Update tools.py

tools.py

@@ -1,421 +1,424 @@
-from langchain.tools import tool, Tool
-import re
-import os
-from langchain_groq import ChatGroq
-import requests
-import cv2
-from moviepy.editor import ImageClip, AudioFileClip, concatenate_videoclips
-from langchain.pydantic_v1 import BaseModel, Field
-from langchain_community.tools import WikipediaQueryRun
-from langchain_community.utilities import WikipediaAPIWrapper
-
-# from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler
-# import bitsandbytes as bnb
-# import torch.nn as nn
-# import torch
-import pyttsx3
-import os
-# from langchain_google_genai import ChatGoogleGenerativeAI
-
-# from langchain.chat_models import ChatOpenAI
-# # llm2 = ChatOpenAI(model='gpt-3.5-turbo')
-# # llm3 = ChatOpenAI(model='gpt-3.5-turbo')
-# llm1 = ChatGroq(model='llama3-70b-8192', temperature=0.6, max_tokens=2048)
-# # llm2 = ChatGroq(model='mixtral-8x7b-32768', temperature=0.6, max_tokens=2048, api_key='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')
-# llm2 = ChatGoogleGenerativeAI(model='gemini-pro', temperature=0.0)
-# # llm2 = ChatGroq(model='llama3-70b-8192', temperature=0.6, max_tokens=2048, api_key='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')
-# # llm3 = ChatGoogleGenerativeAI(model='gemini-pro')
-# llm4 = ChatGroq(model='llama3-70b-8192', temperature=0.6, max_tokens=2048, api_key='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')
-llm = ChatGroq(model='llama3-70b-8192', temperature=0.6, max_tokens=1024, api_key=os.environ.get('GROQ_API_KEY'))
-
-# pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash", torch_dtype=torch.float16).to('cuda')
-# pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
-
-# def quantize_model_to_4bit(model):
-#     replacements = []
-
-#     # Collect layers to be replaced
-#     for name, module in model.named_modules():
-#         if isinstance(module, nn.Linear):
-#             replacements.append((name, module))
-
-#     # Replace layers
-#     for name, module in replacements:
-#         # Split the name to navigate to the parent module
-#         *path, last = name.split('.')
-#         parent = model
-#         for part in path:
-#             parent = getattr(parent, part)
-
-#         # Create and assign the quantized layer
-#         quantized_layer = bnb.nn.Linear4bit(module.in_features, module.out_features, bias=module.bias is not None)
-#         quantized_layer.weight.data = module.weight.data
-#         if module.bias is not None:
-#             quantized_layer.bias.data = module.bias.data
-#         setattr(parent, last, quantized_layer)
-
-#     return model
-
-# pipe.unet = quantize_model_to_4bit(pipe.unet)
-# pipe.enable_model_cpu_offload()
-
-def generate_speech(text, speech_dir='./outputs/audio', lang='en', speed=170, voice='default', num=0):
-    """
-    Generates speech for given script.
-    """
-    engine = pyttsx3.init()
-    
-    # Set language and voice
-    voices = engine.getProperty('voices')
-    if voice == 'default':
-        voice_id = voices[1].id
-    else:
-        # Try to find the voice with the given name
-        voice_id = None
-        for v in voices:
-            if voice in v.name:
-                voice_id = v.id
-                break
-        if not voice_id:
-            raise ValueError(f"Voice '{voice}' not found.")
