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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+nn4.small2.v1.t7 filter=lfs diff=lfs merge=lfs -text

agent_tools.py

@@ -0,0 +1,415 @@
+# -*- coding: utf-8 -*-
+from phi.agent import Agent
+from phi.model.google import Gemini
+from phi.tools.duckduckgo import DuckDuckGo
+import google.generativeai as genai
+from google.generativeai import upload_file, get_file
+
+import os
+import numpy as np
+import time
+import uuid
+
+import yt_dlp
+import cv2
+import mediapipe as mp
+
+#========================================================================================================== 
+# Load a pre-trained face embedding model (OpenCV's FaceNet). This model has better performance than mp embedder
+face_embedder = cv2.dnn.readNetFromTorch("nn4.small2.v1.t7")  # Download the model from OpenCV's GitHub
+
+# Define embedder with Mediapipe, -- comment off as worse performance for face detection
+# Download the model from https://storage.googleapis.com/mediapipe-tasks/image_embedder
+BaseOptions = mp.tasks.BaseOptions
+ImageEmbedder = mp.tasks.vision.ImageEmbedder
+ImageEmbedderOptions = mp.tasks.vision.ImageEmbedderOptions
+VisionRunningMode = mp.tasks.vision.RunningMode
+
+options = ImageEmbedderOptions(
+    base_options=BaseOptions(model_asset_path='mobilenet_v3_small_075_224_embedder.tflite'),
+    quantize=True,
+    running_mode=VisionRunningMode.IMAGE)
+
+mp_embedder = ImageEmbedder.create_from_options(options)
+
+#================================================================================================================   
+def initialize_agent():
+    return Agent(
+        name="Video AI summarizer",
+        model=Gemini(id="gemini-2.0-flash-exp"),
+        tools=[DuckDuckGo()],
+        show_tool_calls=True,
+        markdown=True,
+    )
+
+# Based on cv2 facenet embedder
+def get_face_embedding(face_image):
+    """
+    Generate a face embedding using the pre-trained model.
+    """
+    # Preprocess the face image with cv2
+    blob = cv2.dnn.blobFromImage(face_image, 1.0 / 255, (96, 96), (0, 0, 0), swapRB=True, crop=False)
+    face_embedder.setInput(blob)
+    embedding = face_embedder.forward()
+    
+    return embedding.flatten()
+    
+#  Based on mediapipe embedder
+def get_mp_embedding(face_image):
+    """
+    Generate a face embedding using the pre-trained model.
+    """
+    # Load the input image from a numpy array.
+    mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=np.array(face_image))
+    embedding_result = mp_embedder.embed(mp_image)
+    
+    return embedding_result.embeddings[0]  
+
+# Advanced Face Tracking with MediaPipe and Face Embeddings
+def face_detection_embed(video_path):
+    # Initialize MediaPipe Face Detection
+    mp_face_detection = mp.solutions.face_detection
+    mp_drawing = mp.solutions.drawing_utils
+    
+    # Load a pre-trained face embedding model (OpenCV's FaceNet)
+    #embedder = cv2.dnn.readNetFromTorch("nn4.small2.v1.t7")  # Download the model from OpenCV's GitHub, move out from this function
+    
+    # Open the video file
+    video_capture = cv2.VideoCapture(video_path)
+    
+    # Dictionary to store face embeddings and their corresponding IDs, number of matched, normalized images
+    face_tracker = {}  # Format: {face_id: {"embedding": face_embedding,  "number_matched": number_matched, "image": normalized_face}}
+    face_id_counter = 0
+    similarity_threshold = 0.5  # Threshold for considering two faces the same
+    frame_number = 0
+    
+    # Define the target size for normalization
+    target_width = 100  # Desired width for all faces
+    target_height = 100  # Desired height for all faces
+
+    with mp_face_detection.FaceDetection(min_detection_confidence=0.5) as face_detection:
+        while True:
+            # Grab a single frame of video
+            ret, frame = video_capture.read()
+            if not ret:
+                break
+
+            if frame_number % 30 == 0:
+                # Convert the frame to RGB for MediaPipe
+                rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                
+                # Process the frame with MediaPipe Face Detection
+                results = face_detection.process(rgb_frame)
+
+                if results.detections:
+                    for detection in results.detections:
+                        # Get the bounding box of the face
+                        bboxC = detection.location_data.relative_bounding_box
+                        ih, iw, _ = frame.shape
+                        x = int(bboxC.xmin * iw)
+                        y = int(bboxC.ymin * ih)
+                        w = int(bboxC.width * iw)
+                        h = int(bboxC.height * ih)
+                        score = detection.score[0]
+                        
+                        # Extract the face region
+                        face_image = frame[y:y+h, x:x+w]
+                        if face_image.size == 0:
+                            continue  # Skip empty face regions
