Update app.py

app.py

@@ -47,7 +47,7 @@ def encode_image(image):
 def resize_image(image, max_size=(800, 800)):
     """Resize image to avoid exceeding the API size limits."""
     try:
-        image.thumbnail(max_size, Image.Resampling.LANCZOS)  # Use LANCZOS resampling for better quality
+        image.thumbnail(max_size, Image.Resampling.LANCZOS)
         return image
     except Exception as e:
         logger.error(f"Error resizing image: {str(e)}")
@@ -70,29 +70,29 @@ def extract_frames_from_video(video, frame_points=[0, 0.5, 1], max_size=(800, 80
     cap.release()
     return frames
 
-def analyze_file(file):
-    """Analyze a single file (image or video)"""
+def detect_snags(file):
+    """Detect snags in a single file (image or video)"""
     try:
         file_type = file.name.split('.')[-1].lower()
         if file_type in ['jpg', 'jpeg', 'png', 'bmp']:
-            return analyze_image(file)
+            return detect_snags_in_image(file)
         elif file_type in ['mp4', 'avi', 'mov', 'webm']:
-            return analyze_video(file)
+            return detect_snags_in_video(file)
         else:
             return "Unsupported file type. Please upload an image or video file."
     except Exception as e:
-        logger.error(f"Error analyzing file: {str(e)}")
-        return f"Error analyzing file: {str(e)}"
+        logger.error(f"Error detecting snags: {str(e)}")
+        return f"Error detecting snags: {str(e)}"
 
-def analyze_image(image_file):
+def detect_snags_in_image(image_file):
     image = Image.open(image_file.name)
     resized_image = resize_image(image)
     image_data_url = f"data:image/png;base64,{encode_image(resized_image)}"
     
-    instruction = ("You are an AI assistant specialized in analyzing images for safety issues. "
-                   "Your task is first to explain what you see in the image and determine if the image shows a construction site. "
-                   "If it does, identify any safety issues or hazards, categorize them, and provide a detailed description, "
-                   "and suggest steps to resolve them. If it's not a construction site, simply state that")
+    instruction = ("You are an AI assistant specialized in detecting snags in construction sites. "
+                   "Your task is to analyze the image and identify any construction defects, unfinished work, "
+                   "or quality issues. List each snag, categorize it, and provide a brief description. "
+                   "If no snags are detected, state that the area appears to be free of visible issues.")
 
     messages = [
         {
@@ -100,7 +100,7 @@ def analyze_image(image_file):
             "content": [
                 {
                     "type": "text",
-                    "text": f"{instruction}\n\nAnalyze this image. First, determine if it's a construction site. If it is, explain the image in detail, focusing on safety aspects. If it's not, briefly describe what you see."
+                    "text": f"{instruction}\n\nAnalyze this image for construction snags and provide a detailed report."
                 },
                 {
                     "type": "image_url",
@@ -124,14 +124,14 @@ def analyze_image(image_file):
     
     return completion.choices[0].message.content
 
-def analyze_video(video_file):
+def detect_snags_in_video(video_file):
     frames = extract_frames_from_video(video_file.name)
     results = []
     
-    instruction = ("You are an AI assistant specialized in analyzing images for safety issues. "
-                   "Your task is first to explain what you see in the image and determine if the image shows a construction site. "
-                   "If it does, identify any safety issues or hazards, categorize them, and provide a detailed description, "
-                   "and suggest steps to resolve them. If it's not a construction site, simply state that")
+    instruction = ("You are an AI assistant specialized in detecting snags in construction sites. "
+                   "Your task is to analyze the video frame and identify any construction defects, unfinished work, "
+                   "or quality issues. List each snag, categorize it, and provide a brief description. "
+                   "If no snags are detected, state that the area appears to be free of visible issues.")
 
     for i, frame in enumerate(frames):
         image_data_url = f"data:image/png;base64,{encode_image(frame)}"
@@ -141,7 +141,7 @@ def analyze_video(video_file):
                 "content": [
                     {
                         "type": "text",
-                        "text": f"{instruction}\n\nAnalyze this frame from a video (Frame {i+1}/{len(frames)}). First, explain the video and then determine if it's a construction site. If it is, explain what you observe, focusing on safety aspects. If it's not, briefly describe what you see."
+                        "text": f"{instruction}\n\nAnalyze this frame from a video (Frame {i+1}/{len(frames)}) for construction snags and provide a detailed report."
                     },
                     {
                         "type": "image_url",
@@ -165,26 +165,20 @@ def analyze_video(video_file):
     
     return "\n".join(results)
 
-
-        
-def chat_about_image(message, chat_history):
+def chat_about_snags(message, chat_history):
     try:
-        # Prepare the conversation history for the API
         messages = [
-            {"role": "system", "content": "You are an AI assistant specialized in analyzing construction site images and answering questions about them. Use the information from the initial analysis to answer user queries."},
+            {"role": "system", "content": "You are an AI assistant specialized in analyzing construction site snags and answering questions about them. Use the information from the initial analysis to answer user queries."},
         ]
         