-    
-    engine.setProperty('voice', voice_id)
-    engine.setProperty('rate', speed)
-    os.remove(os.path.join(os.path.dirname(os.path.abspath(__file__)), speech_dir, f'speech_{num}.mp3')) if os.path.exists(os.path.join(speech_dir, f'speech_{num}.mp3')) else None
-    engine.save_to_file(text, os.path.join(os.path.dirname(os.path.abspath(__file__)), speech_dir, f'speech_{num}.mp3'))
-    engine.runAndWait()
-
-# class VideoGeneration(BaseModel):
-#     images_dir : str = Field(description='Path to images directory, such as "outputs/images"')
-#     speeches_dir : str = Field(description='Path to speeches directory, such as "outputs/speeches"')
-
-# @tool(args_schema=VideoGeneration)
-# def create_video_from_images_and_audio(images_dir, speeches_dir, zoom_factor=1.2):
-#     """Creates video using images and audios with zoom-in effect"""
-#     images_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), images_dir)
-#     speeches_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), speeches_dir)
-
-#     images_paths = os.listdir(images_dir)
-#     audio_paths = os.listdir(speeches_dir)
-#     # print(images_paths, audio_paths)
-#     clips = []
-    
-#     for i in range(min(len(images_paths), len(audio_paths))):
-#         # Load the image
-#         img_clip = ImageClip(os.path.join(images_dir, images_paths[i]))
-        
-#         # Load the audio file
-#         audioclip = AudioFileClip(os.path.join(speeches_dir, audio_paths[i]))
-        
-#         # Set the duration of the video clip to the duration of the audio file
-#         videoclip = img_clip.set_duration(audioclip.duration)
-        
-#         # Apply zoom-in effect to the video clip
-#         zoomed_clip = apply_zoom_in_effect(videoclip, zoom_factor)
-        
-#         # Add audio to the zoomed video clip
-#         zoomed_clip = zoomed_clip.set_audio(audioclip)
-        
-#         clips.append(zoomed_clip)
-    
-#     # Concatenate all video clips
-#     final_clip = concatenate_videoclips(clips)
-    
-#     # Write the result to a file
-#     final_clip.write_videofile(os.path.join(os.path.dirname(os.path.abspath(__file__)), "outputs/final_video/final_video.mp4"), codec='libx264', fps=24)
-    
-#     return os.path.join(os.path.dirname(os.path.abspath(__file__)), "outputs/final_video/final_video.mp4")
-
-# def apply_zoom_in_effect(clip, zoom_factor=1.2):
-#     width, height = clip.size
-#     duration = clip.duration
-
-#     def zoom_in_effect(get_frame, t):
-#         frame = get_frame(t)
-#         zoom = 1 + (zoom_factor - 1) * (t / duration)
-#         new_width, new_height = int(width * zoom), int(height * zoom)
-#         resized_frame = cv2.resize(frame, (new_width, new_height))
-        
-#         # Calculate the position to crop the frame to the original size
-#         x_start = (new_width - width) // 2
-#         y_start = (new_height - height) // 2
-#         cropped_frame = resized_frame[y_start:y_start + height, x_start:x_start + width]
-        
-#         return cropped_frame
-
-#     return clip.fl(zoom_in_effect, apply_to=['mask'])
-
-# Example usage
-# image_paths = "outputs/images"
-# audio_paths = "outputs/audio"
-
-# video_path = create_video_from_images_and_audio(image_paths, audio_paths)
-# print(f"Video created at: {video_path}")
-
-
-# class ImageGeneration(BaseModel):
-#     text : str = Field(description='description of sentence used for image generation')
-#     num : int = Field(description='sequence of description passed this tool. Used in image saving path. Example 1,2,3,4,5 and so on')
-
-# class SpeechGeneration(BaseModel):
-#     text : str = Field(description='description of sentence used for image generation')
-#     num : int = Field(description='sequence of description passed this tool. Used in image saving path. Example 1,2,3,4,5 and so on')
-
-import os
-import cv2
-from moviepy.editor import ImageClip, AudioFileClip, concatenate_videoclips, VideoFileClip
-from PIL import Image, ImageDraw, ImageFont
-import numpy as np
-from groq import Groq
-
-client = Groq()
-
-class VideoGeneration(BaseModel):
-    images_dir: str = Field(description='Path to images directory, such as "outputs/images"')
-    speeches_dir: str = Field(description='Path to speeches directory, such as "outputs/speeches"')
-
-def split_text_into_chunks(text, chunk_size):
-    words = text.split()
-    return [' '.join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
-