+                            
+                        #yield face_image  # Yield the frame for streaming
+                        
+                        # Generate the face embedding
+                        face_embedding = get_face_embedding(face_image) #This model has better performance than mp embedder
+                        #face_embedding = get_mp_embedding(face_image)
+                        
+                        # Check if this face matches any previously tracked face, and find face_id with maximum similarity
+                        matched_id = None
+                        max_similarity = 0
+                        for face_id, data in face_tracker.items():
+                            # Calculate the cosine similarity between embeddings. This model has better performance than mp embedder
+                            similarity = np.dot(face_embedding, data["embedding"]) / (
+                                np.linalg.norm(face_embedding) * np.linalg.norm(data["embedding"])
+                            )
+                            '''
+                            # Compute cosine similarity. comment off because of worse performance
+                            similarity = ImageEmbedder.cosine_similarity(
+                              face_embedding, data["embedding"])
+                            '''
+                            if similarity > max_similarity:
+                                max_similarity = similarity
+                                max_face_id = face_id 
+                           
+                        # Define a larger bounding box for output faceface                      
+                        xb = int(x * 0.8)                         
+                        yb = int(y * 0.8)                         
+                        xe = int(x * 1.2 + w)                         
+                        ye = int(y * 1.2 + h)                         
+  
+                        if max_similarity > similarity_threshold:
+                            matched_id = max_face_id
+                            number_matched = face_tracker[matched_id]["number_matched"] + 1
+                            face_tracker[matched_id]["number_matched"] = number_matched
+                            if score > face_tracker[matched_id]["score"]: #switch to higher score image
+                                face_image_b = frame[yb:ye, xb:xe]
+                                normalized_face = cv2.resize(face_image_b, (target_width, target_height))
+                                face_tracker[matched_id] = {"embedding": face_embedding, "number_matched": number_matched, "image": normalized_face, "score":score}
+
+                        # If the face is not matched, assign a new ID
+                        if matched_id is None:
+                            face_id_counter += 1
+                            matched_id = face_id_counter
+                            
+                            # Update the face tracker with the new embedding and frame number
+                            face_image_b = frame[yb:ye, xb:xe]
+                            normalized_face = cv2.resize(face_image_b, (target_width, target_height))
+                            face_tracker[matched_id] = {"embedding": face_embedding, "number_matched": 0, "image": normalized_face, "score":score}
+
+                        # Draw a larger bounding box and face ID
+                        cv2.rectangle(frame, (xb, yb), (xe, ye), (0, 255, 0), 2)
+                        cv2.putText(frame, f"ID: {matched_id}", (xb, yb - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+                # Display the resulting frame, for debug purpose
+                #yield frame  # Yield the frame for streaming
+                #time.sleep(2) #simulate a delay
+
+            # Increment frame number             
+            frame_number += 1
+
+        # finished reading video
+        if len(face_tracker) == 0:
+            return None
+            
+        sorted_data = sorted(face_tracker, key=lambda x: face_tracker[x]['number_matched'], reverse =True)
+        
+        # find top N faces in all detected faces
+        number_faces = len(face_tracker)
+        if number_faces >= 3:
+            center_top1 = [sorted_data[1], sorted_data[0], sorted_data[2]] # Top 1 will take center position
+        else:
+            center_top1 = sorted_data 
+
+        images = []
+        contents = []
+        for face_id in center_top1: 
+            #yield  face_tracker[face_id]["image"]  # Yield the frame for streaming
+            #time.sleep(2) #simulate a delay
+            face_image = face_tracker[face_id]["image"]
+            images.append(face_image)
+                
+    # Release the video capture object
+    video_capture.release()
+    cv2.destroyAllWindows()
+
+    return images
+
+ # Advanced object Tracking with MediaPipe object detection
+def object_detection_embed(video_path):
+    # Initialize MediaPipe Face Detection
+    BaseOptions = mp.tasks.BaseOptions
