From e7cd60e82f0780bc2326a87668178956e03a5d04 Mon Sep 17 00:00:00 2001 From: AstraBert Date: Thu, 23 May 2024 14:33:51 +0200 Subject: [PATCH] v2.0.0 --- README.md | 8 ++ docker/Dockerfile | 4 +- docker/audio_classification.py | 50 ++++++++++ docker/autotrain_interface.py | 43 +++++++++ docker/protein_folding_with_esm.py | 60 ++++++++++++ docker/proteins_viz.py | 135 +++++++++++++++++++++++++++ docker/select_and_run.py | 50 ++++++++-- docker/spaces_api_supabase.py | 145 +++++++++++++++++++++++++++++ docker/speech_recognition.py | 51 ++++++++++ docker/utils.py | 16 +++- docker/video_generation.py | 71 ++++++++++++++ imgs/everything-ai.drawio.png | Bin 159833 -> 472235 bytes 12 files changed, 623 insertions(+), 10 deletions(-) create mode 100644 docker/audio_classification.py create mode 100644 docker/autotrain_interface.py create mode 100644 docker/protein_folding_with_esm.py create mode 100644 docker/proteins_viz.py create mode 100644 docker/spaces_api_supabase.py create mode 100644 docker/speech_recognition.py create mode 100644 docker/video_generation.py diff --git a/README.md b/README.md index 066e80c..7ecd194 100644 --- a/README.md +++ b/README.md @@ -57,6 +57,12 @@ Choose the task among: - *image-generation-pollinations*: stable diffusion, use Pollinations AI API; if you choose 'image-generation-pollinations', you do not need to specify anything else apart from the task - **MULTILINGUAL** - *image-classification*: classify an image, supports every image-classification model on HF Hub - **ENGLISH ONLY** - *image-to-text*: describe an image, supports every image-to-text model on HF Hub - **ENGLISH ONLY** +- *audio-classification*: classify audio files or microphone recordings, supports audio-classification models on HF hub +- *speech-recognition*: transcribe audio files or microphone recordings, supports automatic-speech-recognition models on HF hub. +- *video-generation*: generate video upon text prompt, supports text-to-video models on HF hub - **ENGLISH ONLY** +- *protein-folding*: get the 3D structure of a protein from its amino-acid sequence, using ESM-2 backbone model - **GPU ONLY** +- *autotrain*: fine-tune a model on a specific downstream task with autotrain-advanced, just by specifying you HF username, HF writing token and the path to a yaml config file for the training +- *spaces-api-supabase*: use HF Spaces API in combination with Supabase PostgreSQL databases in order to unleash more powerful LLMs and larger RAG-oriented vector databases - **MULTILINGUAL** - *image-retrieval-search*: search an image database uploading a folder as database input. The folder should have the following structure: ``` @@ -68,6 +74,8 @@ Choose the task among: ├── label1/ └── label2/ ``` + + You can query the database starting from your own pictures. ### 6. Go to `localhost:7860` and start using your assistant diff --git a/docker/Dockerfile b/docker/Dockerfile index 71c96e1..5d62dec 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -7,8 +7,8 @@ WORKDIR /app # Add the current directory contents into the container at /app ADD . /app -# Add new package -RUN python3 -m pip install datasets==2.15.0 +#Upgrade gradio +RUN pip install autotrain-advanced # Expose the port that the application will run on EXPOSE 8760 diff --git a/docker/audio_classification.py b/docker/audio_classification.py new file mode 100644 index 0000000..089c25b --- /dev/null +++ b/docker/audio_classification.py @@ -0,0 +1,50 @@ +from transformers import pipeline +from argparse import ArgumentParser +import torch +import gradio as gr +import numpy as np + +argparse = ArgumentParser() +argparse.add_argument( + "-m", + "--model", + help="HuggingFace Model identifier, such as 'google/flan-t5-base'", + required=True, +) + +args = argparse.parse_args() + + +mod = args.model +mod = mod.replace("\"", "").replace("'", "") + +model_checkpoint = mod + +# Audio class +classifier = pipeline(task="audio-classification", model=mod) + +def classify_text(audio): + global classifier + sr, data = audio + short_tensor = data.astype(np.float32) + res = classifier(short_tensor) + return res[0]["label"] + +input_audio = gr.Audio( + sources=["upload","microphone"], + waveform_options=gr.WaveformOptions( + waveform_color="#01C6FF", + waveform_progress_color="#0066B4", + skip_length=2, + show_controls=False, + ), +) +demo = gr.Interface( + title="everything-ai-audioclass", + fn=classify_text, + inputs=input_audio, + outputs="text" +) + +if __name__ == "__main__": + demo.launch(server_name="0.0.0.0", share=False) diff --git a/docker/autotrain_interface.py b/docker/autotrain_interface.py new file mode 100644 index 0000000..91c6349 --- /dev/null +++ b/docker/autotrain_interface.py @@ -0,0 +1,43 @@ +import subprocess as sp +import gradio as gr +import subprocess as sp + + +def build_command(hf_usr, hf_token, configpath): + sp.run(f"export HF_USERNAME=\"{hf_usr}\"", shell=True) + sp.run(f"export HF_TOKEN=\"{hf_token}\"", shell=True) + sp.run(f"autotrain --config {configpath}", shell=True) + return f"export HF_USERNAME={hf_usr}\nexport HF_TOKEN={hf_token}\nautotrain --config {configpath}" + + +demo = gr.Interface( + build_command, + [ + gr.Textbox( + label="HF username", + info="Your HF username", + lines=3, + value=f"your-cute-name", + ), + gr.Textbox( + label="HF write token", + info="An HF token that has write permissions on your repository", + lines=3, + value=f"your-powerful-token", + ), + gr.Textbox( + label="Yaml configuration file", + info="Path to the yaml configuration file containing the information to use autotrain", + lines=3, + value="/path/to/config.yaml", + ) + ], + title="everything-ai-autotrain", + outputs="textbox", + theme=gr.themes.Base() +) + +if __name__ == "__main__": + demo.launch(server_name="0.0.0.0", server_port=7860, share=False) + + \ No newline at end of file diff --git a/docker/protein_folding_with_esm.py b/docker/protein_folding_with_esm.py new file mode 100644 index 0000000..344bfe8 --- /dev/null +++ b/docker/protein_folding_with_esm.py @@ -0,0 +1,60 @@ +from transformers import AutoTokenizer, EsmForProteinFolding +from transformers.models.esm.openfold_utils.protein import to_pdb, Protein as OFProtein +from transformers.models.esm.openfold_utils.feats import atom14_to_atom37 +from proteins_viz import * +import gradio as gr + +def convert_outputs_to_pdb(outputs): + final_atom_positions = atom14_to_atom37(outputs["positions"][-1], outputs) + outputs = {k: v.to("cpu").numpy() for k, v in outputs.items()} + final_atom_positions = final_atom_positions.cpu().numpy() + final_atom_mask = outputs["atom37_atom_exists"] + pdbs = [] + for i in range(outputs["aatype"].shape[0]): + aa = outputs["aatype"][i] + pred_pos = final_atom_positions[i] + mask = final_atom_mask[i] + resid = outputs["residue_index"][i] + 1 + pred = OFProtein( + aatype=aa, + atom_positions=pred_pos, + atom_mask=mask, + residue_index=resid, + b_factors=outputs["plddt"][i], + chain_index=outputs["chain_index"][i] if "chain_index" in outputs else None, + ) + pdbs.append(to_pdb(pred)) + return pdbs + +tokenizer = AutoTokenizer.from_pretrained("facebook/esmfold_v1") +model = EsmForProteinFolding.from_pretrained("facebook/esmfold_v1", low_cpu_mem_usage=True) + +model = model.cuda() + +model.esm = model.esm.half() + +import torch + +torch.backends.cuda.matmul.allow_tf32 = True + +model.trunk.set_chunk_size(64) + +def fold_protein(test_protein): + tokenized_input = tokenizer([test_protein], return_tensors="pt", add_special_tokens=False)['input_ids'] + tokenized_input = tokenized_input.cuda() + with torch.no_grad(): + output = model(tokenized_input) + pdb = convert_outputs_to_pdb(output) + with open("output_structure.pdb", "w") as f: + f.write("".join(pdb)) + image = take_care("output_structure.pdb") + return