-def add_text_to_video(input_video, output_video, text, duration=1, fontsize=40, fontcolor=(255, 255, 255),
-                      outline_thickness=2, outline_color=(0, 0, 0), delay_between_chunks=0.1,
-                      font_path=os.path.join(os.path.dirname(os.path.abspath(__file__)),'Montserrat-Bold.ttf')):
-    
-    chunks = split_text_into_chunks(text, 3)  # Adjust chunk size as needed
-
-    cap = cv2.VideoCapture(input_video)
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    fps = int(cap.get(cv2.CAP_PROP_FPS))
-    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    out = cv2.VideoWriter(output_video, fourcc, fps, (width, height))
-
-    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    chunk_duration_frames = duration * fps
-    delay_frames = int(delay_between_chunks * fps)
-
-    font = ImageFont.truetype(font_path, fontsize)
-
-    current_frame = 0
-
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-        draw = ImageDraw.Draw(frame_pil)
-
-        chunk_index = current_frame // (chunk_duration_frames + delay_frames)
-
-        if current_frame % (chunk_duration_frames + delay_frames) < chunk_duration_frames and chunk_index < len(chunks):
-            chunk = chunks[chunk_index]
-            text_width, text_height = draw.textsize(chunk, font=font)
-            text_x = (width - text_width) // 2
-            text_y = height - 400  # Position text at the bottom
-
-            if text_width > width:
-                words = chunk.split()
-                half = len(words) // 2
-                line1 = ' '.join(words[:half])
-                line2 = ' '.join(words[half:])
-
-                text_size_line1 = draw.textsize(line1, font=font)
-                text_size_line2 = draw.textsize(line2, font=font)
-                text_x_line1 = (width - text_size_line1[0]) // 2
-                text_x_line2 = (width - text_size_line2[0]) // 2
-                text_y = height - 250 - text_size_line1[1]  # Adjust vertical position for two lines
-
-                for dx in range(-outline_thickness, outline_thickness + 1):
-                    for dy in range(-outline_thickness, outline_thickness + 1):
-                        if dx != 0 or dy != 0:
-                            draw.text((text_x_line1 + dx, text_y + dy), line1, font=font, fill=outline_color)
-                            draw.text((text_x_line2 + dx, text_y + text_size_line1[1] + dy), line2, font=font, fill=outline_color)
-                
-                draw.text((text_x_line1, text_y), line1, font=font, fill=fontcolor)
-                draw.text((text_x_line2, text_y + text_size_line1[1]), line2, font=font, fill=fontcolor)
-
-            else:
-                for dx in range(-outline_thickness, outline_thickness + 1):
-                    for dy in range(-outline_thickness, outline_thickness + 1):
-                        if dx != 0 or dy != 0:
-                            draw.text((text_x + dx, text_y + dy), chunk, font=font, fill=outline_color)
-                
-                draw.text((text_x, text_y), chunk, font=font, fill=fontcolor)
-
-            frame = cv2.cvtColor(np.array(frame_pil), cv2.COLOR_RGB2BGR)
-
-        out.write(frame)
-        current_frame += 1
-
-    cap.release()
-    out.release()
-    cv2.destroyAllWindows()
-
-def apply_zoom_in_effect(clip, zoom_factor=1.2):
-    width, height = clip.size
-    duration = clip.duration
-
-    def zoom_in_effect(get_frame, t):
-        frame = get_frame(t)
-        zoom = 1 + (zoom_factor - 1) * (t / duration)
-        new_width, new_height = int(width * zoom), int(height * zoom)
-        resized_frame = cv2.resize(frame, (new_width, new_height))
-        
-        x_start = (new_width - width) // 2
-        y_start = (new_height - height) // 2
-        cropped_frame = resized_frame[y_start:y_start + height, x_start:x_start + width]
-        
-        return cropped_frame
-
-    return clip.fl(zoom_in_effect, apply_to=['mask'])
-
-@tool(args_schema=VideoGeneration)
-def create_video_from_images_and_audio(images_dir, speeches_dir, zoom_factor=1.2):
-    """Creates video using images and audios.
-    Args:
-    images_dir: path to images folder, example 'outputs/images'
-    speeches_dir: path to speeches folder, example 'outputs/speeches'"""
-    images_paths = sorted(os.listdir(os.path.join(os.path.dirname(os.path.abspath(__file__)),images_dir)))
-    audio_paths = sorted(os.listdir(os.path.join(os.path.dirname(os.path.abspath(__file__)),speeches_dir)))
