updated completely with open source code

app.py

@@ -1,485 +1,206 @@
-# ==============================================================================
-# PitchPerfect AI: Enterprise-Grade Sales Coach (Single File Application)
-#
-# This single file contains the complete application code, enhanced with
-# YouTube support, JAX-based quantitative analysis, and a more robust
-# agentic architecture.
-# ==============================================================================
-
-# ==============================================================================
-# File: README.md (Instructions)
-# ==============================================================================
 """
-# PitchPerfect AI: Enterprise-Grade Sales Coach
-
-This application provides AI-powered feedback on sales pitches using Google's most advanced multimodal AI, all managed through the Vertex AI platform. It analyzes your content, vocal delivery, and visual presence to give you actionable insights for improvement.
-
-This advanced version includes:
-- Support for local video uploads and YouTube URLs.
-- Quantitative vocal analysis powered by JAX for high performance.
-- An agentic architecture where specialized tools (YouTube Downloader, JAX Analyzer) work in concert with the Gemini 1.5 Pro model.
-
-## 🔑 Prerequisites
-
-1.  A Google Cloud Platform (GCP) project with billing enabled.
-2.  The Vertex AI API and Cloud Storage API enabled in your GCP project.
-3.  The `gcloud` CLI installed and authenticated on your local machine.
+🚀 The Content Creator's Journey (AI-Powered Edition)
+==================================================
+This version replaces all mocked functions with real generative AI models
+for image analysis, content generation, and translation.
 
-## 셋업
+- Stage 1: Inspiration Hub (Summarization AI)
+- Stage 2: Creative Studio (Image-to-Text and Text Generation AI)
+- Stage 3: Globalization Suite (Translation AI)
 
-1.  **Create a Google Cloud Storage (GCS) Bucket:**
-    * In your GCP project, create a new GCS bucket. It must have a globally unique name.
-    * **Example name:** `your-project-id-pitch-videos`
-
-2.  **Authenticate with Google Cloud:**
-    Run the following command in your terminal and follow the prompts. This sets up Application Default Credentials (ADC).
-    ```bash
-    gcloud auth application-default login
-    ```
-    *Note: The user/principal needs `Storage Object Admin` and `Vertex AI User` roles.*
-
-3.  **Install Dependencies:**
-    Create a `requirements.txt` file with the content below and run `pip install -r requirements.txt`.
-    ```
-    gradio
-    google-cloud-aiplatform
-    google-cloud-storage
-    moviepy
-    # For JAX and Quantitative Analysis
-    jax
-    jaxlib
-    librosa
-    speechrecognition
-    openai-whisper
-    # For YouTube support
-    yt-dlp
-    ```
-
-4.  **Configure Project Details:**
-    * In this file, scroll down to the "CONFIGURATION" section.
-    * Set your `GCP_PROJECT_ID`, `GCP_LOCATION`, and `GCS_BUCKET_NAME`.
-
-5.  **Run the Application:**
-    ```bash
-    python app.py
-    ```
-    This will launch a Gradio web server. **Look for a public URL ending in `.gradio.live` in the output and open it in your browser.**
+Author: Gemini
+Date: July 4, 2025
 """
 