image + +iface = gr.Interface( + title="everything-ai-proteinfold", + fn=fold_protein, + inputs="text", + outputs="image", +) + +iface.launch(server_name="0.0.0.0", share=False) diff --git a/docker/proteins_viz.py b/docker/proteins_viz.py new file mode 100644 index 0000000..fa132f4 --- /dev/null +++ b/docker/proteins_viz.py @@ -0,0 +1,135 @@ +import pandas as pd +from biopandas.pdb import PandasPdb +from prody import parsePDBHeader + + + + +def read_pdb_to_dataframe( + pdb_path, + model_index: int = 1, + parse_header: bool = True, + ) -> pd.DataFrame: + """ + Read a PDB file, and return a Pandas DataFrame containing the atomic coordinates and metadata. + + Args: + pdb_path (str, optional): Path to a local PDB file to read. Defaults to None. + model_index (int, optional): Index of the model to extract from the PDB file, in case + it contains multiple models. Defaults to 1. + parse_header (bool, optional): Whether to parse the PDB header and extract metadata. + Defaults to True. + + Returns: + pd.DataFrame: A DataFrame containing the atomic coordinates and metadata, with one row + per atom + """ + atomic_df = PandasPdb().read_pdb(pdb_path) + if parse_header: + header = parsePDBHeader(pdb_path) + else: + header = None + atomic_df = atomic_df.get_model(model_index) + if len(atomic_df.df["ATOM"]) == 0: + raise ValueError(f"No model found for index: {model_index}") + + return pd.concat([atomic_df.df["ATOM"], atomic_df.df["HETATM"]]), header + +from graphein.protein.graphs import label_node_id + +def process_dataframe(df: pd.DataFrame, granularity='CA') -> pd.DataFrame: + """ + Process a DataFrame of protein structure data to reduce ambiguity and simplify analysis. + + This function performs the following steps: + 1. Handles alternate locations for an atom, defaulting to keep the first one if multiple exist. + 2. Assigns a unique node_id to each residue in the DataFrame, using a helper function label_node_id. + 3. Filters the DataFrame based on specified granularity (defaults to 'CA' for alpha carbon). + + Parameters + ---------- + df : pd.DataFrame + The DataFrame containing protein structure data to process. It is expected to contain columns 'alt_loc' and 'atom_name'. + + granularity : str, optional + The level of detail or perspective at which the DataFrame should be analyzed. Defaults to 'CA' (alpha carbon). + """ + # handle the case of alternative locations, + # if so default to the 1st one = A + if 'alt_loc' in df.columns: + df['alt_loc'] = df['alt_loc'].replace('', 'A') + df = df.loc[(df['alt_loc']=='A')] + df = label_node_id(df, granularity) + df = df.loc[(df['atom_name']==granularity)] + return df + + +from graphein.protein.graphs import initialise_graph_with_metadata +from graphein.protein.graphs import add_nodes_to_graph +from graphein.protein.visualisation import plotly_protein_structure_graph +from PIL import Image +import networkx as nx + +def take_care(pdb_path): + + + df, header = read_pdb_to_dataframe(pdb_path) + process_df = process_dataframe(df) + + g = initialise_graph_with_metadata(protein_df=process_df, # from above cell + raw_pdb_df=df, # Store this for traceability + pdb_code = '3nir', #and again + granularity = 'CA' # Store this so we know what kind of graph we have + ) + g = add_nodes_to_graph(g) + + + def add_backbone_edges(G: nx.Graph) -> nx.Graph: + # Iterate over every chain + for chain_id in G.graph["chain_ids"]: + # Find chain residues + chain_residues = [ + (n, v) for n, v in G.nodes(data=True) if v["chain_id"] == chain_id + ] + # Iterate over every residue in chain + for i, residue in enumerate(chain_residues): + try: + # Checks not at chain terminus + if i == len(chain_residues) - 1: + continue + # Asserts residues are on the same chain + cond_1 = ( residue[1]["chain_id"] == chain_residues[i + 1][1]["chain_id"]) + # Asserts residue numbers are adjacent + cond_2 = (abs(residue[1]["residue_number"] - chain_residues[i + 1][1]["residue_number"])== 1) + + # If this checks out, we add a peptide bond + if (cond_1) and (cond_2): + # Adds "peptide bond" between current residue and the next + if G.has_edge(i, i + 1): + G.edges[i, i + 1]["kind"].add('backbone_bond') + else: + G.add_edge(residue[0],chain_residues[i + 1][0],kind={'backbone_bond'},) + except IndexError as e: + print(e) + return G + + g = add_backbone_edges(g) + + + + p = plotly_protein_structure_graph( + g, + colour_edges_by="kind", + colour_nodes_by="seq_position", + label_node_ids=False, + plot_title="Backbone Protein Graph", + node_size_multiplier=1, + ) + image_file = "protein_graph.png" + p.write_image(image_file, format='png') + + + # Load the PNG image into a PIL image + image = Image.open(image_file) + + return image \ No newline at end of file diff --git a/docker/select_and_run.py b/docker/select_and_run.py index ee5ffe4..8f7fed7 100644 --- a/docker/select_and_run.py +++ b/docker/select_and_run.py @@ -1,19 +1,25 @@ import subprocess as sp import gradio as gr -TASK_TO_SCRIPT = {"retrieval-text-generation": "retrieval_text_generation.py", "agnostic-text-generation": "agnostic_text_generation.py", "text-summarization": "text_summarization.py", "image-generation": "image_generation.py", "image-generation-pollinations": "image_generation_pollinations.py", "image-classification": "image_classification.py", "image-to-text": "image_to_text.py", "retrieval-image-search": "retrieval_image_search.py"} +TASK_TO_SCRIPT = {"retrieval-text-generation": "retrieval_text_generation.py", "agnostic-text-generation": "agnostic_text_generation.py", "text-summarization": "text_summarization.py", "image-generation": "image_generation.py", "image-generation-pollinations": "image_generation_pollinations.py", "image-classification": "image_classification.py", "image-to-text": "image_to_text.py", "retrieval-image-search": "retrieval_image_search.py", "protein-folding": "protein_folding_with_esm.py", "video-generation": "video_generation.py", "speech-recognition": "speech_recognition.py", "spaces-api-supabase": "spaces_api_supabase.py", "audio-classification": "audio_classification.py", "autotrain": "autotrain_interface.py"} -def build_command(tsk, mod="None", pdff="None", dirs="None", lan="None", imdim="512"): - if tsk != "retrieval-text-generation" and tsk != "image-generation-pollinations" and tsk != "retrieval-image-search": +def build_command(tsk, mod="None", pdff="None", dirs="None", lan="None", imdim="512", gradioclient="None", supabaseurl="None", collectname="None", supenc="all-MiniLM-L6-v2", supdim="384"): + if tsk != "retrieval-text-generation" and tsk != "image-generation-pollinations" and tsk != "retrieval-image-search" and tsk != "autotrain" and tsk != "protein-folding" and tsk != "spaces-api-supabase": sp.run(f"python3 {TASK_TO_SCRIPT[tsk]} -m {mod}", shell=True) return f"python3 {TASK_TO_SCRIPT[tsk]} -m {mod}" elif tsk == "retrieval-text-generation": sp.run(f"python3 {TASK_TO_SCRIPT[tsk]} -m {mod} -pf '{pdff}' -d '{dirs}' -l '{lan}'", shell=True) return f"python3 {TASK_TO_SCRIPT[tsk]} -m {mod} -pf '{pdff}' -d '{dirs}' -l '{lan}'" - elif tsk == "image-generation-pollinations": + elif tsk == "image-generation-pollinations" or tsk == "autotrain" or tsk == "protein-folding": sp.run(f"python3 {TASK_TO_SCRIPT[tsk]}", shell=True) return f"python3 {TASK_TO_SCRIPT[tsk]}" + elif tsk == "spaces-api-supabase": + if lan == "None": + sp.run(f"python3 {TASK_TO_SCRIPT[tsk]} -gc {gradioclient} -sdu {supabaseurl} -cn {collectname} -en {supenc} -s {supdim}", shell=True) + else: + sp.run(f"python3 {TASK_TO_SCRIPT[tsk]} -gc {gradioclient} -sdu {supabaseurl} -cn {collectname} -en {supenc} -s {supdim} -l {lan}", shell=True) + return f"python3 {TASK_TO_SCRIPT[tsk]} -gc {gradioclient} -sdu {supabaseurl} -cn {collectname} -en {supenc} -s {supdim} -l {lan}" else: sp.run(f"python3 {TASK_TO_SCRIPT[tsk]} -d {dirs} -id {imdim} -m {mod}", shell=True) return f"python3 {TASK_TO_SCRIPT[tsk]} -d {dirs} -id {imdim} -m {mod}" @@ -25,7 +31,7 @@ def build_command(tsk, mod="None", pdff="None", dirs="None", lan="None", imdim=" label="Task", info="Task you want your assistant to help you with", lines=3, - value=f"Choose one of the following: {','.join(list(TASK_TO_SCRIPT.keys()))}; if you choose 'image-generation-pollinations', you do not need to specify anything else", + value=f"Choose one of the following: {','.join(list(TASK_TO_SCRIPT.keys()))}; if you choose 'image-generation-pollinations' or 'autotrain' or 'protein-folding', you do not need to specify anything else. If you choose 'spaces-api-supabase' you need to specify the Spaces API client, the database URL, the collection name, the Sentence-Transformers encoder used to upload the vectors to the Supabase database and the vectors size (optionally also the language)", ), gr.Textbox( label="Model", @@ -48,15 +54,45 @@ def build_command(tsk, mod="None", pdff="None", dirs="None", lan="None", imdim=" gr.Textbox( label="Language", info="Language of the written content contained in the pdfs", - lines=3, + lines=1, value="None", ), gr.Textbox( label="Image dimension", info="Dimension of the image (this is generally model and/or task-dependent!)", - lines=3, + lines=1, value=f"e.g.: 512, 384, 758...", ), + gr.Textbox( + label="Spaces API client", + info="Client for Spaces API", + lines=3, + value=f"e.g.: eswardivi/Phi-3-mini-4k-instruct", + ), + gr.Textbox( + label="Supabase Database URL", + info="URL of the Supabase database (to use with Spaces API)", + lines=3, + value=f"e.g.: postgresql://postgres.reneogdbgdsbgdbgdsgbdlf:yourcomplexpasswordhere@aws-0-eu-central-1.pooler.supabase.com:5432/postgres", + ), + gr.Textbox( + label="Supabase collection name", + info="Name of the Supabase collectio (to use with Spaces API)", + lines=2, + value=f"e.g.: documents", + ), + gr.Textbox( + label="Supabase Vector Encoder", + info="Name of the sentence-transformers encoder you used to upload vectors to your supabase database", + lines=2, + value=f"e.g.: all-MiniLM-L6-v2", + ), + gr.Textbox( + label="Supabase Vector Size", + info="Size of vectors in you supabase database", + lines=1, + value=f"e.g.: 384", + ), ], outputs="textbox", theme=gr.themes.Base() diff --git a/docker/spaces_api_supabase.py b/docker/spaces_api_supabase.py new file mode 100644 index 0000000..1fffbb3 --- /dev/null +++ b/docker/spaces_api_supabase.py @@ -0,0 +1,145 @@ +import gradio as gr +from utils import Translation, NeuralSearcheR +from gradio_client import Client +import os +import vecs +from sentence_transformers import SentenceTransformer +from argparse import ArgumentParser + +argparse = ArgumentParser() + +argparse.add_argument( + "-gc", + "--gradio_client", + help="Spaces API to connect with", + required=True, +) + +argparse.add_argument( + "-sdu", + "--supabase_database_url", + help="URL for Supabase database", + required=True +) + +argparse.add_argument( + "-cn", + "--collection_name", + help="Name of the Supabase collection", + required=True +) + +argparse.add_argument( + "-l", + "--language", + help="Language of the written content contained in the pdfs", + required=False, + default="en" +) + +argparse.add_argument( + "-en", + "--encoder", + help="Encoder used in text vectorization", + required=False, + default="all-MiniLM-L6-v2" +) + +argparse.add_argument( + "-s", + "--size", + help="Size of the vectors", + required=False, + default=384, + type=int +) + +args = argparse.parse_args() + + +gradcli = args.gradio_client +supdb = args.supabase_database_url +collname = args.collection_name +lan = args.language +encd = args.encoder +sz = args.size + + +collection_name = collname +encoder = SentenceTransformer(encd) +client = supdb +api_client = Client(gradcli) +lan = "en" +vx = vecs.create_client(client) +docs = vx.get_or_create_collection(name=collection_name, dimension=sz) + +def reply(message, history): + global docs + global encoder + global api_client + global lan + txt = Translation(message, "en") + print(txt.original, lan) + if txt.original == "en" and lan == "en": + txt2txt = NeuralSearcheR(docs, encoder) + results = txt2txt.search(message) + response = api_client.predict( + f"Context: {results[0][2]['Content']}; Prompt: {message}", # str in 'Message' Textbox component + 0.4, # float (numeric value between 0 and 1) in 'Temperature' Slider component + True, # bool in 'Sampling' Checkbox component + 512, # float (numeric value between 128 and 4096) in 'Max new tokens' Slider component + api_name="/chat" + ) + return response + elif txt.original == "en" and lan != "en": + txt2txt = NeuralSearcheR(docs, encoder) + transl = Translation(message, lan) + message = transl.translatef() + results = txt2txt.search(message) + t = Translation(results[0][2]['Content'], txt.original) + res = t.translatef() + response = api_client.predict( + f"Context: {res}; Prompt: {message}", # str in 'Message' Textbox component + 0.4, # float (numeric value between 0 and 1) in 'Temperature' Slider component + True, # bool in 'Sampling' Checkbox component + 512, # float (numeric value between 128 and 4096) in 'Max new tokens' Slider component + api_name="/chat" + ) + response = Translation(response, txt.original) + return response.translatef() + elif txt.original != "en" and lan == "en": + txt2txt = NeuralSearcheR(docs, encoder) + results = txt2txt.search(message) + transl = Translation(results[0][2]['Content'], "en") + translation = transl.translatef() + response = api_client.predict( + f"Context: {translation}; Prompt: {message}", # str in 'Message' Textbox component + 0.4, # float (numeric value between 0 and 1) in 'Temperature' Slider component + True, # bool in 'Sampling' Checkbox component + 512, # float (numeric value between 128 and 4096) in 'Max new tokens' Slider component + api_name="/chat" + ) + t = Translation(response, txt.original) + res = t.translatef() + return res + else: + txt2txt = NeuralSearcheR(docs, encoder) + transl = Translation(message, lan.replace("\\","").replace("'","")) + message = transl.translatef() + results = txt2txt.search(message) + t = Translation(results[0][2]['Content'], txt.original) + res = t.translatef() + response = api_client.predict( + f"Context: {res}; Prompt: {message}", # str in 'Message' Textbox component + 0.4, # float (numeric value between 0 and 1) in 'Temperature' Slider component + True, # bool in 'Sampling' Checkbox component + 512, # float (numeric value between 128 and 4096) in 'Max new tokens' Slider component + api_name="/chat" + ) + tr = Translation(response, txt.original) + ress = tr.translatef() + return ress + + +demo = gr.ChatInterface(fn=reply, title="everything-ai-supabase2spacesapi") +demo.launch(server_name="0.0.0.0", share=False) \ No newline at end of file diff --git a/docker/speech_recognition.py b/docker/speech_recognition.py new file mode 100644 index 0000000..cafa45a --- /dev/null +++ b/docker/speech_recognition.py @@ -0,0 +1,51 @@ +from transformers import pipeline +from argparse import ArgumentParser +import torch +import gradio as gr +import numpy as np + + +argparse = ArgumentParser() +argparse.add_argument( + "-m", + "--model", + help="HuggingFace Model identifier, such as 'google/flan-t5-base'", + required=True, +) + +args = argparse.parse_args() + + +mod = args.model +mod = mod.replace("\"", "").replace("'", "") + +model_checkpoint = mod + +# Audio class +classifier = pipeline(task="automatic-speech-recognition", model=mod) + +def classify_text(audio): + global classifier + sr, data = audio + short_tensor = data.astype(np.float32) + res = classifier(short_tensor) + return res["text"] + +input_audio = gr.Audio( + sources=["upload","microphone"], + waveform_options=gr.WaveformOptions( + waveform_color="#01C6FF", + waveform_progress_color="#0066B4", + skip_length=2, + show_controls=False, + ), +) +demo = gr.Interface( + title="everything-ai-speechrec", + fn=classify_text, + inputs=input_audio, + outputs="text" +) + +if __name__ == "__main__": + demo.launch(server_name="0.0.0.0", share=False) diff --git a/docker/utils.py b/docker/utils.py index 60da330..a4ddf60 100644 --- a/docker/utils.py +++ b/docker/utils.py @@ -163,4 +163,18 @@ def searchDB(self, image): limit=4 ) return results - \ No newline at end of file + +class NeuralSearcheR: + def __init__(self, collection, encoder): + self.collection = collection + self.encoder = encoder + def search(self, text): + results = self.collection.query( + data=self.encoder.encode(text).tolist(), # required + limit=1, # number of records to return + filters={}, # metadata filters + measure="cosine_distance", # distance measure to use + include_value=True, # should distance measure values be returned? + include_metadata=True, # should record metadata be returned? + ) + return results \ No newline at end of file diff --git a/docker/video_generation.py b/docker/video_generation.py new file mode 100644 index 0000000..9354094 --- /dev/null +++ b/docker/video_generation.py @@ -0,0 +1,71 @@ +import gradio as gr +import moviepy.editor as mp +from diffusers import DiffusionPipeline +from argparse import ArgumentParser + + +argparse = ArgumentParser() +argparse.add_argument( + "-m", + "--model", + help="HuggingFace Model identifier, such as 'google/flan-t5-base'", + required=True, +) + +args = argparse.parse_args() + + +mod = args.model +mod = mod.replace("\"", "").replace("'", "") + +model_checkpoint = mod + +# Load diffusion pipelines +image_pipeline = DiffusionPipeline.from_pretrained(model_checkpoint) +video_pipeline = DiffusionPipeline.from_pretrained(model_checkpoint) + +def generate_images(prompt, num_images): + """Generates images using the image pipeline.""" + images = [] + for _ in range(num_images): + generated_image = image_pipeline(prompt=prompt).images[0] + images.append(generated_image) + return images + + +def generate_videos(images): + """Generates videos from a list of images using the video pipeline.""" + videos = [] + for image in images: + # Wrap the image in a list as expected by the pipeline + generated_video = video_pipeline(images=[image]).images[0] + videos.append(generated_video) + return videos + +def combine_videos(video_clips): + final_clip = mp.concatenate_videoclips(video_clips) + return final_clip + +def generate(prompt): + images = generate_images(prompt, 2) + video_clips = generate_videos(images) + combined_video = combine_videos(video_clips) + return combined_video + +# Gradio interface with improved formatting and video output +interface = gr.Interface( + fn=generate, + inputs="text", + outputs="video", + title="everything-ai-text2vid", + description="Enter a prompt to generate a video using diffusion models.", + css=""" + .output-video { + width: 100%; /* Adjust width as needed */ + height: 400px; /* Adjust height as desired */ + } + """, +) + +# Launch the interface +interface.launch(server_name="0.0.0.0", share=False) diff --git a/imgs/everything-ai.drawio.png b/imgs/everything-ai.drawio.png index a1a7ac0838766494f9dcc8b8d572db4149a50fe9..fc859f28259e8c2f3a2d3acef32a4615b93ee0ae 100644 GIT binary patch literal 472235 zcmeFaNs{B()+Y93lF7=-tTs@lX_}P=;f5aIkN^n~1kn%;yU8;U4be9QK!V&sZAL55 z3N+9XG}l-&4P-jko$z2p@MU=BpBe6vFC#tONf5jM?mhQ3-|7Clp-KPcKmXVN{EvV9 zs@ANcb>G^WJ=&VqmZLJBXhej%`^Y}#MQ$u9)qR@IXqyN9EjH%~>-!XfxPFa0=-1OMe50wbtT z82yPLZJhXp91|S;!muZTBzG_8yN95-4XBpaILq8$2$6omV5tALjtjp$H}F!k;TMj< zVUmYe@Bu@;yzCk3e}yCCs>*_OuvcQZ50NI>Cz64~|4o_M>f#qdfY(`^2JlA;ytG7L z5u#(6!x0fD;CkpkIB2<{dyMafK+hDk?&0NmLNA_EN~Xp6SRwqtQ$CHUm*;3aHw#`) 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