-    clips = []
-    temp_files = []
-    
-    for i in range(min(len(images_paths), len(audio_paths))):
-        img_clip = ImageClip(os.path.join(os.path.dirname(os.path.abspath(__file__)),images_dir, images_paths[i]))
-        audioclip = AudioFileClip(os.path.join(os.path.dirname(os.path.abspath(__file__)),speeches_dir, audio_paths[i]))
-        videoclip = img_clip.set_duration(audioclip.duration)
-        zoomed_clip = apply_zoom_in_effect(videoclip, zoom_factor)
-        
-        with open(os.path.join(os.path.dirname(os.path.abspath(__file__)),speeches_dir, audio_paths[i]), "rb") as file:
-            transcription = client.audio.transcriptions.create(
-                file=(audio_paths[i], file.read()),
-                model="whisper-large-v3",
-                response_format="verbose_json",
-            )
-            caption = transcription.text
-        
-        temp_video_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), f"outputs/final_video/temp_zoomed_{i}.mp4")
-        zoomed_clip.write_videofile(temp_video_path, codec='libx264', fps=24)
-        temp_files.append(temp_video_path)
-        
-        final_video_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), f"outputs/final_video/temp_captioned_{i}.mp4")
-        add_text_to_video(temp_video_path, final_video_path, caption, duration=1, fontsize=60)
-        temp_files.append(final_video_path)
-        
-        final_clip = VideoFileClip(final_video_path)
-        final_clip = final_clip.set_audio(audioclip)
-        
-        clips.append(final_clip)
-    
-    final_clip = concatenate_videoclips(clips)
-    final_clip.write_videofile(os.path.join(os.path.dirname(os.path.abspath(__file__)), "outputs/final_video/final_video.mp4"), codec='libx264', fps=24)
-    
-    # Close all video files properly
-    for clip in clips:
-        clip.close()
-        
-    # Remove all temporary files
-    for temp_file in temp_files:
-        try:
-            os.remove(temp_file)
-        except Exception as e:
-            print(f"Error removing file {temp_file}: {e}")
-    
-    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "outputs/final_video/final_video.mp4")
-
-# Example usage
-# image_paths = "outputs/images"
-# audio_paths = "outputs/speeches"
-
-# video_path = create_video_from_images_and_audio(image_paths, audio_paths)
-# print(f"Video created at: {video_path}")
-
-class WikiInputs(BaseModel):
-    """Inputs to the wikipedia tool."""
-    query: str = Field(description="query to look up in Wikipedia, should be 3 or less words")
-
-api_wrapper = WikipediaAPIWrapper(top_k_results=3)#, doc_content_chars_max=100)
-
-wiki_tool = WikipediaQueryRun(
-    name="wiki-tool",
-    description="{query:'input here'}",
-    args_schema=WikiInputs,
-    api_wrapper=api_wrapper,
-    return_direct=True,
-)
-
-wiki = Tool(
-    name = 'wikipedia',
-    func = wiki_tool.run,
-    description= "{query:'input here'}"
-)
-
-# wiki_tool.run("latest news in India")
-
-# @tool
-def process_script(script):
-    """Used to process the script into dictionary format"""
-    dict = {}
-    dict['text_for_image_generation'] = re.findall(r'<image>(.*?)</?image>', script)
-    dict['text_for_speech_generation'] = re.findall(r'<narration>.*?</?narration>', script)
-    return dict
-
-@tool#(args_schema=ImageGeneration)
-def image_generator(script):
-    """Generates images for the given script.
-    Saves it to images_dir and return path
-    Args:
-    script: a complete script containing narrations and image descriptions"""
-    images_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), './outputs/images')
-    # if num==1:
-    for filename in os.listdir(images_dir):
-        file_path = os.path.join(images_dir, filename)
-        if os.path.isfile(file_path):
-            os.remove(file_path)
-    
-    dict = process_script(script)
-    for i, text in enumerate(dict['text_for_image_generation']):
-        # image = pipe(text, num_inference_steps=12, guidance_scale=2, width=720, height=1280, verbose=0).images[0]
-        # image.save(os.path.join(images_dir, f'image{i}.jpg'))
-        response = requests.post(
-        f"https://api.stability.ai/v2beta/stable-image/generate/core",
-        headers={
-            "authorization": os.environ.get('STABILITY_AI_API_KEY'),
-            "accept": "image/*"
-        },
-        files={"none": ''},
-        data={
-            "prompt": text,