-# ==============================================================================
-# IMPORTS
-# ==============================================================================
-import logging
-import json
-import uuid
 import os
 import re
-import sys
-import subprocess
-from typing import Dict, Any, List
-
-# Setup Logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-
-
-
-def check_gcloud_auth():
-    """Checks for GCP authentication and provides instructions if missing."""
-    try:
-        # The 'gcloud auth print-access-token' command is a reliable way to check for active credentials.
-        # We capture stderr to suppress the verbose output on success.
-        subprocess.check_output("gcloud auth application-default print-access-token", shell=True, stderr=subprocess.DEVNULL)
-        logging.info("GCP authentication credentials found.")
-        return True
-    except subprocess.CalledProcessError:
-        error_message = """
-================================================================================
-CRITICAL ERROR: Google Cloud Authentication Not Found!
-================================================================================
-This application requires authentication to access Google Cloud services (Vertex AI, Cloud Storage).
-
-To fix this, please run the following command in your terminal and follow the prompts to log in with your Google account:
-
-    gcloud auth application-default login
-
-After authenticating, please restart this Python script.
-================================================================================
-"""
-        print(error_message, file=sys.stderr)
-        return False
-
-# Run the auth check before trying to import heavy libraries
-if not check_gcloud_auth():
-    sys.exit(1)
-
-
-
-from typing import Dict, Any
 import gradio as gr
-import vertexai
-from google.cloud import storage
-from vertexai.generative_models import (
-    GenerativeModel, Part, GenerationConfig,
-    HarmCategory, HarmBlockThreshold
-)
-
-# Third-party imports for advanced features
-import yt_dlp
-import librosa
-import numpy as np
-import whisper
-import jax
-import jax.numpy as jnp
-# from moviepy.editor import VideoFileClip
-from moviepy import VideoFileClip
-
-
-
-# ==============================================================================
-# CONFIGURATION
-# ==============================================================================
-# --- GCP and Vertex AI Configuration ---
-GCP_PROJECT_ID = "aniket-personal"
-GCP_LOCATION = "us-central1"
-
-# --- GCS Configuration ---
-GCS_BUCKET_NAME = "ghiblify"
-
-# --- Model Configuration ---
-MODEL_GEMINI_PRO = "gemini-1.5-pro-002"
-
-# --- Example Videos ---
-# These are publicly accessible videos for demonstration purposes.
-EXAMPLE_VIDEOS = [
-    ["Confident Business Presentation", "https://storage.googleapis.com/pitchperfect-ai-examples/business_pitch_example.mp4"],
-    ["Casual Tech Talk", "https://storage.googleapis.com/pitchperfect-ai-examples/tech_talk_example.mp4"],
-]
-
-# --- Schemas for Controlled Generation (as Dictionaries) ---
-FEEDBACK_ITEM_SCHEMA = {
-    "type": "object",
-    "properties": {
-        "score": {"type": "integer", "minimum": 1, "maximum": 10},
-        "feedback": {"type": "string"}
-    },
-    "required": ["score", "feedback"]
-}
-HOLISTIC_ANALYSIS_SCHEMA = {
-    "type": "object",
-    "properties": {
-        "content_analysis": {"type": "object", "properties": {"clarity": FEEDBACK_ITEM_SCHEMA, "structure": FEEDBACK_ITEM_SCHEMA, "value_proposition": FEEDBACK_ITEM_SCHEMA, "cta": FEEDBACK_ITEM_SCHEMA}},
-        "vocal_analysis": {"type": "object", "properties": {"pacing": FEEDBACK_ITEM_SCHEMA, "vocal_variety": FEEDBACK_ITEM_SCHEMA, "confidence_energy": FEEDBACK_ITEM_SCHEMA, "clarity_enunciation": FEEDBACK_ITEM_SCHEMA}},
-        "visual_analysis": {"type": "object", "properties": {"eye_contact": FEEDBACK_ITEM_SCHEMA, "body_language": FEEDBACK_ITEM_SCHEMA, "facial_expressions": FEEDBACK_ITEM_SCHEMA}}
-    },
-    "required": ["content_analysis", "vocal_analysis", "visual_analysis"]
-}
-FINAL_SYNTHESIS_SCHEMA = {
-    "type": "object",
-    "properties": {
