Update app.py

app.py

@@ -6,7 +6,6 @@ import openai
 import requests
 import json
 import streamlit.components.v1 as components
-from streamlit_drawable_canvas import st_canvas
 import webbrowser
 import pickle
 import random
@@ -167,151 +166,6 @@ def update_chat(messages, role, content):
     messages.append({"role": role, "content": content})
     return messages
 
-
-# # Defining Stable Diffusion Methods
-# def get_image(key: str) -> Optional[Image.Image]:
-#     if key in st.session_state:
-#         return st.session_state[key]
-#     return None
-
-
-# def set_image(key: str, img: Image.Image):
-#     st.session_state[key] = img
-
-
-# def prompt_and_generate_button(prefix, pipeline_name: PIPELINE_NAMES, **kwargs):
-#     prompt = st.text_area(
-#         "Prompt",
-#         value=DEFAULT_PROMPT,
-#         key=f"{prefix}-prompt",
-#     )
-#     negative_prompt = st.text_area(
-#         "Negative prompt",
-#         value="",
-#         key=f"{prefix}-negative-prompt",
-#     )
-#     steps = st.slider("Number of inference steps", min_value=1, max_value=200, value=50, key=f"{prefix}-steps")
-#     guidance_scale = st.slider(
-#         "Guidance scale", min_value=0.0, max_value=20.0, value=7.5, step=0.5,key=f"{prefix}-guidance"
-#     )
-
-#     if st.button("Generate image", key=f"{prefix}-btn"):
-#         with st.spinner("Generating image..."):
-#             image = generate(
-#                 prompt,
-#                 pipeline_name,
-#                 negative_prompt=negative_prompt,
-#                 steps=steps,
-#                 guidance_scale=guidance_scale,
-#                 **kwargs,
-#             )
-#             set_image(OUTPUT_IMAGE_KEY, image.copy())
-#         st.image(image)
-
-
-# def width_and_height_sliders(prefix):
-#     col1, col2 = st.columns(2)
-#     with col1:
-#         width = st.slider(
-#             "Width",
-#             min_value=512,
-#             max_value=1024,
-#             step=64,
-#             value=512,
-#             key=f"{prefix}-width",
-#         )
-#     with col2:
-#         height = st.slider(
-#             "Height",
-#             min_value=512,
-#             max_value=1024,
-#             step=64,
-#             value=512,
-#             key=f"{prefix}-height",
-#         )
-#     return width, height
-
-
-# def image_uploader(prefix):
-#     image = st.file_uploader("Image", ["jpg", "png"], key=f"{prefix}-uploader")
-#     if image:
-#         image = Image.open(image)
-#         print(f"loaded input image of size ({image.width}, {image.height})")
-#         image = image.resize((DEFAULT_WIDTH, DEFAULT_HEIGHT))
-#         return image
-
-#     return get_image(LOADED_IMAGE_KEY)
-
-
-# def inpainting():
-#     image = image_uploader("inpainting")
-
-#     if not image:
-#         return None, None
-
-#     brush_size = st.number_input("Brush Size", value=50, min_value=1, max_value=100)
-
-#     canvas_result = st_canvas(
-#         fill_color="rgba(255, 255, 255, 0.0)",
-#         stroke_width=brush_size,
-#         stroke_color="#FFFFFF",
-#         background_color="#000000",
-#         background_image=image,
-#         update_streamlit=True,
-#         height=image.height,
-#         width=image.width,
-#         drawing_mode="freedraw",
-#         # Use repr(image) to force the component to reload when the image
-#         # changes, i.e. when asking to use the current output image
-#         key="inpainting",
-#     )
-
-#     if not canvas_result or canvas_result.image_data is None:
-#         return None, None
-
-#     mask = canvas_result.image_data
-#     mask = mask[:, :, -1] > 0
-#     if mask.sum() > 0:
-#         mask = Image.fromarray(mask)
-#         st.image(mask)
-#         return image, mask
-
-#     return None, None
-
-
-# def txt2img_tab():
-#     prefix = "txt2img"
-#     width, height = width_and_height_sliders(prefix)
-#     prompt_and_generate_button(prefix, "txt2img", width=width, height=height)