-            "output_format": "png",
-            'aspect_ratio': "9:16",
-        },
-        )
-
-        if response.status_code == 200:
-            with open(os.path.join(images_dir, f'image_{i}.png'), 'wb') as file:
-                file.write(response.content)
-        else:
-            raise Exception(str(response.json()))
-    return f'images generated.'#f'image generated for "{text}" and saved to directory {images_dir} as image{num}.jpg'
-
-@tool
-def speech_generator(script):
-    """Generates speech for given text
-    Saves it to speech_dir and return path
-    Args:
-    script: a complete script containing narrations and image descriptions"""
-    speech_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), './outputs/speeches')
-
-    # if num==1:
-    for filename in os.listdir(speech_dir):
-        file_path = os.path.join(speech_dir, filename)
-        if os.path.isfile(file_path):
-            os.remove(file_path)
-    
-    dict = process_script(script)
-    print(dict)
-    for i, text in enumerate(dict['text_for_speech_generation']):
-        generate_speech(text, speech_dir, num=i)
+from langchain.tools import tool, Tool
+import re
+import os
+from langchain_groq import ChatGroq
+import requests
+import cv2
+from moviepy.editor import ImageClip, AudioFileClip, concatenate_videoclips
+from langchain.pydantic_v1 import BaseModel, Field
+from langchain_community.tools import WikipediaQueryRun
+from langchain_community.utilities import WikipediaAPIWrapper
+
+# from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler
+# import bitsandbytes as bnb
+# import torch.nn as nn
+# import torch
+import pyttsx3
+# from agents import get_agents_and_tasks
+# from langchain_google_genai import ChatGoogleGenerativeAI
+
+# from langchain.chat_models import ChatOpenAI
+# # llm2 = ChatOpenAI(model='gpt-3.5-turbo')
+# # llm3 = ChatOpenAI(model='gpt-3.5-turbo')
+# llm1 = ChatGroq(model='llama3-70b-8192', temperature=0.6, max_tokens=2048)
+# # llm2 = ChatGroq(model='mixtral-8x7b-32768', temperature=0.6, max_tokens=2048, api_key='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')
+# llm2 = ChatGoogleGenerativeAI(model='gemini-pro', temperature=0.0)
+# # llm2 = ChatGroq(model='llama3-70b-8192', temperature=0.6, max_tokens=2048, api_key='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')
+# # llm3 = ChatGoogleGenerativeAI(model='gemini-pro')
+# llm4 = ChatGroq(model='llama3-70b-8192', temperature=0.6, max_tokens=2048, api_key='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')
+# groq_api_key=os.environ.get('GROQ_API_KEY')
+# llm = ChatGroq(model='llama3-70b-8192', temperature=0.6, max_tokens=1024, api_key=groq_api_key)
+
+# pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash", torch_dtype=torch.float16).to('cuda')
+# pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+
+# def quantize_model_to_4bit(model):
+#     replacements = []
+
+#     # Collect layers to be replaced
+#     for name, module in model.named_modules():
+#         if isinstance(module, nn.Linear):
+#             replacements.append((name, module))
+
+#     # Replace layers
+#     for name, module in replacements:
+#         # Split the name to navigate to the parent module
+#         *path, last = name.split('.')
+#         parent = model
+#         for part in path:
+#             parent = getattr(parent, part)
+
+#         # Create and assign the quantized layer
+#         quantized_layer = bnb.nn.Linear4bit(module.in_features, module.out_features, bias=module.bias is not None)
+#         quantized_layer.weight.data = module.weight.data
+#         if module.bias is not None:
+#             quantized_layer.bias.data = module.bias.data
+#         setattr(parent, last, quantized_layer)
+
+#     return model
+
+# pipe.unet = quantize_model_to_4bit(pipe.unet)
+# pipe.enable_model_cpu_offload()
+
+
+def generate_speech(text, speech_dir='./outputs/audio', lang='en', speed=170, voice='default', num=0):
+    """
+    Generates speech for given script.
+    """
+    engine = pyttsx3.init()
+    
+    # Set language and voice
+    voices = engine.getProperty('voices')
+    if voice == 'default':
+        voice_id = voices[1].id
+    else:
+        # Try to find the voice with the given name
+        voice_id = None
+        for v in voices:
+            if voice in v.name:
+                voice_id = v.id
+                break
+        if not voice_id:
+            raise ValueError(f"Voice '{voice}' not found.")