-        "key_strengths": {"type": "string"},
-        "growth_opportunities": {"type": "string"},
-        "executive_summary": {"type": "string"}
-    },
-    "required": ["key_strengths", "growth_opportunities", "executive_summary"]
-}
-
-# --- Enhanced Prompts ---
-PROMPT_HOLISTIC_VIDEO_ANALYSIS = """
-You are an expert sales coach. Analyze the provided video and the supplementary quantitative metrics to generate a structured, holistic feedback report. Your output MUST strictly conform to the provided JSON schema, including the 1-10 score range.
-
-**Quantitative Metrics (for additional context):**
-{quantitative_metrics_json}
-
-**Evaluation Framework (Analyze the video directly):**
-1.  **Content & Structure:** Analyze clarity, flow, value proposition, and the call to action.
-2.  **Vocal Delivery:** Analyze pacing, vocal variety, confidence, energy, and enunciation. Use the quantitative metrics to inform your qualitative assessment.
-3.  **Visual Delivery:** Analyze eye contact, body language, and facial expressions.
-
-Provide specific examples from the video to support your points.
-"""
-
-PROMPT_FINAL_SYNTHESIS = """
-You are a senior executive coach. Synthesize the provided detailed analysis data into a high-level summary. Your output MUST strictly conform to the provided JSON schema.
+import arxiv
+import nltk
+from transformers import pipeline
+from youtube_transcript_api import YouTubeTranscriptApi, NoTranscriptFound
+from youtube_transcript_api.formatters import TextFormatter
+from PIL import Image
+
+# --- Model & Pipeline Setup (Lazy Loading) ---
+
+# Dictionary to hold our models, loaded only when needed.
+models = {}
+
+def get_pipeline(task, model_name):
+    """Initializes and returns a pipeline, caching it for reuse."""
+    if model_name not in models:
+        print(f"🔄 Initializing {task} pipeline with model {model_name}...")
+        models[model_name] = pipeline(task, model=model_name)
+        print(f"✅ {model_name} loaded.")
+    return models[model_name]
+
+# --- Stage 1: The Spark (Inspiration Hub) ---
+# This section already uses a real summarization model, so no changes are needed here.
+# (Functions search_arxiv_papers and summarize_youtube_from_url are omitted for brevity but remain the same)
+
+# ... (Previous code for Stage 1 remains here) ...
+
+# --- Stage 2: The Craft (Creative Studio) ---
+
+def analyze_image_with_ai(image: Image.Image) -> (str, dict):
+    """Uses a real AI model to generate a description of the image."""
+    captioner = get_pipeline("image-to-text", "Salesforce/blip-image-captioning-large")
+    description = captioner(image)[0]['generated_text']
+    
+    analysis = {"description": description}
+    report = (
+        f"**🎨 AI Vision Analysis:**\n\n"
+        f"- **Image Content:** {description}"
+    )
+    return report, analysis
+
+def generate_creative_content_with_ai(style: str, audience: str, image_analysis: dict, custom_prompt: str) -> (str, str):
+    """Uses a real LLM to generate content based on a detailed prompt."""
+    generator = get_pipeline("text-generation", "gpt2")
+    image_desc = image_analysis.get("description", "a visual scene")
+
+    # Create a detailed prompt for the LLM
+    prompt = (
+        f"Create a '{style}' for a '{audience}' audience. "
+        f"The content should be inspired by the following scene: '{image_desc}'. "
+        f"Follow this specific instruction: '{custom_prompt if custom_prompt else 'Be creative and engaging'}'.\n\n"
+        f"Here is the content:"
+    )
+    
+    # Generate text and clean it up
+    generated_outputs = generator(prompt, max_length=150, num_return_sequences=1, pad_token_id=generator.tokenizer.eos_token_id)
+    generated_text = generated_outputs[0]['generated_text']
+    
+    # Clean the output by removing the initial prompt
+    clean_text = generated_text.replace(prompt, "").strip()
+
+    # The analytics are now informational rather than predictive
+    analytics_report = (
+        f"**📊 Generation Details:**\n\n"
+        f"- **Model Used:** gpt2\n"
+        f"- **Core Prompt:** Based on a photo of '{image_desc[:40]}...'"
+    )
+    
+    return clean_text, analytics_report
 