-
-
-# def inpainting_tab():
-#     col1, col2 = st.columns(2)
-
-#     with col1:
-#         image_input, mask_input = inpainting()
-
-#     with col2:
-#         if image_input and mask_input:
-#             prompt_and_generate_button(
-#                 "inpaint", "inpaint", image_input=image_input, mask_input=mask_input
-#             )
-
-
-# def img2img_tab():
-#     col1, col2 = st.columns(2)
-
-#     with col1:
-#         image = image_uploader("img2img")
-#         if image:
-#             st.image(image)
-
-#     with col2:
-#         if image:
-#             prompt_and_generate_button("img2img", "img2img", image_input=image)
-
-# # End of Stable Diffusion Methods
-
 # ------
 # Define image sizes
 image_sizes = {
@@ -2866,96 +2720,4 @@ Format the text as follows using HTML code and H2 sub headings: [introduction
 
                     # Allow the user to view the conversation history and other information stored in the agent's memory
                     with st.expander("History/Memory"):
-                        st.session_state.memory
-    # elif selected == "GPT-LipSync":
-    #     genre = st.radio(
-    #     "What type of content do you want to generate?",
-    #     ('chatGPT', 'Fixed'))
-    #     if genre == 'chatGPT':
-    #         # Stage 1: Choose avatar image
-    #         st.subheader("Stage 1: Choose Avatar Image")
-    #         col1, col2 = st.columns([1, 2])
-    #         with col1:
-    #             avatar_images = ["avatar1.jpg", "avatar2.jpg","avatar3.jpg", "avatar4.jpg"]
-    #             selected_avatar_index = st.selectbox("Choose your avatar image", range(len(avatar_images)), format_func=lambda i: avatar_images[i], index=0)
-    #             # print(selected_avatar_index)
-    #         with col2:
-    #             st.image(avatar_images[selected_avatar_index], width=200)
-    #         # # avatar_images = ["avatar1.jpg", "avatar2.jpg", "avatar3.jpg", "avatar4.jpg", "avatar5.jpg", "avatar6.jpg"]
-    #         # avatar_images = ["avatar1.jpg", "avatar2.jpg"]
-    #         # selected_avatar_index = st.selectbox("Choose your avatar image", range(len(avatar_images)), format_func=lambda i: avatar_images[i], index=0)
-            
-    #         st.subheader("Stage 2: Generate Video Script")
-    #         st.info("As soon as you enter the prompt, press CTRL+Enter or just click anywhere on the black screen!")
-    #         prompt = st.text_area("Enter your prompt here", height=200)
-    #         if prompt:
-    #             st.info("Generating video script...")
-    #             completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[
-    #                             {"role": "system", "content": "You are an AI assistant custom trained and created by Alpha AI. You are proficient at everytask."},
-    #                             {"role": "user", "content": "Generate only a first person video script for the speaker on " + prompt}
-    #                             ],max_tokens=2500, temperature = 0.6,presence_penalty = 0.1,frequency_penalty = 0.1)
-    #                         # print(type(completion))
-    #             video_script = completion.choices[0].message.content
-    #             st.success("Video script generated successfully!")
-    #             edit_script = st.text_area("Edit your video script here", value=video_script, height=200)
-    #             if st.button("Accept Script", type="primary"):
-    #                 video_script = edit_script.strip()
-    #                 if text_to_speech_avatar(video_script):
-    #                     st.success("Audio has been generated! Go to Stage 3")
-    #         # Stage 3: Generate lip sync video
-    #         st.subheader("Stage 3: Generate Lip Sync Video")
-    #         if st.button("Generate Video", type="primary"):
-    #             st.spinner("Generating lip sync video...")
-    #             # st.info("Generating lip sync video...")