+    
+    engine.setProperty('voice', voice_id)
+    engine.setProperty('rate', speed)
+    os.remove(os.path.join(os.path.dirname(os.path.abspath(__file__)), speech_dir, f'speech_{num}.mp3')) if os.path.exists(os.path.join(speech_dir, f'speech_{num}.mp3')) else None
+    engine.save_to_file(text, os.path.join(os.path.dirname(os.path.abspath(__file__)), speech_dir, f'speech_{num}.mp3'))
+    engine.runAndWait()
+
+# class VideoGeneration(BaseModel):
+#     images_dir : str = Field(description='Path to images directory, such as "outputs/images"')
+#     speeches_dir : str = Field(description='Path to speeches directory, such as "outputs/speeches"')
+
+# @tool(args_schema=VideoGeneration)
+# def create_video_from_images_and_audio(images_dir, speeches_dir, zoom_factor=1.2):
+#     """Creates video using images and audios with zoom-in effect"""
+#     images_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), images_dir)
+#     speeches_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), speeches_dir)
+
+#     images_paths = os.listdir(images_dir)
+#     audio_paths = os.listdir(speeches_dir)
+#     # print(images_paths, audio_paths)
+#     clips = []
+    
+#     for i in range(min(len(images_paths), len(audio_paths))):
+#         # Load the image
+#         img_clip = ImageClip(os.path.join(images_dir, images_paths[i]))
+        
+#         # Load the audio file
+#         audioclip = AudioFileClip(os.path.join(speeches_dir, audio_paths[i]))
+        
+#         # Set the duration of the video clip to the duration of the audio file
+#         videoclip = img_clip.set_duration(audioclip.duration)
+        
+#         # Apply zoom-in effect to the video clip
+#         zoomed_clip = apply_zoom_in_effect(videoclip, zoom_factor)
+        
+#         # Add audio to the zoomed video clip
+#         zoomed_clip = zoomed_clip.set_audio(audioclip)
+        
+#         clips.append(zoomed_clip)
+    
+#     # Concatenate all video clips
+#     final_clip = concatenate_videoclips(clips)
+    
+#     # Write the result to a file
+#     final_clip.write_videofile(os.path.join(os.path.dirname(os.path.abspath(__file__)), "outputs/final_video/final_video.mp4"), codec='libx264', fps=24)
+    
+#     return os.path.join(os.path.dirname(os.path.abspath(__file__)), "outputs/final_video/final_video.mp4")
+
+# def apply_zoom_in_effect(clip, zoom_factor=1.2):
+#     width, height = clip.size
+#     duration = clip.duration
+
+#     def zoom_in_effect(get_frame, t):
+#         frame = get_frame(t)
+#         zoom = 1 + (zoom_factor - 1) * (t / duration)
+#         new_width, new_height = int(width * zoom), int(height * zoom)
+#         resized_frame = cv2.resize(frame, (new_width, new_height))
+        
+#         # Calculate the position to crop the frame to the original size
+#         x_start = (new_width - width) // 2
+#         y_start = (new_height - height) // 2
+#         cropped_frame = resized_frame[y_start:y_start + height, x_start:x_start + width]
+        
+#         return cropped_frame
+
+#     return clip.fl(zoom_in_effect, apply_to=['mask'])
+
+# Example usage
+# image_paths = "outputs/images"
+# audio_paths = "outputs/audio"
+
+# video_path = create_video_from_images_and_audio(image_paths, audio_paths)
+# print(f"Video created at: {video_path}")
+
+
+# class ImageGeneration(BaseModel):
+#     text : str = Field(description='description of sentence used for image generation')
+#     num : int = Field(description='sequence of description passed this tool. Used in image saving path. Example 1,2,3,4,5 and so on')
+
+# class SpeechGeneration(BaseModel):
+#     text : str = Field(description='description of sentence used for image generation')
+#     num : int = Field(description='sequence of description passed this tool. Used in image saving path. Example 1,2,3,4,5 and so on')
+
+import os
+import cv2
+from moviepy.editor import ImageClip, AudioFileClip, concatenate_videoclips, VideoFileClip
+from PIL import Image, ImageDraw, ImageFont
+import numpy as np
+from groq import Groq
+
+
+
+class VideoGeneration(BaseModel):
+    images_dir: str = Field(description='Path to images directory, such as "outputs/images"')
+    speeches_dir: str = Field(description='Path to speeches directory, such as "outputs/speeches"')
+
+def split_text_into_chunks(text, chunk_size):
+    words = text.split()
+    return [' '.join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
+
+def add_text_to_video(input_video, output_video, text, duration=1, fontsize=40, fontcolor=(255, 255, 255),
+                      outline_thickness=2, outline_color=(0, 0, 0), delay_between_chunks=0.1,
+                      font_path=os.path.join(os.path.dirname(os.path.abspath(__file__)),'Montserrat-Bold.ttf')):
+    
+    chunks = split_text_into_chunks(text, 3)  # Adjust chunk size as needed
+
+    cap = cv2.VideoCapture(input_video)
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    out = cv2.VideoWriter(output_video, fourcc, fps, (width, height))
+
+    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    chunk_duration_frames = duration * fps
+    delay_frames = int(delay_between_chunks * fps)
+
+    font = ImageFont.truetype(font_path, fontsize)
+
+    current_frame = 0
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        draw = ImageDraw.Draw(frame_pil)
+
+        chunk_index = current_frame // (chunk_duration_frames + delay_frames)
+
+        if current_frame % (chunk_duration_frames + delay_frames) < chunk_duration_frames and chunk_index < len(chunks):
+            chunk = chunks[chunk_index]
+            text_width, text_height = draw.textsize(chunk, font=font)