-- "key_strengths" should be a single string with bullet points (e.g., "- Point one\\n- Point two").
-- "growth_opportunities" should be a single string, formatted similarly.
-- "executive_summary" should be a single string paragraph.
+def run_creative_studio(uploaded_image, style, audience, custom_prompt):
+    """Interface function to run the full AI-powered 'Craft' stage."""
+    if uploaded_image is None:
+        return "❌ Please upload an image.", "", ""
+    try:
+        image = uploaded_image
+        analysis_report, image_analysis = analyze_image_with_ai(image)
+        generated_text, analytics = generate_creative_content_with_ai(style, audience, image_analysis, custom_prompt)
+        return analysis_report, generated_text, analytics
+    except Exception as e:
+        return f"⚠️ Error: {e}", "", ""
 
-**Detailed Analysis Data:**
----
-{full_analysis_json}
----
-"""
+# --- Stage 3: The Reach (Globalization Suite) ---
 
-# ==============================================================================
-# AGENT TOOLKIT
-# ==============================================================================
-class YouTubeDownloaderTool:
-    """A tool to download a YouTube video to a local path."""
-    def run(self, url: str, output_dir: str = "temp_downloads") -> str:
-        if not os.path.exists(output_dir):
-            os.makedirs(output_dir)
+def translate_content_with_ai(text: str, languages: list) -> str:
+    """Translates content using real AI models."""
+    if not text:
+        return "❌ Please provide text to translate."
+    if not languages:
+        return "❌ Please select at least one language."
         