+    ObjectDetector = mp.tasks.vision.ObjectDetector
+    ObjectDetectorOptions = mp.tasks.vision.ObjectDetectorOptions
+    VisionRunningMode = mp.tasks.vision.RunningMode
+
+    options = ObjectDetectorOptions(
+        base_options=BaseOptions(model_asset_path='efficientdet_lite0.tflite'),
+        max_results=3,
+        score_threshold=0.5,
+        running_mode=VisionRunningMode.IMAGE,
+        )
+ 
+    mp_drawing = mp.solutions.drawing_utils
+    
+    # Load a pre-trained face embedding model (OpenCV's FaceNet)
+    #embedder = cv2.dnn.readNetFromTorch("nn4.small2.v1.t7")  # Download the model from OpenCV's GitHub, move out from this function
+    
+    # Open the video file
+    video_capture = cv2.VideoCapture(video_path)
+    
+    # Dictionary to store face embeddings and their corresponding IDs, number of matched, normalized images
+    object_tracker = {}  # Format: {object_id: {"embedding": obj_embedding,  "number_matched": number_matched, "image": normalized_obj, "score":score, "category": category}}
+    object_id_counter = 0
+    similarity_threshold = 0.5  # Threshold for considering two faces the same
+    frame_number = 0
+    
+    # Define the target size for normalization, only fix height
+    #target_width = 100  # Desired width for all faces
+    target_height = 100  # Desired height for all faces
+
+    with ObjectDetector.create_from_options(options) as obj_detection:
+        while True:
+            # Grab a single frame of video
+            ret, frame = video_capture.read()
+            if not ret:
+                break
+
+            if frame_number % 30 == 0:
+                # Convert the frame to RGB for MediaPipe
+                rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+                # Load the image back into memory because Image object needs filepath input
+                frame_height, frame_width, _ = rgb_frame.shape
+                                
+                # Load the input image from a numpy array.
+                mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb_frame)
+
+                # Process the frame with MediaPipe Face Detection
+                results = obj_detection.detect(mp_image)
+                
+                if results.detections:
+                    for detection in results.detections:
+                        #print("line 297: detection:", detection)
+                        '''
+                        sample output：
+                        Detection(bounding_box=BoundingBox(origin_x=84, origin_y=168, width=272, height=448), 
+                                  categories=[Category(index=None, score=0.81640625, display_name=None, category_name='person')], keypoints=[])
+                        '''
+                        # Get the bounding box of the face, note x is in height ditection(h)
+                        bboxC = detection.bounding_box
+                        x = int(bboxC.origin_x)
+                        y = int(bboxC.origin_y)
+                        w = int(bboxC.width)
+                        h = int(bboxC.height)
+                        score = detection.categories[0].score
+                        category = detection.categories[0].category_name
+                        
+                        # Extract the face region
+                        obj_image = frame[y:y+w, x:x+h]
+                        if obj_image.size == 0:
+                            continue  # Skip empty face regions
+                            
+                        #yield obj_image  # Yield the frame for streaming
+                        
+                        # Generate the face embedding
+                        #face_embedding = get_face_embedding(face_image) #This model has better performance than mp embedder
+                        obj_embedding = get_mp_embedding(obj_image)
+                        
+                        # Check if this face matches any previously tracked face, and find face_id with maximum similarity
+                        matched_id = None
+                        max_similarity = 0
+                        for obj_id, data in object_tracker.items():
+                            '''
+                            # Calculate the cosine similarity between embeddings. This model has better performance than mp embedder
+                            similarity = np.dot(face_embedding, data["embedding"]) / (
+                                np.linalg.norm(face_embedding) * np.linalg.norm(data["embedding"])
+                            )
+                            '''
+                            # Compute cosine similarity. comment off because of worse performance
+                            similarity = ImageEmbedder.cosine_similarity(
+                              obj_embedding, data["embedding"])
+                            
+                            if similarity > max_similarity:
+                                max_similarity = similarity
+                                max_obj_id = obj_id 
+