+            text_x = (width - text_width) // 2
+            text_y = height - 400  # Position text at the bottom
+
+            if text_width > width:
+                words = chunk.split()
+                half = len(words) // 2
+                line1 = ' '.join(words[:half])
+                line2 = ' '.join(words[half:])
+
+                text_size_line1 = draw.textsize(line1, font=font)
+                text_size_line2 = draw.textsize(line2, font=font)
+                text_x_line1 = (width - text_size_line1[0]) // 2
+                text_x_line2 = (width - text_size_line2[0]) // 2
+                text_y = height - 250 - text_size_line1[1]  # Adjust vertical position for two lines
+
+                for dx in range(-outline_thickness, outline_thickness + 1):
+                    for dy in range(-outline_thickness, outline_thickness + 1):
+                        if dx != 0 or dy != 0:
+                            draw.text((text_x_line1 + dx, text_y + dy), line1, font=font, fill=outline_color)
+                            draw.text((text_x_line2 + dx, text_y + text_size_line1[1] + dy), line2, font=font, fill=outline_color)
+                
+                draw.text((text_x_line1, text_y), line1, font=font, fill=fontcolor)
+                draw.text((text_x_line2, text_y + text_size_line1[1]), line2, font=font, fill=fontcolor)
+
+            else:
+                for dx in range(-outline_thickness, outline_thickness + 1):
+                    for dy in range(-outline_thickness, outline_thickness + 1):
+                        if dx != 0 or dy != 0:
+                            draw.text((text_x + dx, text_y + dy), chunk, font=font, fill=outline_color)
+                
+                draw.text((text_x, text_y), chunk, font=font, fill=fontcolor)
+
+            frame = cv2.cvtColor(np.array(frame_pil), cv2.COLOR_RGB2BGR)
+
+        out.write(frame)
+        current_frame += 1
+
+    cap.release()
+    out.release()
+    cv2.destroyAllWindows()
+
+def apply_zoom_in_effect(clip, zoom_factor=1.2):
+    width, height = clip.size
+    duration = clip.duration
+
+    def zoom_in_effect(get_frame, t):
+        frame = get_frame(t)
+        zoom = 1 + (zoom_factor - 1) * (t / duration)
+        new_width, new_height = int(width * zoom), int(height * zoom)
+        resized_frame = cv2.resize(frame, (new_width, new_height))
+        
+        x_start = (new_width - width) // 2
+        y_start = (new_height - height) // 2
+        cropped_frame = resized_frame[y_start:y_start + height, x_start:x_start + width]
+        
+        return cropped_frame
+
+    return clip.fl(zoom_in_effect, apply_to=['mask'])
+
+@tool(args_schema=VideoGeneration)
+def create_video_from_images_and_audio(images_dir, speeches_dir, zoom_factor=1.2):
+    """Creates video using images and audios.
+    Args:
+    images_dir: path to images folder, example 'outputs/images'
+    speeches_dir: path to speeches folder, example 'outputs/speeches'"""
+    client = Groq()
+    images_paths = sorted(os.listdir(os.path.join(os.path.dirname(os.path.abspath(__file__)),images_dir)))
+    audio_paths = sorted(os.listdir(os.path.join(os.path.dirname(os.path.abspath(__file__)),speeches_dir)))
+    clips = []
+    temp_files = []
+    
+    for i in range(min(len(images_paths), len(audio_paths))):
+        img_clip = ImageClip(os.path.join(os.path.dirname(os.path.abspath(__file__)),images_dir, images_paths[i]))
+        audioclip = AudioFileClip(os.path.join(os.path.dirname(os.path.abspath(__file__)),speeches_dir, audio_paths[i]))
+        videoclip = img_clip.set_duration(audioclip.duration)
+        zoomed_clip = apply_zoom_in_effect(videoclip, zoom_factor)
+        
+        with open(os.path.join(os.path.dirname(os.path.abspath(__file__)),speeches_dir, audio_paths[i]), "rb") as file:
+            transcription = client.audio.transcriptions.create(
+                file=(audio_paths[i], file.read()),
+                model="whisper-large-v3",
+                response_format="verbose_json",
+            )
+            caption = transcription.text
+        
+        temp_video_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), f"outputs/final_video/temp_zoomed_{i}.mp4")
+        zoomed_clip.write_videofile(temp_video_path, codec='libx264', fps=24)
+        temp_files.append(temp_video_path)
+        
+        final_video_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), f"outputs/final_video/temp_captioned_{i}.mp4")
+        add_text_to_video(temp_video_path, final_video_path, caption, duration=1, fontsize=60)
+        temp_files.append(final_video_path)
+        
+        final_clip = VideoFileClip(final_video_path)
+        final_clip = final_clip.set_audio(audioclip)
+        
+        clips.append(final_clip)
+    
+    final_clip = concatenate_videoclips(clips)
+    final_clip.write_videofile(os.path.join(os.path.dirname(os.path.abspath(__file__)), "outputs/final_video/final_video.mp4"), codec='libx264', fps=24)
+    
+    # Close all video files properly
+    for clip in clips:
+        clip.close()
+        
+    # Remove all temporary files
+    for temp_file in temp_files:
+        try:
+            os.remove(temp_file)
+        except Exception as e:
+            print(f"Error removing file {temp_file}: {e}")
+    
+    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "outputs/final_video/final_video.mp4")
+
+# Example usage
+# image_paths = "outputs/images"
+# audio_paths = "outputs/speeches"
+
+# video_path = create_video_from_images_and_audio(image_paths, audio_paths)
+# print(f"Video created at: {video_path}")
+
+class WikiInputs(BaseModel):
+    """Inputs to the wikipedia tool."""