-        filepath = os.path.join(output_dir, f"{uuid.uuid4()}.mp4")
-        ydl_opts = {
-            'format': 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best',
-            'outtmpl': filepath,
-            'quiet': True,
-        }
-        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-            ydl.download([url])
-        return filepath
-
-class QuantitativeAudioTool:
-    """A tool for performing objective, numerical analysis on an audio track."""
-    class JAXAudioProcessor:
-        """A nested class demonstrating JAX for high-performance audio processing."""
-        def __init__(self):
-            self.jit_rms_energy = jax.jit(self._calculate_rms_energy)
-        @staticmethod
-        @jax.jit
-        def _calculate_rms_energy(waveform: jnp.ndarray) -> jnp.ndarray:
-            return jnp.sqrt(jnp.mean(jnp.square(waveform)))
-        def analyze_energy_variation(self, waveform_np):
-            if waveform_np is None or waveform_np.size == 0: return 0.0
-            waveform_jnp = jnp.asarray(waveform_np)
-            frame_length, hop_length = 2048, 512
-            num_frames = (waveform_jnp.shape[0] - frame_length) // hop_length
-            start_positions = jnp.arange(num_frames) * hop_length
-            offsets = jnp.arange(frame_length)
-            frame_indices = start_positions[:, None] + offsets[None, :]
-            frames = waveform_jnp[frame_indices]
-            frame_energies = jax.vmap(self.jit_rms_energy)(frames)
-            return float(jnp.std(frame_energies))
-
-    def __init__(self):
-        self.jax_processor = self.JAXAudioProcessor()
-        self.whisper_model = whisper.load_model("base.en")
-
-    def run(self, video_path: str, output_dir: str = "temp_output"):
-        if not os.path.exists(output_dir): os.makedirs(output_dir)
-        video = None
-        try:
-            video = VideoFileClip(video_path)
-            
-            if video.audio is None:
-                raise ValueError("The provided video file does not contain an audio track, or it could not be decoded. Analysis cannot proceed.")
-
-            audio_path = os.path.join(output_dir, f"audio_{uuid.uuid4()}.wav")
-            video.audio.write_audiofile(audio_path, codec='pcm_s16le', fps=16000)
-            
-            transcript_result = self.whisper_model.transcribe(audio_path, fp16=False)
-            word_count = len(transcript_result['text'].split())
-            duration = video.duration
-            pace = (word_count / duration) * 60 if duration > 0 else 0
-            
-            y, sr = librosa.load(audio_path, sr=16000)
-            energy_variation = self.jax_processor.analyze_energy_variation(y)
-            
-            os.remove(audio_path)
-            
-            return {
-                "speaking_pace_wpm": round(pace, 2),
-                "vocal_energy_variation": round(energy_variation, 4),
-            }
-        finally:
-            if video:
-                video.close()
-
-# ==============================================================================
-# VERTEX AI MANAGER CLASS
-# ==============================================================================
-class VertexAIManager:
-    def __init__(self):
-        vertexai.init(project=GCP_PROJECT_ID, location=GCP_LOCATION)
-        self.model = GenerativeModel(MODEL_GEMINI_PRO)
-
-    def run_multimodal_analysis(self, video_gcs_uri: str, prompt: str) -> dict:
-        video_part = Part.from_uri(uri=video_gcs_uri, mime_type="video/mp4")
-        contents = [video_part, prompt]
-        config = GenerationConfig(response_schema=HOLISTIC_ANALYSIS_SCHEMA, temperature=0.2, response_mime_type="application/json")
-        response = self.model.generate_content(contents, generation_config=config)
-        return json.loads(response.text)
-
-    def run_synthesis(self, prompt: str) -> dict:
-        config = GenerationConfig(response_schema=FINAL_SYNTHESIS_SCHEMA, temperature=0.3, response_mime_type="application/json")
-        response = self.model.generate_content(prompt, generation_config=config)
-        return json.loads(response.text)
-
-# ==============================================================================
-# AGENT CLASS
-# ==============================================================================
-class PitchAnalyzerAgent:
-    def __init__(self):
-        self.vertex_manager = VertexAIManager()
-        self.storage_client = storage.Client(project=GCP_PROJECT_ID)
-        self.youtube_tool = YouTubeDownloaderTool()
-        self.quant_tool = QuantitativeAudioTool()
-        self._check_bucket()
-
-    def _check_bucket(self):
-        self.storage_client.get_bucket(GCS_BUCKET_NAME)
-
-    def _upload_to_gcs(self, path: str) -> str:
-        bucket = self.storage_client.bucket(GCS_BUCKET_NAME)
-        blob_name = f"pitch-videos/{uuid.uuid4()}.mp4"
-        blob = bucket.blob(blob_name)
-        blob.upload_from_filename(path)
-        return f"gs://{GCS_BUCKET_NAME}/{blob_name}"
-
-    def _delete_from_gcs(self, gcs_uri: str):
-        bucket_name, blob_name = gcs_uri.replace("gs://", "").split("/", 1)
-        self.storage_client.bucket(bucket_name).blob(blob_name).delete()
-
-    def run_analysis_pipeline(self, video_path_or_url: str, progress_callback):
-        local_video_path = None
-        video_gcs_uri = None
-        try:
-            if re.match(r"^(https?://)?(www\.)?(youtube\.com|youtu\.?be)/.+$", video_path_or_url):
-                progress_callback(0.1, "Downloading video from YouTube...")
-                local_video_path = self.youtube_tool.run(video_path_or_url)
-            else:
-                local_video_path = video_path_or_url
-
-            progress_callback(0.3, "Performing JAX-based quantitative analysis...")
-            quant_metrics = self.quant_tool.run(local_video_path)
-
-            progress_callback(0.5, "Uploading video to secure Cloud Storage...")
-            video_gcs_uri = self._upload_to_gcs(local_video_path)
-
-            progress_callback(0.7, "Gemini 1.5 Pro is analyzing the video...")
-            analysis_prompt = PROMPT_HOLISTIC_VIDEO_ANALYSIS.format(quantitative_metrics_json=json.dumps(quant_metrics, indent=2))
-            multimodal_analysis = self.vertex_manager.run_multimodal_analysis(video_gcs_uri, analysis_prompt)
-
-            progress_callback(0.9, "Synthesizing final report...")
-            synthesis_prompt = PROMPT_FINAL_SYNTHESIS.format(full_analysis_json=json.dumps(multimodal_analysis, indent=2))
-            final_summary = self.vertex_manager.run_synthesis(synthesis_prompt)
-            
-            return {"quantitative_metrics": quant_metrics, "multimodal_analysis": multimodal_analysis, "executive_summary": final_summary}
-        except Exception as e:
-            logging.error(f"Analysis pipeline failed: {e}", exc_info=True)
-            return {"error": str(e)}
-        finally:
-            if video_gcs_uri:
-                try: self._delete_from_gcs(video_gcs_uri)
-                except Exception as e: logging.warning(f"Failed to delete GCS object {video_gcs_uri}: {e}")
-            if local_video_path and video_path_or_url != local_video_path:
-                if os.path.exists(local_video_path): os.remove(local_video_path)
-
-# ==============================================================================
-# UI FORMATTING HELPER
-# ==============================================================================
-def format_feedback_markdown(analysis: dict) -> str:
-    if not analysis or "error" in analysis:
-        return f"## Analysis Failed 😞\n\n**Reason:** {analysis.get('error', 'Unknown error.')}"
+    lang_model_map = {
+        "German 🇩🇪": "Helsinki-NLP/opus-mt-en-de",
+        "Spanish 🇪🇸": "Helsinki-NLP/opus-mt-en-es",
+        "Japanese 🇯🇵": "Helsinki-NLP/opus-mt-en-jap",
+    }
     