+                        # Define a larger bounding box for output faceface                      
+                        xb = int(x * 0.8)                         
+                        yb = int(y * 0.8)                         
+                        xe = int(x * 1.2 + h)                         
+                        ye = int(y * 1.2 + w)                         
+
+                        scale = target_height / (x * 0.4 + w)
+                        target_width = int((y * 0.4 + w) * scale)
+                        
+                        if max_similarity > similarity_threshold:
+                            matched_id = max_obj_id
+                            number_matched = object_tracker[matched_id]["number_matched"] + 1
+                            object_tracker[matched_id]["number_matched"] = number_matched
+                            if score > object_tracker[matched_id]["score"]: #switch to higher score image
+                                obj_image_b = frame[yb:ye, xb:xe]
+                                normalized_obj = cv2.resize(obj_image_b, (target_width, target_height))
+                                object_tracker[matched_id] = {"embedding": obj_embedding, "number_matched": number_matched, "image": normalized_obj, "score":score, "category":category}
+
+                        # If the face is not matched, assign a new ID
+                        if matched_id is None:
+                            object_id_counter += 1
+                            matched_id = object_id_counter
+                            
+                            # Update the face tracker with the new embedding and frame number
+                            obj_image_b = frame[yb:ye, xb:xe]
+                            normalized_obj = cv2.resize(obj_image_b, (target_width, target_height))
+                            object_tracker[matched_id] = {"embedding": obj_embedding, "number_matched": 0, "image": normalized_obj, "score":score, "category":category}
+
+                        # Draw a larger bounding box and face ID
+                        #cv2.rectangle(frame, (xb, yb), (xe, ye), (0, 255, 0), 2)
+                        #cv2.putText(frame, f"ID: {matched_id}", (xb, yb - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+                # Display the resulting frame, for debug purpose
+                #yield frame  # Yield the frame for streaming
+                #time.sleep(2) #simulate a delay
+
+            # Increment frame number             
+            frame_number += 1
+
+        # finished reading video
+        if len(object_tracker) == 0:
+            return None
+            
+        sorted_data = sorted(object_tracker, key=lambda x: object_tracker[x]['number_matched'], reverse =True)
+         
+        # find top N faces in all detected faces
+        number_objs = len(object_tracker)
+        if number_objs >= 3:
+            center_top1 = [sorted_data[1], sorted_data[0], sorted_data[2]] # Top 1 will take center position
+        else:
+            center_top1 = sorted_data 
+
+        images = []
+        contents = []
+        #center_top1 = [sorted_data[1], sorted_data[0], sorted_data[2]] # Top 1 will take center position
+        for obj_id in center_top1: 
+            #yield  object_tracker[obj_id]["image"]  # Yield the frame for streaming
+            #time.sleep(2) #simulate a delay
+            obj_image = object_tracker[obj_id]["image"]
+            images.append(obj_image)
+                
+    # Release the video capture object
+    video_capture.release()
+    cv2.destroyAllWindows()
+
+    return images
+   
+#=========================================================================================================
+# Summarize video using phi Agent
+
+def summarize_video(video_path, user_prompt, out_lang = 'Original'):
+    # Upload and process the video
+    processed_video = upload_file(video_path)
+
+    # Extract video info to a dictionary
+    video_info = str(processed_video).split('File(')[1]
+    video_info = video_info.replace(")", "")
+    video_dic = eval(video_info)
+    print("display_name, sha256_hash:", video_dic['display_name'], video_dic['sha256_hash'])
+    
+    while processed_video.state.name == "PROCESSING":
+        time.sleep(1)
+        processed_video = get_file(processed_video.name)
+    
+    # detect language
+    lang_prompt = (f'''Give language name''')
+    lang_response = multimodal_Agent.run(lang_prompt, videos=[processed_video]).content
+    language = str(lang_response).split(' ')[-1]
+    print('Video language is:', language)  
+    if out_lang == 'Original':
+        out_lang = language
+        
+    # Analysis prompt
+    analysis_prompt = ( f'''
+                        First analyze the video and then answer following questions using the video analysis, questions: 
+                        {user_prompt}
+                        Provide a comprehensive response focusing on practical, actionable information with original questions.
+                        Answer questions in {out_lang}. limit the total lines to 30 lines.'''