-    #             dv_index = selected_avatar_index + 1
-    #             # avatar_image = avatar_images[selected_avatar_index]
-    #             driving_video = f"{dv_index}.mp4"
-    #             addition_name = st.session_state['name'][:5]
-    #             aud_name = "dummy_" + addition_name
-    #             output_vid = "output_" + addition_name
-    #             # os.system(rf"python inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face {driving_video} --audio 'temp/dummy.mp3' --outfile output.mp4")
-    #             # subprocess.run(["python", "generate_video.py", avatar_image, driving_video, "audio.wav"])
-    #             cmd = f"python inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face {driving_video} --audio temp/{aud_name}.mp3 --outfile {output_vid}.mp4"
-    #             subprocess.run(cmd, shell=True)
-    #             # subprocess.run(["python", "inference.py","--checkpoint_path checkpoints/wav2lip_gan.pth","--face ",driving_video,"--audio temp/dummy.mp3","--outfile output.mp4"])
-    #             st.success("Lip sync video generated successfully!")
-    #             st.video(output_vid+".mp4")
-        # elif genre == 'Fixed':
-        #                 # Stage 1: Choose avatar image
-        #     st.subheader("Stage 1: Choose Avatar Image")
-        #     col1, col2 = st.columns([1, 2])
-        #     with col1:
-        #         avatar_images = ["avatar1.jpg", "avatar2.jpg","avatar3.jpg", "avatar4.jpg"]
-        #         selected_avatar_index = st.selectbox("Choose your avatar image", range(len(avatar_images)), format_func=lambda i: avatar_images[i], index=0)
-        #         # print(selected_avatar_index)
-        #     with col2:
-        #         st.image(avatar_images[selected_avatar_index], width=200)
-        #     # # avatar_images = ["avatar1.jpg", "avatar2.jpg", "avatar3.jpg", "avatar4.jpg", "avatar5.jpg", "avatar6.jpg"]
-        #     # avatar_images = ["avatar1.jpg", "avatar2.jpg"]
-        #     # selected_avatar_index = st.selectbox("Choose your avatar image", range(len(avatar_images)), format_func=lambda i: avatar_images[i], index=0)
-            
-        #     st.subheader("Stage 2: Enter the Script")
-        #     st.info("As soon as you enter the prompt, press CTRL+Enter or just click anywhere on the black screen!")
-        #     prompt = st.text_area("Enter your prompt here", height=200)
-        #     if st.button("Generate Script"):
-        #         st.info("Generating video script...")
-        #         if text_to_speech_avatar(prompt):
-        #             st.success("Audio has been generated! Go to Stage 3")
-        #     # Stage 3: Generate lip sync video
-        #     st.subheader("Stage 3: Generate Lip Sync Video")
-        #     if st.button("Generate Video", type="primary"):
-        #         st.spinner("Generating lip sync video...")
-        #         # st.info("Generating lip sync video...")
-        #         dv_index = selected_avatar_index + 1
-        #         # avatar_image = avatar_images[selected_avatar_index]
-        #         driving_video = f"{dv_index}.mp4"
-        #         addition_name = st.session_state['name'][:5]
-        #         aud_name = "dummy_" + addition_name
-        #         output_vid = "output_" + addition_name
-        #         # os.system(rf"python inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face {driving_video} --audio 'temp/dummy.mp3' --outfile output.mp4")
-        #         # subprocess.run(["python", "generate_video.py", avatar_image, driving_video, "audio.wav"])
-        #         cmd = f"python inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face {driving_video} --audio temp/{aud_name}.mp3 --outfile {output_vid}.mp4"
-        #         subprocess.run(cmd, shell=True)
-        #         # subprocess.run(["python", "inference.py","--checkpoint_path checkpoints/wav2lip_gan.pth","--face ",driving_video,"--audio temp/dummy.mp3","--outfile output.mp4"])
-        #         st.success("Lip sync video generated successfully!")
-        #         st.video(output_vid+".mp4")
+                        st.session_state.memory