Update app.py

app.py

@@ -150,12 +150,12 @@ def count_words_and_tokens(text):
     tokens = len(AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3").tokenize(text))
     return words, tokens
 
+
 @spaces.GPU(duration=150)
 def process_input(input_file):
     start_time = time.time()
     
-    progress_info = "Processing file..."
-    yield progress_info, None, None, None, None, None, None, None, None
+    yield 0, "Processing file...", None, None, None, None, None, None, None, None
     
     file_extension = os.path.splitext(input_file.name)[1].lower()
     
@@ -176,8 +176,7 @@ def process_input(input_file):
         temp_video_path = "temp_video" + file_extension
         shutil.copy2(input_file.name, temp_video_path)
         
-        progress_info = "Transcribing video..."
-        yield progress_info, None, None, None, None, None, None, None, temp_video_path
+        yield 0.2, "Transcribing video...", None, None, None, None, None, None, None, temp_video_path
         
         language = "en"  # Default to English for video files
         diarization.process_video(temp_video_path, hf_token, language)
@@ -189,21 +188,24 @@ def process_input(input_file):
         input_info = f"Video transcribed. Words: {words}, Tokens: {tokens}"
         video_path = temp_video_path
     else:
-        return "Unsupported file format. Please upload a TXT, PDF, or video file.", None, None, None, None, None, None, None, None
+        yield 1, "Unsupported file format. Please upload a TXT, PDF, or video file.", None, None, None, None, None, None, None, None
+        return
 
     detected_language = detect_language(content)
     
-    progress_info = "Analyzing content..."
-    yield progress_info, None, detected_language, input_info, None, None, None, None, video_path
+    yield 0.4, "Analyzing content...", None, detected_language, input_info, None, None, None, None, video_path
     
     attachments_chain, bigfive_chain, personalities_chain = lazy_chains.get_chains()
     
+    yield 0.6, "Analyzing attachments...", None, detected_language, input_info, None, None, None, None, video_path
     attachments_result = attachments_chain({"query": content})
     attachments_answer = attachments_result['result'].split("-----------\n\nAnswer:")[-1].strip()
     
+    yield 0.7, "Analyzing Big Five traits...", None, detected_language, input_info, attachments_answer, None, None, None, video_path
     bigfive_result = bigfive_chain({"query": content})
     bigfive_answer = bigfive_result['result'].split("-----------\n\nAnswer:")[-1].strip()
     
+    yield 0.8, "Analyzing personalities...", None, detected_language, input_info, attachments_answer, bigfive_answer, None, None, video_path
     personalities_result = personalities_chain({"query": content})
     personalities_answer = personalities_result['result'].split("-----------\n\nAnswer:")[-1].strip()
     
@@ -212,9 +214,7 @@ def process_input(input_file):
     
     execution_info = f"{execution_time:.2f} seconds"
     
-    progress_info = "Analysis complete!"
-    
-    yield progress_info, execution_info, detected_language, input_info, attachments_answer, bigfive_answer, personalities_answer, video_path
+    yield 1, "Analysis complete!", execution_info, detected_language, input_info, attachments_answer, bigfive_answer, personalities_answer, video_path
 
 def create_interface():
     with gr.Blocks() as iface:
@@ -225,7 +225,8 @@ def create_interface():
             input_file = gr.File(label="Upload File (TXT, PDF, or Video)")
         
         with gr.Column():
-            progress = gr.Textbox(label="Progress")
+            progress = gr.Slider(label="Progress", minimum=0, maximum=1, value=0, interactive=False)
+            progress_text = gr.Textbox(label="Status")
             execution_time = gr.Textbox(label="Execution Time", visible=False)
             detected_language = gr.Textbox(label="Detected Language", visible=False)
             input_info = gr.Textbox(label="Input Information", visible=False)
@@ -234,11 +235,28 @@ def create_interface():
             bigfive_output = gr.Textbox(label="Big Five Results", visible=False)
             personalities_output = gr.Textbox(label="Personalities Results", visible=False)
 
+        def update_output(progress_value, progress_text, execution_time, detected_lang, input_info, attachments, bigfive, personalities, video):
+            return {
+                progress: progress_value,
+                progress_text: progress_text,
+                execution_time: gr.update(value=execution_time, visible=execution_time is not None),
+                detected_language: gr.update(value=detected_lang, visible=detected_lang is not None),
+                input_info: gr.update(value=input_info, visible=input_info is not None),
+                attachments_output: gr.update(value=attachments, visible=attachments is not None),
+                bigfive_output: gr.update(value=bigfive, visible=bigfive is not None),
+                personalities_output: gr.update(value=personalities, visible=personalities is not None),
+                video_output: gr.update(value=video, visible=video is not None)
+            }
+
         input_file.upload(
             fn=process_input,
             inputs=[input_file],
-            outputs=[progress, execution_time, detected_language, input_info, attachments_output, bigfive_output, personalities_output, video_output],
+            outputs=[progress, progress_text, execution_time, detected_language, input_info, attachments_output, bigfive_output, personalities_output, video_output],
             show_progress=True
+        ).then(
+            fn=update_output,
+            inputs=[progress, progress_text, execution_time, detected_language, input_info, attachments_output, bigfive_output, personalities_output, video_output],
+            outputs=[progress, progress_text, execution_time, detected_language, input_info, attachments_output, bigfive_output, personalities_output, video_output]
         )
 
     return iface