-        # Add chat history to messages
         for human, ai in chat_history:
             if human:
                 messages.append({"role": "user", "content": human})
             if ai:
                 messages.append({"role": "assistant", "content": ai})
         
-        # Add the new user message
         messages.append({"role": "user", "content": message})
         
-        # Make API call
         completion = client.chat.completions.create(
             model="llama-3.2-90b-vision-preview",
             messages=messages,
@@ -203,11 +197,11 @@ def chat_about_image(message, chat_history):
         logger.error(f"Error during chat: {str(e)}")
         return "", chat_history + [(message, f"Error: {str(e)}")]
 
-def generate_summary_report(chat_history):
+def generate_snag_report(chat_history):
     """
-    Generate a summary report from the chat history.
+    Generate a snag report from the chat history.
     """
-    report = "Construction Site Safety Analysis Report\n"
+    report = "Construction Site Snag Detection Report\n"
     report += "=" * 40 + "\n"
     report += f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
 
@@ -220,22 +214,20 @@ def generate_summary_report(chat_history):
 
     return report
 
-def download_report(chat_history):
+def download_snag_report(chat_history):
     """
-    Generate and provide a download link for the summary report.
+    Generate and provide a download link for the snag report.
     """
-    report = generate_summary_report(chat_history)
+    report = generate_snag_report(chat_history)
     timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    filename = f"safety_analysis_report_{timestamp}.txt"
+    filename = f"snag_detection_report_{timestamp}.txt"
     
-    # Create a temporary file
     with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as temp_file:
         temp_file.write(report)
         temp_file_path = temp_file.name
     
     return temp_file_path
 
-
 # Custom CSS for improved styling
 custom_css = """
 .container { max-width: 1200px; margin: auto; padding-top: 1.5rem; }
@@ -269,14 +261,13 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as iface:
         """
         <div class="container">
             <div class="header">
-                <h1>🏗️ Construction Site Safety Analyzer</h1>
+                <h1>🔍 Construction Site Snag Detector</h1>
             </div>
-            <p class="subheader">Enhance workplace safety and compliance with AI-powered image and video analysis using Llama 3.2 90B Vision and expert chat assistance.</p>
+            <p class="subheader">Enhance quality control and project management with AI-powered snag detection using Llama 3.2 90B Vision and expert chat assistance.</p>
         </div>
         """
     )
     
-    # First row: Combined file upload for images and videos
     with gr.Row():
         file_input = gr.File(
             label="Upload Construction Site Images or Videos",
@@ -285,40 +276,35 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as iface:
             elem_classes="file-container"
         )
     
-    # Second row: Analyze Safety Hazards Button
     with gr.Row():
-        analyze_button = gr.Button("🔍 Analyze Safety Hazards", elem_classes="analyze-button")
+        analyze_button = gr.Button("🔍 Detect Snags", elem_classes="analyze-button")
     
-    # Third row: Chat Interface (Safety Analysis Results)
     with gr.Row():
         chatbot = gr.Chatbot(
-            label="Safety Analysis Results and Expert Chat",
+            label="Snag Detection Results and Expert Chat",
             elem_classes="chatbot",
-            show_share_button=False,  # Remove share button
-            show_copy_button=False    # Remove copy button
+            show_share_button=False,
+            show_copy_button=False
         )
     
-    # Fourth row: Question Bar
     with gr.Row():
         msg = gr.Textbox(
-            label="Ask about safety measures or regulations",
-            placeholder="E.g., 'Ask follow-up question and press ENTER'",
+            label="Ask about detected snags or quality issues",
+            placeholder="E.g., 'What are the most critical snags detected?'",
             show_label=False,
             elem_classes="chat-input"
         )
 
-    # Fifth row: Clear Chat and Download Report Buttons
     with gr.Row():
         clear = gr.Button("🗑️ Clear Chat", elem_classes="clear-button")
         download_button = gr.Button("📥 Download Report", elem_classes="download-button")
 
-    # Add a file component to handle the download
-    report_file = gr.File(label="Download Safety Analysis Report")
+    report_file = gr.File(label="Download Snag Detection Report")
 
     def process_files(files):
         results = []
         for file in files:
-            result = analyze_file(file)
+            result = detect_snags(file)
             results.append((file.name, result))
         return results
 
@@ -335,11 +321,11 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as iface:
         postprocess=lambda x: update_chat(chatbot.value, x)
     )
 
-    msg.submit(chat_about_image, [msg, chatbot], [msg, chatbot])
+    msg.submit(chat_about_snags, [msg, chatbot], [msg, chatbot])
     clear.click(lambda: None, None, chatbot, queue=False)
 
     download_button.click(
-        download_report,
+        download_snag_report,
         inputs=[chatbot],
         outputs=[report_file]
     )