+    query: str = Field(description="query to look up in Wikipedia, should be 3 or less words")
+
+api_wrapper = WikipediaAPIWrapper(top_k_results=3)#, doc_content_chars_max=100)
+
+wiki_tool = WikipediaQueryRun(
+    name="wiki-tool",
+    description="{query:'input here'}",
+    args_schema=WikiInputs,
+    api_wrapper=api_wrapper,
+    return_direct=True,
+)
+
+wiki = Tool(
+    name = 'wikipedia',
+    func = wiki_tool.run,
+    description= "{query:'input here'}"
+)
+
+# wiki_tool.run("latest news in India")
+
+# @tool
+def process_script(script):
+    """Used to process the script into dictionary format"""
+    dict = {}
+    dict['text_for_image_generation'] = re.findall(r'<image>(.*?)</?image>', script)
+    dict['text_for_speech_generation'] = re.findall(r'<narration>.*?</?narration>', script)
+    return dict
+
+@tool#(args_schema=ImageGeneration)
+def image_generator(script):
+    """Generates images for the given script.
+    Saves it to images_dir and return path
+    Args:
+    script: a complete script containing narrations and image descriptions"""
+    images_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), './outputs/images')
+    # if num==1:
+    for filename in os.listdir(images_dir):
+        file_path = os.path.join(images_dir, filename)
+        if os.path.isfile(file_path):
+            os.remove(file_path)
+    
+    dict = process_script(script)
+    for i, text in enumerate(dict['text_for_image_generation']):
+        # image = pipe(text, num_inference_steps=12, guidance_scale=2, width=720, height=1280, verbose=0).images[0]
+        # image.save(os.path.join(images_dir, f'image{i}.jpg'))
+        response = requests.post(
+        f"https://api.stability.ai/v2beta/stable-image/generate/core",
+        headers={
+            "authorization": os.environ.get('STABILITY_AI_API_KEY'),
+            "accept": "image/*"
+        },
+        files={"none": ''},
+        data={
+            "prompt": text,
+            "output_format": "png",
+            'aspect_ratio': "9:16",
+        },
+        )
+
+        if response.status_code == 200:
+            with open(os.path.join(images_dir, f'image_{i}.png'), 'wb') as file:
+                file.write(response.content)
+        else:
+            raise Exception(str(response.json()))
+    return f'images generated.'#f'image generated for "{text}" and saved to directory {images_dir} as image{num}.jpg'
+
+@tool
+def speech_generator(script):
+    """Generates speech for given text
+    Saves it to speech_dir and return path
+    Args:
+    script: a complete script containing narrations and image descriptions"""
+    speech_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), './outputs/speeches')
+
+    # if num==1:
+    for filename in os.listdir(speech_dir):
+        file_path = os.path.join(speech_dir, filename)
+        if os.path.isfile(file_path):
+            os.remove(file_path)
+    
+    dict = process_script(script)
+    print(dict)
+    for i, text in enumerate(dict['text_for_speech_generation']):
+        generate_speech(text, speech_dir, num=i)
     return f'speechs generated.'#f'speech generated for "{text}" and saved to directory {speech_dir} as speech{num}.mp3'