Upload app.py

app.py

@@ -7,32 +7,53 @@ import threading
 
 # Load AI models
 def load_models():
-    models = {
-        "gpt-3": pipeline("text-generation", model="gpt-3"),
-        "bert-base-uncased": pipeline("text-classification", model="bert-base-uncased"),
-        "roberta-large": pipeline("text-classification", model="roberta-large"),
-        "distilbert-base-uncased": pipeline("text-classification", model="distilbert-base-uncased"),
-        "albert-base-v2": pipeline("text-classification", model="albert-base-v2"),
-        "tinybert": pipeline("text-classification", model="tinybert"),
-        "cybersecurity-bert": pipeline("text-classification", model="cybersecurity-bert"),
-        "malware-detection-bert": pipeline("text-classification", model="malware-detection-bert"),
-        "phishing-detection-bert": pipeline("text-classification", model="phishing-detection-bert")
-    }
+    models = {}
+    try:
+        # Text generation model (using smaller open source alternative)
+        models["gpt2"] = pipeline("text-generation", model="gpt2")
+        
+        # Classification models
+        models["bert-base"] = pipeline("text-classification", model="bert-base-uncased")
+        models["distilbert"] = pipeline("text-classification", model="distilbert-base-uncased")
+        
+        # Cybersecurity specific models
+        models["phishing-bert"] = pipeline(
+            "text-classification",
+            model="deepset/bert-base-cased-squad2"  # Using a QA model that can be fine-tuned for security
+        )
+        
+    except Exception as e:
+        print(f"Error loading models: {str(e)}")
+        # Fallback to at least one working model
+        models["distilbert"] = pipeline("text-classification", model="distilbert-base-uncased")
+    
     return models
 
-models = load_models()
-
 # Define functions to interact with AI models
 def analyze_text(text, model_name):
+    if not text.strip():
+        return "Please provide some text to analyze."
+        
     model = models.get(model_name)
-    if model:
-        return model(text)
-    else:
-        return "Model not found."
+    if not model:
+        return f"Model {model_name} not found. Available models: {', '.join(models.keys())}"
+    
+    try:
+        if model_name == "gpt2":
+            result = model(text, max_length=100, num_return_sequences=1)
+            return result[0]['generated_text']
+        else:
+            result = model(text)
+            return str(result)
+    except Exception as e:
+        return f"Error analyzing text: {str(e)}"
 
 def analyze_file(file, model_name):
-    content = file.read().decode("utf-8")
-    return analyze_text(content, model_name)
+    try:
+        content = file.read().decode("utf-8")
+        return analyze_text(content, model_name)
+    except Exception as e:
+        return f"Error processing file: {str(e)}"
 
 # Real-time monitoring and alerting
 alert_thresholds = {
@@ -42,42 +63,77 @@ alert_thresholds = {
 }
 
 def monitor_real_time_data(data_stream, model_name):
-    for data in data_stream:
-        result = analyze_text(data, model_name)
-        if result["score"] >= alert_thresholds.get(model_name, 0.8):
-            send_alert(result)
+    if not data_stream.strip():
+        return "Please provide a data stream URL or content."
+        
+    try:
+        # For demo purposes, we'll analyze the provided text as a single data point
+        result = analyze_text(data_stream, model_name)
+        return f"Monitoring result: {result}"
+    except Exception as e:
+        return f"Error monitoring data: {str(e)}"
 
-def send_alert(alert):
-    # Implement notification methods (e.g., email, SMS, in-app notifications)
-    print(f"Alert: {alert}")
+# Load models at startup
+models = load_models()
 
 # Gradio interface
-def gradio_interface():
+def create_gradio_interface():
     with gr.Blocks() as demo:
         gr.Markdown("# Cybersecurity AI Platform")
-
-        with gr.Tab("Text Input"):
-            text_input = gr.Textbox(label="Enter text for analysis")
-            model_dropdown = gr.Dropdown(choices=list(models.keys()), label="Select AI Model")
+        
+        with gr.Tab("Text Analysis"):
+            text_input = gr.Textbox(
+                label="Enter text for analysis",
+                placeholder="Enter text here..."
+            )
+            model_dropdown = gr.Dropdown(
+                choices=list(models.keys()),
+                value=list(models.keys())[0],
+                label="Select AI Model"
+            )
             text_output = gr.Textbox(label="Analysis Result")
             text_button = gr.Button("Analyze Text")
-            text_button.click(analyze_text, inputs=[text_input, model_dropdown], outputs=text_output)
+            text_button.click(
+                analyze_text,
+                inputs=[text_input, model_dropdown],
+                outputs=text_output
+            )
 
-        with gr.Tab("File Upload"):
+        with gr.Tab("File Analysis"):
             file_input = gr.File(label="Upload file for analysis")
-            model_dropdown = gr.Dropdown(choices=list(models.keys()), label="Select AI Model")
+            file_model_dropdown = gr.Dropdown(
+                choices=list(models.keys()),
+                value=list(models.keys())[0],
+                label="Select AI Model"
+            )
             file_output = gr.Textbox(label="Analysis Result")
             file_button = gr.Button("Analyze File")
-            file_button.click(analyze_file, inputs=[file_input, model_dropdown], outputs=file_output)
+            file_button.click(
+                analyze_file,
+                inputs=[file_input, file_model_dropdown],
+                outputs=file_output
+            )
 
-        with gr.Tab("Real-time Data Stream"):
-            data_stream_input = gr.Textbox(label="Enter real-time data stream URL")
-            model_dropdown = gr.Dropdown(choices=list(models.keys()), label="Select AI Model")
-            real_time_output = gr.Textbox(label="Real-time Monitoring Result")
-            real_time_button = gr.Button("Start Monitoring")
-            real_time_button.click(monitor_real_time_data, inputs=[data_stream_input, model_dropdown], outputs=real_time_output)
+        with gr.Tab("Real-time Monitoring"):
+            stream_input = gr.Textbox(
+                label="Enter data stream content",
+                placeholder="Enter data to monitor..."
+            )
+            stream_model_dropdown = gr.Dropdown(
+                choices=list(models.keys()),
+                value=list(models.keys())[0],
+                label="Select AI Model"
+            )
+            stream_output = gr.Textbox(label="Monitoring Result")
+            stream_button = gr.Button("Start Monitoring")
+            stream_button.click(
+                monitor_real_time_data,
+                inputs=[stream_input, stream_model_dropdown],
+                outputs=stream_output
+            )
 
-    demo.launch()
+    return demo
 
 if __name__ == "__main__":
-    gradio_interface()
+    demo = create_gradio_interface()
+    demo.launch()