-    summary = analysis.get('executive_summary', {})
-    metrics = analysis.get('quantitative_metrics', {})
-    ai_analysis = analysis.get('multimodal_analysis', {})
+    translations = [f"### 🌍 Translated Content\n"]
     
-    def get_pace_rating(wpm):
-        if wpm == 0: return "N/A (No speech detected)"
-        if wpm < 120: return "Slow / Deliberate"
-        if wpm <= 160: return "Conversational"
-        return "Fast-Paced"
-
-    def get_energy_rating(variation):
-        if variation == 0: return "N/A"
-        if variation < 0.02: return "Consistent / Monotonous"
-        if variation <= 0.05: return "Moderately Dynamic"
-        return "Highly Dynamic & Engaging"
-        
-    wpm = metrics.get('speaking_pace_wpm', 0)
-    energy_var = metrics.get('vocal_energy_variation', 0)
-    pace_rating = get_pace_rating(wpm)
-    energy_rating = get_energy_rating(energy_var)
-
-    metrics_md = f"""
-- **Speaking Pace:** **{wpm} WPM** *(Rating: {pace_rating})*
-  - *This measures the number of words spoken per minute. A typical conversational pace is between 120-160 WPM.*
-- **Vocal Energy Variation:** **{energy_var:.4f}** *(Rating: {energy_rating})*
-  - *This measures the standard deviation of your vocal loudness. A higher value indicates a more dynamic and engaging vocal range, while a very low value suggests a monotonous delivery.*
-    """
-
-    # --- FIX: Revert to using bold text instead of headers for consistency ---
-    def format_ai_item(title, data):
-        if not data or "score" not in data: return f"**{title}:**\n> Analysis not available.\n\n"
-        raw_score = data.get('score', 0); score = max(1, min(10, raw_score))
-        stars = "🟢" * score + "⚪️" * (10 - score)
-        feedback = data.get('feedback', 'No feedback.').replace('\n', '\n> ')
-        return f"**{title}:** `{stars} [{score}/10]`\n\n> {feedback}\n\n"
+    for lang_name in languages:
+        model_name = lang_model_map.get(lang_name)
+        if model_name:
+            translator = get_pipeline("translation", model_name)
+            translated_text = translator(text)[0]['translation_text']
+            translations.append(f"**{lang_name.upper()} VERSION:**\n\n{translated_text}")
     
-    content = ai_analysis.get('content_analysis', {}); vocal = ai_analysis.get('vocal_analysis', {}); visual = ai_analysis.get('visual_analysis', {})
+    return "\n\n---\n\n".join(translations)
 
-    # --- FIX: Use a more consistent structure for the final report ---
-    return f"""
-# PitchPerfect AI Analysis Report 📊
-## 🏆 Executive Summary
-### Key Strengths
-{summary.get('key_strengths', '- N/A')}
-### High-Leverage Growth Opportunities
-{summary.get('growth_opportunities', '- N/A')}
-### Final Verdict
-> {summary.get('executive_summary', 'N/A')}
----
-## 📈 Quantitative Metrics Explained (via JAX)
-{metrics_md}
----
-## 🧠 AI Multimodal Analysis (via Gemini 1.5 Pro)
-### I. Content & Structure
-{format_ai_item("Clarity", content.get('clarity'))}
-{format_ai_item("Structure & Flow", content.get('structure'))}
-{format_ai_item("Value Proposition", content.get('value_proposition'))}
-{format_ai_item("Call to Action (CTA)", content.get('cta'))}
-<hr style="border:1px solid #ddd">
 
-### II. Vocal Delivery
-{format_ai_item("Pacing", vocal.get('pacing'))}
-{format_ai_item("Vocal Variety", vocal.get('vocal_variety'))}
-{format_ai_item("Confidence & Energy", vocal.get('confidence_energy'))}
-{format_ai_item("Clarity & Enunciation", vocal.get('clarity_enunciation'))}
-<hr style="border:1px solid #ddd">
+# --- Full Gradio UI ---
+# The UI structure remains the same, but the functions it calls are now AI-powered.
+# The code for create_ui(), search_arxiv_papers, and summarize_youtube_from_url is omitted here 
+# for brevity, as it doesn't change from the previous version. You can just plug the
+# new functions above into your existing app.py file.
 
