Update app.py

app.py

@@ -1,3 +1,74 @@
+import gradio as gr
+from llm_loader import load_model
+from processing import process_input
+from transcription_diarization import diarize_audio
+from visualization import create_charts
+import time
+from config import openai_api_key
+
+# Load the model
+llm = load_model(openai_api_key)
+
+def analyze_video(video_path, max_speakers, progress=gr.Progress()):
+    start_time = time.time()
+
+    if not video_path:
+        return [gr.Markdown("Please upload a video file.")] + [gr.update(visible=False)] * (max_speakers * 7 + max_speakers) + ["Analysis not started."]
+
+    # Start the progress bar
+    progress(0, desc="Starting analysis...")
+
+    # Progress for transcription and diarization
+    progress(0.2, desc="Starting transcription and diarization...")
+    transcription = diarize_audio(video_path, max_speakers)
+    progress(0.6, desc="Transcription and diarization complete.")
+
+    # Progress for processing the transcription
+    progress(0.7, desc="Processing transcription...")
+    results = process_input(transcription, llm)
+    progress(0.8, desc="Transcription processing complete.")
+
+    # Progress for creating charts
+    progress(0.9, desc="Generating charts...")
+    charts, explanations = create_charts(results)
+    progress(1.0, desc="Charts generation complete.")
+
+    end_time = time.time()
+    execution_time = end_time - start_time
+
+    # Prepare outputs for each speaker
+    output_components = []
+    
+    for speaker_id in range(max_speakers):
+        speaker_charts = charts.get(speaker_id, {})
+        speaker_explanations = explanations.get(speaker_id, {})
+
+        # Check and assign plot outputs
+        attachment_plot = speaker_charts.get("attachment", None)
+        dimensions_plot = speaker_charts.get("dimensions", None)
+        bigfive_plot = speaker_charts.get("bigfive", None)
+        personality_plot = speaker_charts.get("personality", None)
+
+        speaker_section = [
+            gr.Plot(value=attachment_plot if attachment_plot else None, visible=True),
+            gr.Textbox(value=speaker_explanations.get("attachment", ""), label="Attachment Styles Explanation", visible=True),
+            gr.Plot(value=dimensions_plot if dimensions_plot else None, visible=True),
+            gr.Plot(value=bigfive_plot if bigfive_plot else None, visible=True),
+            gr.Textbox(value=speaker_explanations.get("bigfive", ""), label="Big Five Traits Explanation", visible=True),
+            gr.Plot(value=personality_plot if personality_plot else None, visible=True),
+            gr.Textbox(value=speaker_explanations.get("personality", ""), label="Personality Disorders Explanation", visible=True),
+        ]
+
+        output_components.extend(speaker_section)
+
+    # Add the transcript at the end
+    output_components.append(gr.Textbox(value=transcription, label="Transcript", lines=10, visible=True))
+
+    # Add the execution time
+    output_components.append(gr.Textbox(value=f"Completed in {int(execution_time)} seconds.", label="Execution Information", visible=True))
+
+    return [gr.update(visible=True)] * (max_speakers * 7 + max_speakers) + output_components[-2:]  # Making tabs and contents visible after analysis
+
 # Define the Gradio interface
 with gr.Blocks() as iface:
     gr.Markdown("# AI Personality Detection")
@@ -12,27 +83,41 @@ with gr.Blocks() as iface:
     execution_info_box = gr.Textbox(label="Execution Information", value="Waiting for analysis...", lines=1)
     
     # Create placeholders for output components
-    output_components = []
-    
-    for i in range(3):
-        with gr.Tab(f"Speaker {i}"):
+    def create_output_components(num_speakers):
+        output_components = []
+        for i in range(4):
             with gr.TabItem("Attachment Styles"):
-                output_components.extend([
-                    gr.Plot(visible=False),
-                    gr.Plot(visible=False),
-                    gr.Textbox(label="Explanation", visible=False)])
-
-            with gr.TabItem("Big Five Traits"):
-                output_components.extend([
-                    gr.Plot(visible=False),
-                    gr.Textbox(label="Explanation", visible=False)])
-                        
-            with gr.TabItem("Personalities"):
-                output_components.extend([
-                    gr.Plot(visible=False),
-                    gr.Textbox(label="Explanation", visible=False)])
-            
-    output_components.append(gr.Textbox(label="Transcript", lines=10, visible=False))
+                    output_components.extend([
+                        gr.Plot(visible=False),
+                        gr.Textbox(label="Attachment Styles Explanation", visible=False)])
+
+                with gr.TabItem("Big Five Traits"):
+                    output_components.extend([
+                        gr.Plot(visible=False),
+                        gr.Textbox(label="Big Five Traits Explanation", visible=False)])
+                            
+                with gr.TabItem("Personalities"):
+                    output_components.extend([
+                        gr.Plot(visible=False),
+                        gr.Textbox(label="Personality Disorders Explanation", visible=False)])
+        
+        output_components.append(gr.Textbox(label="Transcript", lines=10, visible=False))
+        output_components.append(gr.Textbox(label="Execution Information", visible=True))
+
+        return output_components
+
+    # Initialize with default number of speakers
+    output_components = create_output_components(2)
+
+    # Update the output components when the slider changes
+    def update_interface(max_speakers):
+        return create_output_components(max_speakers)
+
+    max_speakers.change(
+        fn=update_interface,
+        inputs=max_speakers,
+        outputs=output_components
+    )
 
     analyze_button.click(
         fn=analyze_video,
@@ -43,4 +128,4 @@ with gr.Blocks() as iface:
 
 # Launch the app
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()