click analyze to run the complete pipeline
-            4. **Results**: View results in different tabs - summary, tree visualization, and detailed report
-            5. **Downloads**: Download alignment, tree, simplified tree HTML, and detailed report HTML files
-            ### 🔬 Pipeline Components:
-            - **Boundary Detection**: Identifies F gene regions
-            - **F Gene Validation**: Validates F gene using ML
-            - **Phylogenetic Placement**: Places sequence in reference tree (optional)
-            - **Tree Analysis**: Builds phylogenetic tree with similar sequences
-            """)
-
-        logger.debug("Gradio interface created successfully")
         return iface
+
     except Exception as e:
-        logger.error(f"Gradio interface creation failed: {e}", exc_info=True)
+        logger.error(f"Failed to create Gradio interface: {e}", exc_info=True)
+        # Fallback simple interface
         return gr.Interface(
-            fn=lambda x: f"Error: {str(e)}",
-            inputs=gr.Textbox(label="DNA Sequence"),
-            outputs=gr.Textbox(label="Error"),
-            title="🧬 Gene Analysis Pipeline (Error Mode)"
+            fn=lambda x: f"Interface creation failed: {str(e)}",
+            inputs=gr.Textbox(label="Input"),
+            outputs=gr.Textbox(label="Output"),
+            title="🧬 Gene Analysis Pipeline - Error"
         )
 
-# --- Application Startup ---
-def run_application():
+# Create the Gradio interface
+gradio_app = create_gradio_interface()
+
+# Mount Gradio app to FastAPI
+@app.get("/gradio", response_class=HTMLResponse)
+async def gradio_interface():
+    """Serve the Gradio interface"""
     try:
-        logger.debug("Starting application...")
-        gradio_app = create_gradio_interface()
-        gradio_app = gr.mount_gradio_app(app, gradio_app, path="/gradio")
-        logger.info("🚀 Starting Gene Analysis Pipeline...")
-        logger.info("📊 FastAPI docs available at: http://localhost:7860/docs")
-        logger.info("🧬 Gradio interface available at: http://localhost:7860/gradio")
-        uvicorn.run(
-            app,
-            host="0.0.0.0",
-            port=7860,
-            log_level="info"
-        )
+        # Generate the Gradio app HTML
+        return gradio_app.launch(prevent_thread_lock=True, share=False, show_error=True)
     except Exception as e:
-        logger.error(f"Application startup failed: {e}", exc_info=True)
-        try:
-            logger.info("🔄 Falling back to Gradio-only mode...")
-            gradio_app = create_gradio_interface()
+        logger.error(f"Failed to serve Gradio interface: {e}", exc_info=True)
+        return HTMLResponse(f"""
+        <html>
+            <head><title>🧬 Gene Analysis Pipeline - Error</title></head>
+            <body>
+                <h1>🧬 Gene Analysis Pipeline</h1>
+                <p style="color: red;">Failed to load Gradio interface: {str(e)}</p>
+                <p>Please try using the API endpoints instead:</p>
+                <ul>
+                    <li><a href="/docs">API Documentation</a></li>
+                    <li><a href="/health">Health Check</a></li>
+                </ul>
+            </body>
+        </html>
+        """)
+
+# --- Main Application Runner ---
+if __name__ == "__main__":
+    try:
+        logger.info("🚀 Starting Gene Analysis Pipeline...")
+        
+        # Check if running in Gradio Spaces
+        if os.getenv("SPACE_ID"):
+            logger.info("🌐 Running in Hugging Face Spaces - Gradio mode")
             gradio_app.launch(
                 server_name="0.0.0.0",
                 server_port=7860,
-                share=False,
-                debug=False
+                share=True,
+                show_error=True,
+                enable_queue=True
             )
-        except Exception as fallback_error:
-            logger.error(f"Fallback failed: {fallback_error}", exc_info=True)
-            print("❌ Application failed to start. Check logs for details.")
-
-# --- Main Entry Point ---
-if __name__ == "__main__":
-    print("🧬 Gene Analysis Pipeline Starting...")
-    print("=" * 50)
-    print("🔍 Checking system components...")
-    mafft_available, iqtree_available, _, _ = check_tool_availability()
-    print(f"🤖 Boundary Model: {'✅' if boundary_model else '❌'}")
-    print(f"🧠 Keras Model: {'✅' if keras_model else '❌'}")
-    print(f"🌳 Tree Analyzer: {'✅' if analyzer else '❌'}")
-    print(f"🧬 MAFFT: {'✅' if mafft_available else '❌'}")
-    print(f"🌲 IQ-TREE: {'✅' if iqtree_available else '❌'}")
-    print("=" * 50)
-    run_application()
+        else:
+            logger.info("🔧 Running in local/server mode - FastAPI + Gradio")
+            # Run FastAPI with Gradio mounted
+            uvicorn.run(
+                app,
+                host="0.0.0.0",
+                port=int(os.getenv("PORT", 7860)),
+                log_level="info"
+            )
+            
+    except Exception as e:
+        logger.error(f"❌ Failed to start application: {e}", exc_info=True)
+        sys.exit(1)