-### III. Visual Delivery
-{format_ai_item("Eye Contact", visual.get('eye_contact'))}
-{format_ai_item("Body Language", visual.get('body_language'))}
-{format_ai_item("Facial Expressions", visual.get('facial_expressions'))}
-"""
+# --- Helper functions from previous version to make the file runnable ---
 
-# ==============================================================================
-# GRADIO APPLICATION
-# ==============================================================================
-if __name__ == "__main__":
-    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-    pitch_agent = None
-    try:
-        pitch_agent = PitchAnalyzerAgent()
-    except Exception as e:
-        logging.fatal(f"Failed to initialize agent during startup: {e}", exc_info=True)
+def search_arxiv_papers(topic: str) -> str:
+    if not topic: return "❌ Please enter a topic to search."
+    summarizer = get_pipeline("summarization", "sshleifer/distilbart-cnn-12-6")
+    search = arxiv.Search(query=topic, max_results=3, sort_by=arxiv.SortCriterion.Relevance)
+    results = [f"**📄 {res.title}**\n\n**Summary:** {summarizer(res.summary.replace(' ', ' '), max_length=80, min_length=20, do_sample=False)[0]['summary_text']}\n\n**🔗 [Read Paper]({res.pdf_url})**" for res in search.results()]
+    return "\n\n---\n\n".join(results) if results else "No papers found."
 
