Update app.py

app.py

@@ -3,68 +3,112 @@ import pandas as pd
 from datetime import datetime
 import json
 from transformers import pipeline
+import logging
+import os
+
+# Configure logging for debugging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 # Load Hugging Face summarization model
-summarizer = pipeline("text2text-generation", model="google/flan-t5-base")
+try:
+    summarizer = pipeline("text2text-generation", model="google/flan-t5-base")
+    logging.info("Hugging Face model loaded successfully")
+except Exception as_abi0 as e:
+    logging.error(f"Failed to load model: {str(e)}")
+    raise e
 
 # Sample rule-based anomaly detector
 def detect_anomalies(df):
     anomalies = []
-    for _, row in df.iterrows():
-        if row.get("usage_hours", 0) > 10:  # Example threshold
-            anomalies.append({
-                "device_id": row["device_id"],
-                "issue": "Usage spike",
-                "detected_on": row["timestamp"].split("T")[0],
-                "severity": "high"
-            })
+    try:
+        for _, row in df.iterrows():
+            usage_hours = row.get("usage_hours", 0)
+            if isinstance(usage_hours, (int, float)) and usage_hours > 10:  # Example threshold
+                anomalies.append({
+                    "device_id": row["device_id"],
+                    "issue": "Usage spike",
+                    "detected_on": row["timestamp"].split("T")[0] if isinstance(row["timestamp"], str) else str(row["timestamp"]),
+                    "severity": "high"
+                })
+    except Exception as e:
+        logging.error(f"Anomaly detection failed: {str(e)}")
+        return []
     return anomalies
 
 # Format summary prompt and generate report
 def summarize_logs(df, lab_name, start_date, end_date):
-    # Simple aggregation
-    total_devices = df["device_id"].nunique()
-    avg_uptime = "97%"  # Placeholder
-    most_used = df.groupby("device_id")["usage_hours"].sum().idxmax()
-    downtime_events = 3  # Placeholder
+    try:
+        total_devices = df["device_id"].nunique()
+        avg_uptime = "97%"  # Placeholder
+        most_used = df.groupby("device_id")["usage_hours"].sum().idxmax()
+        downtime_events = 3  # Placeholder
 
-    prompt = (
-        f"Summarize maintenance and usage logs for lab {lab_name} "
-        f"from {start_date} to {end_date}. "
-        f"There were {total_devices} devices. "
-        f"The most used device was {most_used}."
-    )
-    summary = summarizer(prompt, max_length=200, do_sample=False)[0]["generated_text"]
-    return summary
+        prompt = (
+            f"Summarize maintenance and usage logs for lab {lab_name} "
+            f"from {start_date} to {end_date}. "
+            f"There were {total_devices} devices. "
+            f"The most used device was {most_used}."
+        )
+        summary = summarizer(prompt, max_length=200, do_sample=False)[0]["generated_text"]
+        logging.info("Summary generated successfully")
+        return summary
+    except Exception as e:
+        logging.error(f"Summary generation failed: {str(e)}")
+        return "Failed to generate summary."
 
 # Main Gradio function
 def process_logs(file_obj, lab_site, start_date, end_date):
     try:
-        df = pd.read_json(file_obj.name) if file_obj.name.endswith(".json") else pd.read_csv(file_obj.name)
-    except Exception as e:
-        return f"Failed to read file: {str(e)}", None, None
+        if file_obj is None:
+            logging.warning("No file uploaded, returning empty results")
+            return "No file uploaded.", [], "No data to preview."
+        
+        # Read file based on extension
+        file_name = file_obj.name if hasattr(file_obj, 'name') else file_obj
+        logging.info(f"Processing file: {file_name}")
+        
+        if file_name.endswith(".json"):
+            df = pd.read_json(file_name)
+        elif file_name.endswith(".csv"):
+            df = pd.read_csv(file_name)
+        else:
+            logging.error("Unsupported file format")
+            return "Unsupported file format. Please upload a CSV or JSON file.", [], None
+
+        logging.info(f"File loaded successfully with {len(df)} rows")
 
-    anomalies = detect_anomalies(df)
-    summary = summarize_logs(df, lab_site, start_date, end_date)
+        anomalies = detect_anomalies(df)
+        summary = summarize_logs(df, lab_site, start_date, end_date)
+        preview = df.head().to_markdown() if not df.empty else "No data available."
 
-    return summary, anomalies, df.head().to_markdown()
+        return summary, anomalies, preview
+    except Exception as e:
+        logging.error(f"Failed to process file: {str(e)}")
+        return f"Failed to process file: {str(e)}", [], None
 
 # Gradio Interface
 iface = gr.Interface(
     fn=process_logs,
     inputs=[
-        gr.File(label="Upload Logs (CSV or JSON)"),
-        gr.Textbox(label="Lab Site"),
-        gr.Textbox(label="Start Date (YYYY-MM-DD)"),
-        gr.Textbox(label="End Date (YYYY-MM-DD)")
+        gr.File(label="Upload Logs (CSV or JSON)", file_types=[".csv", ".json"]),
+        gr.Textbox(label="Lab Site", placeholder="e.g., Lab A"),
+        gr.Textbox(label="Start Date (YYYY-MM-DD)", placeholder="e.g., 2025-01-01"),
+        gr.Textbox(label="End Date (YYYY-MM-DD)", placeholder="e.g., 2025-01-31")
     ],
     outputs=[
         gr.Textbox(label="Summary Report"),
         gr.JSON(label="Anomalies"),
         gr.Markdown(label="Preview of Logs")
     ],
-    title="LabOps Log Analyzer (Hugging Face AI)"
+    title="LabOps Log Analyzer (Hugging Face AI)",
+    description="Upload a CSV or JSON file containing lab equipment logs to analyze usage and detect anomalies."
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    try:
+        logging.info("Launching Gradio interface")
+        # Launch with debug mode for local testing
+        iface.launch(server_name="0.0.0.0", server_port=7860, debug=True)
+    except Exception as e:
+        logging.error(f"Failed to launch Gradio interface: {str(e)}")
+        print(f"Error launching app: {str(e)}")