Update app.py

app.py

@@ -317,7 +317,7 @@ def translate_text(text_prompt, target_language):
 def openai_chat_avatar(text_prompt):
     response = client.chat.completions.create(
         model="gpt-4o-mini",
-        messages=[{"role": "system", "content": "Answer using the minimum words you can ever use."},
+        messages=[{"role": "system", "content": "Answer in Portuguese language always using the minimum words you can ever use."},
             {"role": "user", "content": f"Hi! I need help with something. Can you assist me with the following: {text_prompt}"},
         ],
         max_tokens = len(text_prompt) + 300 # Use the length of the input text
@@ -381,7 +381,7 @@ def generate_video():
     try:
         if request.method == 'POST':
             # source_image = request.files['source_image']
-            image_path = '/home/user/app/images/vibhu2.jpg'
+            image_path = '/home/user/app/images/marc.png'
             source_image = Image.open(image_path)
             text_prompt = request.form['text_prompt']
             
@@ -508,93 +508,67 @@ def generate_video():
                     ref_pose_video.save(ref_pose_video_path)
                     print('ref_pose_video_path',ref_pose_video_path)
                     
-    
-        # Example of using the class with some hypothetical paths
-        args = AnimationConfig(driven_audio_path=driven_audio_path, source_image_path=source_image_path, result_folder=result_folder, pose_style=pose_style, expression_scale=expression_scale,enhancer=enhancer,still=still,preprocess=preprocess,ref_pose_video_path=ref_pose_video_path, image_hardcoded=image_hardcoded)
-            
-        if torch.cuda.is_available() and not args.cpu:
-            args.device = "cuda"
-        else:
-            args.device = "cpu"
-            
-        generation_thread = threading.Thread(target=main, args=(args,))
-        app.config['generation_thread'] = generation_thread
-        generation_thread.start()
-        # response_data = {"message": "Video generation started",
-        #                 "process_id": generation_thread.ident}
-    
-        # return jsonify(response_data)
-    
-        while generation_thread.is_alive():
-            if app.config.get('temp_response'):
-                final_response = app.config['temp_response']
-                response_data = {
-                    "base64_video": final_response,
-                    "text_prompt": app.config.get('text_prompt'),
-                    "duration": app.config.get('final_video_duration'),
-                    "status": "completed"
-                }
-                final_video_path = app.config['final_video_path']
-                print('final_video_path', final_video_path)
-    
-                if final_video_path and os.path.exists(final_video_path):
-                    os.remove(final_video_path)
-                    print("Deleted video file:", final_video_path)
-    
-                preprocess_dir = os.path.join("/tmp", "preprocess_data")
-                custom_cleanup(TEMP_DIR.name, preprocess_dir)
-    
-                end_time = time.time()
-                total_time = round(end_time - start_time, 2)
-                print("Total time taken for execution:", total_time, " seconds")
-                response_data["time_taken"] = total_time
-    
-                return jsonify(response_data)
-
     except Exception as e:
         app.logger.error(f"An error occurred: {e}")
         return "An error occurred", 500
+    
+    # Example of using the class with some hypothetical paths
+    args = AnimationConfig(driven_audio_path=driven_audio_path, source_image_path=source_image_path, result_folder=result_folder, pose_style=pose_style, expression_scale=expression_scale,enhancer=enhancer,still=still,preprocess=preprocess,ref_pose_video_path=ref_pose_video_path, image_hardcoded=image_hardcoded)
+        
+    if torch.cuda.is_available() and not args.cpu:
+        args.device = "cuda"
+    else:
+        args.device = "cpu"
+        
+    generation_thread = threading.Thread(target=main, args=(args,))
+    app.config['generation_thread'] = generation_thread
+    generation_thread.start()
+    response_data = {"message": "Video generation started",
+                    "process_id": generation_thread.ident}
 
-# @app.route("/status", methods=["GET"])
-# def check_generation_status():
-#     global TEMP_DIR
-#     global start_time
-#     response = {"base64_video": "","text_prompt":"", "status": ""}
-#     process_id = request.args.get('process_id', None)
-
-#     # process_id is required to check the status for that specific process
-#     if process_id:
-#         generation_thread = app.config.get('generation_thread')
-#         if generation_thread and generation_thread.ident == int(process_id) and generation_thread.is_alive():
-#             return jsonify({"status": "in_progress"}), 200
-#         elif app.config.get('temp_response'):
-#             # app.config['temp_response']['status'] = 'completed'
-#             final_response = app.config['temp_response']
-#             response["base64_video"] = final_response
-#             response["text_prompt"] = app.config.get('text_prompt')
-#             response["duration"] = app.config.get('final_video_duration')
-#             response["status"] = "completed"
-
-#             final_video_path = app.config['final_video_path']
-#             print('final_video_path',final_video_path)
-
-
-#             if final_video_path and os.path.exists(final_video_path):
-#                 os.remove(final_video_path)
-#                 print("Deleted video file:", final_video_path)
-
-#             # TEMP_DIR.cleanup()
-#             preprocess_dir = os.path.join("/tmp", "preprocess_data")
-#             custom_cleanup(TEMP_DIR.name, preprocess_dir)
-
-#             print("Temporary files cleaned up, but preprocess_data is retained.")
+    return jsonify(response_data)
+
+
+@app.route("/status", methods=["GET"])
+def check_generation_status():
+    global TEMP_DIR
+    global start_time
+    response = {"base64_video": "","text_prompt":"", "status": ""}
+    process_id = request.args.get('process_id', None)
+
+    # process_id is required to check the status for that specific process
+    if process_id:
+        generation_thread = app.config.get('generation_thread')
+        if generation_thread and generation_thread.ident == int(process_id) and generation_thread.is_alive():
+            return jsonify({"status": "in_progress"}), 200
+        elif app.config.get('temp_response'):
+            # app.config['temp_response']['status'] = 'completed'
+            final_response = app.config['temp_response']
+            response["base64_video"] = final_response
+            response["text_prompt"] = app.config.get('text_prompt')
+            response["duration"] = app.config.get('final_video_duration')
+            response["status"] = "completed"
+
+            final_video_path = app.config['final_video_path']
+            print('final_video_path',final_video_path)
+
+
+            if final_video_path and os.path.exists(final_video_path):
+                os.remove(final_video_path)
+                print("Deleted video file:", final_video_path)
+
+            # TEMP_DIR.cleanup()
+            preprocess_dir = os.path.join("/tmp", "preprocess_data")
+            custom_cleanup(TEMP_DIR.name, preprocess_dir)
+
+            print("Temporary files cleaned up, but preprocess_data is retained.")
             
-#             end_time = time.time()
-#             total_time = round(end_time - start_time, 2)
-#             print("Total time taken for execution:", total_time, " seconds")
-#             response["time_taken"] = total_time
-#             return jsonify(response)
-#     return jsonify({"error":"No process id provided"})
+            end_time = time.time()
+            total_time = round(end_time - start_time, 2)
+            print("Total time taken for execution:", total_time, " seconds")
+            response["time_taken"] = total_time
+            return jsonify(response)
+    return jsonify({"error":"No process id provided"})
 
 @app.route("/health", methods=["GET"])
 def health_status():