+                        )
+    
+    # AI agent processing
+    response = multimodal_Agent.run(analysis_prompt, videos=[processed_video])
+    
+    markdown_text = response.content
+
+    return out_lang, str(markdown_text)
+
+#=======================================================================================
+
+# Initialize the agent
+multimodal_Agent = initialize_agent()

app.py

@@ -0,0 +1,291 @@
+# -*- coding: utf-8 -*-
+import gradio as gr
+from typing import Optional, List
+
+from pydantic import BaseModel, Field
+
+from phi.agent import Agent
+from phi.model.google import Gemini
+from phi.workflow import Workflow, RunResponse, RunEvent
+from phi.storage.workflow.sqlite import SqlWorkflowStorage
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.utils.pprint import pprint_run_response
+from phi.utils.log import logger
+
+from agent_tools import (
+    object_detection_embed,
+    summarize_video,
+    )
+from utils import (
+    create_poster,
+    download_youtube_video,
+    generate_tmp_filename,
+    pdf_to_jpg,
+    )
+
+import os
+from PIL import Image
+import numpy as np
+
+# Output language
+LANG_OPTIONS = [
+    "Original",
+    "Chinese",
+    "English",
+] 
+
+#====================================================================================
+class Video(BaseModel):
+    name: str = Field(..., description="File name of the video.")
+    url: str = Field(..., description="Link to the video.")
+    summary: Optional[str] = Field(..., description="Summary of the video.")
+    hash_value: Optional[str] = Field(..., description="sha256_hash value of the video.")
+
+class VideoCache:
+    def __init__(self):
+        self.session_state = gr.State({
+            'metadata': {},  # For summaries
+            'frame_data': {}  # For image arrays (serialized)
+        })
+    
+    def add_to_cache(self, basename: str, out_lang: str, summary: str, frames: list[np.ndarray]):
+        """Store both summary and frames properly"""
+        key = basename + '_' + out_lang
+        # Convert numpy arrays to bytes
+        serialized_frames = [self._array_to_bytes(arr) for arr in frames]
+        
+        # Update cache
+        current_state = self.session_state.value
+        current_state['metadata'][key] = summary
+        current_state['frame_data'][key] = serialized_frames
+        self.session_state.value = current_state
+    
+    def get_from_cache(self, basename: str, out_lang: str) -> tuple:
+        """Retrieve both summary and frames"""
+        key = basename + '_' + out_lang
+        cache = self.session_state.value
+        summary = cache['metadata'].get(key)
+        frame_bytes = cache['frame_data'].get(key, [])
+        
+        # Convert bytes back to arrays
+        frames = [self._bytes_to_array(*b) for b in frame_bytes]
+        return summary, frames
+    
+    @staticmethod
+    def _array_to_bytes(arr: np.ndarray) -> tuple:
+        """Convert array to (bytes, shape)"""
+        return arr.tobytes(), arr.shape
+    
+    @staticmethod
+    def _bytes_to_array(b: bytes, shape: tuple) -> np.ndarray:
+        """Reconstruct array from (bytes, shape)"""
+        return np.frombuffer(b, dtype=np.uint8).reshape(shape)
+
+class VideoPosterGenerator(Workflow):
+    # Define an Agent that will load video clip
+    loader: Agent = Agent(
+        tools=[download_youtube_video],
+        show_tool_calls=True,
+        description="Given a url_link, load video to process.",
+    )
+    
+    # Define an Agent that will summarize video
+    summarizer: Agent = Agent(
+        tools=[summarize_video],
+        show_tool_calls=True,
+        markdown=True,
+        description="Given a video, answer the prompt questions.",
+    )
+    
+    # Define an Agent that will extract top three object images
+    detector: Agent = Agent(
+        tools=[object_detection_embed],
+        show_tool_calls=True,
+        structured_outputs=True,
+        description="Given a video, extract top three object images.",
+    )
+    
+    # Define an Agent that will generate a poster
+    poster: Agent = Agent(
+        tools=[create_poster],    
+        show_tool_calls=True,
+        structured_outputs=True,
+        description="Given summary and images, generate one page postes.",
+    )
+    
+    def run(self, url: str, user_prompt: str, out_lang: str, use_cache: bool = True) -> RunResponse:
+        """This is where the main logic of the workflow is implemented."""
+
+        logger.info(f"Generating a poster for video: {url}")
+        basename = os.path.basename(url)
+        pdf_name = generate_tmp_filename(basename, ".pdf")
+        
+        # Step 1: Use the cached video poster if use_cache is True
+        if use_cache:
+            summary, objects = video_cache.get_from_cache(basename, out_lang)
+
+            if summary is not None and objects is not None:
+                logger.info(f"found cached_video_content: {url}")
+                poster_response: Optional[poster] = create_poster(pdf_name, objects, out_lang, summary, url)
+        
+                if poster_response is None:
+                    return RunResponse(
+                        event=RunEvent.workflow_completed,
+                        content=f"Failed to generate video poster, please try again!",
+                    ) 
+                else:
+                    logger.info(f"Poster is generated sucessfully.")
