Update app.py

app.py

@@ -1,35 +1,74 @@
 import gradio as gr
-from transformers import pipeline
-import numpy as np
-import moviepy.editor as mp
-
-transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
-translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")  # Example for English to French
-
-def transcribe(video_file):
-    # Load video file and extract audio
-    audio_file = mp.AudioFileClip(video_file).write_audiofile("temp_audio.wav")
-    result = transcriber("temp_audio.wav")
-    return result['text']
-
-def translate(text):
-    return translator(text)[0]['translation_text']
-
-with gr.Blocks() as demo:
-    gr.Markdown("# Curify Studio Demo")
-    
-    with gr.Tab("Transcription"):
-        video_input = gr.File(label="Upload Video File")
-        transcribe_output = gr.Textbox(label="Transcription Output", lines=10)
-        transcribe_button = gr.Button("Transcribe")
-        
-        transcribe_button.click(fn=transcribe, inputs=video_input, outputs=transcribe_output)
-        
-    with gr.Tab("Translation"):
-        text_input = gr.Textbox(label="Text to Translate")
-        translate_output = gr.Textbox(label="Translation Output", lines=10)
-        translate_button = gr.Button("Translate")
+from datetime import datetime
+import random
+
+# Mock functions for platform actions and analytics
+def mock_post_to_platform(platform, content_title):
+    return f"Content '{content_title}' successfully posted on {platform}!"
+
+def mock_analytics():
+    return {
+        "YouTube": {"Views": random.randint(1000, 5000), "Engagement Rate": f"{random.uniform(5, 15):.2f}%"},
+        "Instagram": {"Views": random.randint(500, 3000), "Engagement Rate": f"{random.uniform(10, 20):.2f}%"},
+    }
+
+# Core functionalities
+def upload_and_manage(file, platform, language):
+    if file is None:
+        return "Please upload a video/audio file.", None, None, None
+
+    # Mock transcription and translation process
+    transcription = "This is a sample transcription of the uploaded content."
+    translation = f"[{language}] This is a sample translation of the content."
+
+    # Mock posting action
+    post_message = mock_post_to_platform(platform, file.name)
+
+    # Mock analytics generation
+    analytics = mock_analytics()
+
+    return post_message, transcription, translation, analytics
+
+def generate_dashboard(analytics):
+    if not analytics:
+        return "No analytics available."
+
+    dashboard = "Platform Analytics:\n"
+    for platform, data in analytics.items():
+        dashboard += f"\n{platform}:\n"
+        for metric, value in data.items():
+            dashboard += f"  {metric}: {value}\n"
+    return dashboard
+
+# Gradio Interface with Tabs
+def build_interface():
+    with gr.Blocks() as demo:
+        with gr.Tab("Content Management"):
+            gr.Markdown("## Integrated Content Management")
+            with gr.Row():
+                file_input = gr.File(label="Upload Video/Audio File")
+                platform_input = gr.Dropdown(["YouTube", "Instagram"], label="Select Platform")
+                language_input = gr.Dropdown(["English", "Spanish", "French", "Chinese"], label="Select Language")
+            
+            submit_button = gr.Button("Post and Process")
+            
+            with gr.Row():
+                post_output = gr.Textbox(label="Posting Status", interactive=False)
+                transcription_output = gr.Textbox(label="Transcription", interactive=False)
+                translation_output = gr.Textbox(label="Translation", interactive=False)
+
+            submit_button.click(upload_and_manage, 
+                                inputs=[file_input, platform_input, language_input], 
+                                outputs=[post_output, transcription_output, translation_output, gr.State()])
         
-        translate_button.click(fn=translate, inputs=text_input, outputs=translate_output)
+        with gr.Tab("Analytics Dashboard"):
+            gr.Markdown("## Content Performance Analytics")
+            analytics_output = gr.Textbox(label="Dashboard", interactive=False)
+            generate_dashboard_button = gr.Button("Generate Dashboard")
+
+            generate_dashboard_button.click(generate_dashboard, inputs=[gr.State()], outputs=[analytics_output])
+
+    return demo
 
-demo.launch(debug=True)
+demo = build_interface()
+demo.launch()