-    def run_analysis_pipeline(video_path, url_path, progress=gr.Progress(track_tqdm=True)):
-        if not pitch_agent: return "## FATAL ERROR: Application not initialized. Check logs and config."
-        input_path = url_path if url_path else video_path
-        if not input_path: return "## No Video Provided. Please upload a video or enter a YouTube URL."
-        
-        analysis_result = pitch_agent.run_analysis_pipeline(input_path, progress)
-        return format_feedback_markdown(analysis_result)
-
-    with gr.Blocks(theme=gr.themes.Soft(primary_hue="teal", secondary_hue="orange")) as demo:
-        gr.Markdown("# **Video Analysis AI**: Your Enterprise-Grade Sales Coach 🚀")
-        with gr.Row():
-            with gr.Column(scale=1):
-                video_uploader = gr.Video(label="Upload Your Pitch", sources=["upload"])
-                gr.Markdown("--- **OR** ---")
-                youtube_url = gr.Textbox(label="Enter YouTube URL")
-                analyze_button = gr.Button("Analyze My Pitch 🧠", variant="primary")
-                gr.Examples(examples=EXAMPLE_VIDEOS, inputs=youtube_url, label="Example Pitches (Click to Use)")
-            with gr.Column(scale=2):
-                analysis_output = gr.Markdown(label="Your Feedback Report", value="### Your detailed report will appear here...")
-        analyze_button.click(fn=run_analysis_pipeline, inputs=[video_uploader, youtube_url], outputs=analysis_output)
+def summarize_youtube_from_url(video_url: str) -> str:
+    if not video_url: return "❌ Please enter a YouTube URL."
+    video_id_match = re.search(r"(?:v=|\/)([0-9A-Za-z_-]{11}).*", video_url)
+    if not video_id_match: return "❌ Invalid YouTube URL."
+    video_id = video_id_match.group(1)
+    try:
+        transcript_list = YouTubeTranscriptApi.get_transcript(video_id)
+        transcript_text = " ".join([d['text'] for d in transcript_list])
+        if len(transcript_text) < 200: return "Transcript too short."
+        summarizer = get_pipeline("summarization", "sshleifer/distilbart-cnn-12-6")
+        summary = summarizer(transcript_text, max_length=100, min_length=30, do_sample=False)
+        return f"**✅ Summary:**\n\n{summary[0]['summary_text']}"
+    except NoTranscriptFound: return "❌ No transcript available."
+    except Exception as e: return f"⚠️ Error: {e}"
+
+def create_ui():
+    css = """.gradio-container { font-family: 'Inter', sans-serif; background: #f5f7fa; } .tab-item { background: white; border-radius: 12px; padding: 25px; border: 1px solid #e0e0e0; } footer { display: none !important }"""
+    with gr.Blocks(theme=gr.themes.Base(), css=css, title="The Content Creator's Journey") as app:
+        gr.Markdown("""<div style="text-align: center; padding: 20px; background: #1f2937; color: white; border-radius: 12px;"><h1 style="font-size: 2.5em; margin: 0; font-weight: 700;">🚀 The Content Creator's Journey</h1><p style="font-size: 1.2em; margin-top: 5px;">From a spark of an idea to a global message, in three stages.</p></div>""")
+        with gr.Tabs() as tabs:
+            with gr.TabItem("1. The Spark: Inspiration Hub", id=0, elem_classes=["tab-item"]):
+                gr.Markdown("### Every great creation starts with an idea. Research any topic to get summarized insights from academia and popular culture.")
+                with gr.Row(variant="panel"):
+                    with gr.Column(min_width=400):
+                        gr.Markdown("#### 🔬 Academic Insights (from arXiv)")
+                        inspire_topic = gr.Textbox(label="Enter a Topic to Search Papers", placeholder="e.g., 'sustainable technology'")
+                        arxiv_btn = gr.Button("Search arXiv")
+                        inspire_arxiv_output = gr.Markdown()
+                    with gr.Column(min_width=400):
+                        gr.Markdown("#### 📺 Video Insights (from YouTube URL)")
+                        inspire_yt_url = gr.Textbox(label="Paste a YouTube Video URL", placeholder="e.g., 'https://www.youtube.com/watch?v=...'")
+                        yt_btn = gr.Button("Summarize Video")
+                        inspire_yt_output = gr.Markdown()
+                arxiv_btn.click(fn=search_arxiv_papers, inputs=inspire_topic, outputs=inspire_arxiv_output)
+                yt_btn.click(fn=summarize_youtube_from_url, inputs=inspire_yt_url, outputs=inspire_yt_output)
+            with gr.TabItem("2. The Craft: Creative Studio", id=1, elem_classes=["tab-item"]):
+                gr.Markdown("### Transform your idea into a polished piece of content. Upload a visual anchor and let the AI help you write.")
+                with gr.Row(variant="panel"):
+                    with gr.Column(scale=1):
+                        craft_image = gr.Image(label="🖼️ Upload a Visual Anchor", type="pil")
+                        craft_style = gr.Dropdown(choices=["✍️ Blog Post", "💬 Social Media Caption", "💡 Video Script Hook"], value="✍️ Blog Post", label="📝 Content Format")
+                        craft_audience = gr.Dropdown(choices=["🎓 Experts", "🧐 General Audience", "👩‍💻 Tech Enthusiasts"], value="🧐 General Audience", label="👥 Target Audience")
+                        craft_prompt = gr.Textbox(label="📌 Key Message or Note", placeholder="e.g., 'Focus on the human element...'")
+                        craft_btn = gr.Button("🎨 Craft My Content")
+                    with gr.Column(scale=2):
+                        craft_analysis_output = gr.Markdown(label="AI Vision Analysis")
+                        craft_text_output = gr.Textbox(label="✒️ Generated Content", lines=10)
+                        craft_analytics_output = gr.Markdown(label="Performance Analytics")
+                craft_btn.click(fn=run_creative_studio, inputs=[craft_image, craft_style, craft_audience, craft_prompt], outputs=[craft_analysis_output, craft_text_output, craft_analytics_output])
+            with gr.TabItem("3. The Reach: Globalization Suite", id=2, elem_classes=["tab-item"]):
+                gr.Markdown("### Your masterpiece is ready. Now, adapt it for a global audience with our translation suite.")
+                with gr.Row(variant="panel"):
+                    with gr.Column(scale=2):
+                        reach_text_input = gr.Textbox(label="Paste Content Here (from Stage 2)", lines=8)
+                        reach_lang_select = gr.CheckboxGroup(choices=["German 🇩🇪", "Spanish 🇪🇸", "Japanese 🇯🇵"], label="🌐 Select Languages")
+                        reach_btn = gr.Button("🌍 Globalize My Content")
+                    with gr.Column(scale=3):
+                        reach_output = gr.Markdown(label="Adapted for Global Audiences")
+                reach_btn.click(fn=translate_content_with_ai, inputs=[reach_text_input, reach_lang_select], outputs=reach_output)
+    return app
 
-    if pitch_agent:
-        demo.launch(debug=True, share=True)
-    else:
-        print("\n" + "="*80 + "\nCOULD NOT START GRADIO APP: Agent failed to initialize.\n" + "="*80)
+if __name__ == "__main__":
+    # To enable lazy loading of models, the UI is created first.
+    # The get_pipeline function will handle model loading upon first use.
+    app = create_ui()
+    app.launch(debug=True)