+            
+                    return RunResponse(
+                        event=RunEvent.workflow_completed,
+                        content=[None, poster_response],
+                    ) 
+    
+        # Step 2: load video for the given url
+        video_response: Optional[loader] = download_youtube_video(url)
+        # If the video is not loaded sucessfully, end the workflow
+        if video_response is None:
+            return RunResponse(
+                event=RunEvent.workflow_completed,
+                content=f"Sorry, could not load the video: {url}",
+            )
+        else:
+            logger.info(f"Video {url} is loaded.")
+            video_path = video_response
+            
+        # Step 3: summalize the video for the given questions
+        summary_response:  Optional[summarizer] = summarize_video(video_path, user_prompt, out_lang)
+        # If the summary is not generated, end the workflow
+        if summary_response is None:
+            return RunResponse(
+                event=RunEvent.workflow_completed,
+                content=f"Failed to get summary, please try again!",
+            )
+        else:
+            logger.info(f"Video summary is generated.")
+            lang, summary = summary_response
+            
+        # Step 4: extract top 3 object(person or other) images
+        images_response: Optional[detector] = object_detection_embed(video_path)
+        # If objects are not detected sucessfully, end the workflow
+        if images_response is None:
+            return RunResponse(
+                event=RunEvent.workflow_completed,
+                content=f"Failed to extract images, please try again!",
+            ) 
+        else:
+            logger.info(f"Objects are extracted sucessfully.")
+            objects = images_response
+            
+        # Step 5: generate video poster
+        poster_response: Optional[poster] = create_poster(pdf_name, objects, lang, summary, url)
+        
+        if poster_response is None:
+            return RunResponse(
+                event=RunEvent.workflow_completed,
+                content=f"Failed to generate video poster, please try again!",
+            ) 
+        else:
+            logger.info(f"Poster is generated sucessfully.")
+            
+            # Store in cache
+            video_cache.add_to_cache(basename=basename, out_lang=out_lang, summary=summary, frames=objects)
+            
+            return RunResponse(
+                event=RunEvent.workflow_completed,
+                content=[video_path, poster_response],
+            ) 
+#=====================================================================================
+# Combine outputs of face detection and video summary to generate a single page paster
+def generate_poster_2(url, user_prompt, out_lang): 
+    url_base_name = os.path.basename(url)
+    jpg_name = generate_tmp_filename(url_base_name, ".jpg")
+    
+    # Initialize the poster generator workflow
+    # - Creates a unique session ID based on the video url
+    # - Sets up SQLite storage for caching results
+    poster = VideoPosterGenerator(
+        session_id=f"generate-poster-on-{url}",
+        storage=SqlWorkflowStorage(
+        table_name="generate_poster_workflows",
+        db_file="tmp/workflows.db",
+        ),
+    )
+
+    # Execute the workflow with caching enabled
+    # Returns an iterator of RunResponse objects containing the generated content
+    video_path, video_poster = poster.run(url=url, user_prompt=user_prompt, out_lang=out_lang, use_cache=True).content
+    logger.info(f"video_poster: {video_poster}")
+
+    poster_jpg = pdf_to_jpg(video_poster, jpg_name)
+    
+    return video_path, video_poster, jpg_name
+#==================================================================================
+# Gradio interface     
+print("Setting up Gradio interface...")
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    video_cache = VideoCache()
+    
+    gr.Markdown(
+        """
+        # 🎥 Video Smart Summary - From Video to Poster with Multimodal Agent
+        
+        Provide a YouTube or other video url to get an AI-generated summary poster. 
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale = 5, variant = "compact"):
+            url_input = gr.Textbox(label="Paste YouTube URL here", 
+                            placeholder="https://www.youtube.com/shorts/AE5HZsZOlkY", 
+                            value="https://www.youtube.com/shorts/AE5HZsZOlkY")
+            video_input = gr.Video(label="Downloaded Video", height = 300, scale = 5)
+        
+        with gr.Column(scale = 5, variant = "compact"):
+            lang_name = gr.Dropdown(
+                choices=LANG_OPTIONS,
+                value=LANG_OPTIONS[0],
+                label="Output Language",
+                interactive = True,
+            )
+            
+            user_prompt = gr.Textbox(label="📊 User Prompt", 
+            value=
+f'''0. **Title**: Summarize this video in one sentence with no more than 8 words.
+1. **Story:** How the set scene introduced and tone is set. What is happening in the scene? Describe key visuals and actions. 
+2. **Characters**: Identify top three character, noting their expressions, attire, actions, and interactions. Highlight emotional nuances and gestures.
+3. **Narration or Voiceover**: Describe what types of narrations or voiceovers are used in the video.  
+4. **Mood and Tone**: Capture the overall mood and tone of each scene, mentioning any music or sound effects that enhance these elements.''', 
+            placeholder="Ask anything about the video - AI Agent will analyze everything and search the web if needed",
+            info="You can ask questions about the video content",
+            max_lines=30,
+            interactive = True)
+            
+    with gr.Row():      
+        poster_button = gr.Button("🚀 Generate Poster", variant="primary")
+           
+    with gr.Row():    
+        with gr.Column(scale = 6, variant = "compact"):
+            jpg_file = gr.Image(label="Generated Poster Image", type = "filepath")
+        with gr.Column(scale = 4, variant = "compact"):    
+            pdf_file = gr.File(label="Generated Poster PDF", file_types=[".pdf"])
+
+  
+    gr.Markdown(
+        """
+        ### How to use:
+        1. Paste a YouTube link in the URL input textbox;
+        2. Select output language you want to use, currently only support original(default, no translation), English and Chinese;
+        3. Modify you prompt questions if you want (optional);
+        4. Click the primary task button "Generate Poster";
+        5. Downalod generated poster (JPG or PDF) file from ‘Generated Poster ...’ block.
+        
+        *Note: Processing may take a few minutes depending on the video length.*
+        *If you get error for some reason, retry it before debug it!*
+        """
+    )
+
+    # actions
+    poster_button.click(generate_poster_2, inputs=[url_input, user_prompt, lang_name], outputs=[video_input, pdf_file, jpg_file])  
+    
+demo.launch(share=True)

cookies.txt

@@ -0,0 +1,106 @@
+# Netscape HTTP Cookie File
+# http://curl.haxx.se/rfc/cookie_spec.html
+# This is a generated file!  Do not edit.
+
+.youtube.com	TRUE	/	TRUE	1737011390	GPS	1
+.youtube.com	TRUE	/	TRUE	1771569697	PREF	f4=4000000&tz=Asia.Shanghai
+.youtube.com	TRUE	/	TRUE	1737010197	CONSISTENCY	AKreu9vCfSs6pP6NbKMCvU1eYGcbBAg1tgQNnfkLoXJbJ7kHkErV_NV5RDuUQpFQFj71zvR8mS3w3aDOEBB6nL_vghwpUrX4DImcN8EQ1sSj1yjmZIkCskc
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+www.google.com	FALSE	/	FALSE	1737010262	DV	I-ER0WmhH2AfMMNiN6YW-gY0cYDdRhk
+accounts.google.com	FALSE	/	TRUE	1739601671	OTZ	7911761_24_24__24_
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+.google.com	TRUE	/	TRUE	1771569694	SSID	ARPosjI5U2j0EUZjn
+.google.com	TRUE	/	FALSE	1771569694	APISID	NQo2BYHF9ulg8byP/AYDJ1Kma5Z56PpGgm
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+.google.com	TRUE	/	TRUE	1771569694	__Secure-3PAPISID	CUOKDfcJr5fsAuGB/A_rgQNjYhdnyijGwj
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+accounts.google.com	FALSE	/	TRUE	1771569694	__Host-1PLSID	s.HK|s.youtube:g.a000sghTt4qHecL8VzIlofS3hyqQ9G36KeWl6kbej3QDkAeo-jUtJwSmGJavnt2MVNOj2T4R9gACgYKATgSARESFQHGX2Mi0h-fm59mmVUt9CHO60aFnxoVAUF8yKpr4pWJllra6NcEAhozO72L0076
+accounts.google.com	FALSE	/	TRUE	1771569694	__Host-3PLSID	s.HK|s.youtube:g.a000sghTt4qHecL8VzIlofS3hyqQ9G36KeWl6kbej3QDkAeo-jUtmS6j1SYGHxzLdhLpMl-OYQACgYKAVcSARESFQHGX2Mi2y89TCXJ6pkWk4ymz526SBoVAUF8yKrrGH0NhgZzD9fwr_Gilgrc0076
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