Update app.py

app.py

@@ -1,126 +1,112 @@
-from sentence_transformers import SentenceTransformer, util
-from huggingface_hub import hf_hub_download
-import os
-import pickle
-import pandas as pd
 import gradio as gr
-
-pd.options.mode.chained_assignment = None  # Turn off SettingWithCopyWarning
-
-auth_token = os.environ.get("TOKEN_FROM_SECRET") or True
-pickled = pickle.load(open(hf_hub_download("NimaBoscarino/playlist-generator", repo_type="dataset", filename="clean-large_embeddings_msmarco-MiniLM-L-6-v3.pkl"), "rb"))
-songs = pd.read_csv(hf_hub_download("NimaBoscarino/playlist-generator", repo_type="dataset", filename="songs_new.csv"))
-verses = pd.read_csv(hf_hub_download("NimaBoscarino/playlist-generator-private", repo_type="dataset", filename="verses.csv", use_auth_token=auth_token))
-lyrics = pd.read_csv(hf_hub_download("NimaBoscarino/playlist-generator-private", repo_type="dataset", filename="lyrics_new.csv", use_auth_token=auth_token))
-
-embedder = SentenceTransformer('msmarco-MiniLM-L-6-v3')
-
-song_ids = pickled["song_ids"]
-corpus_embeddings = pickled["embeddings"]
-
-
-def generate_playlist(prompt):
-    prompt_embedding = embedder.encode(prompt, convert_to_tensor=True)
-    hits = util.semantic_search(prompt_embedding, corpus_embeddings, top_k=20)
-    hits = pd.DataFrame(hits[0], columns=['corpus_id', 'score'])
-
-    verse_match = verses.iloc[hits['corpus_id']]
-    verse_match = verse_match.drop_duplicates(subset=["song_id"])
-    song_match = songs[songs["song_id"].isin(verse_match["song_id"].values)]
-    song_match.song_id = pd.Categorical(song_match.song_id, categories=verse_match["song_id"].values)
-    song_match = song_match.sort_values("song_id")
-    song_match = song_match[0:9]  # Only grab the top 9
-
-    song_names = list(song_match["full_title"])
-    song_art = list(song_match["art"].fillna("https://i.imgur.com/bgCDfT1.jpg"))
-    images = [gr.Image.update(value=art, visible=True) for art in song_art]
-
-    return (
-        gr.Radio.update(label="Songs", interactive=True, choices=song_names),
-        *images
-    )
-
-
-def set_lyrics(full_title):
-    lyrics_text = lyrics[lyrics["song_id"].isin(songs[songs["full_title"] == full_title]["song_id"])]["text"].iloc[0]
-    return gr.Textbox.update(value=lyrics_text)
-
-
-def set_example_prompt(example):
-    return gr.TextArea.update(value=example[0])
+import requests
+
+# GPT-J-6B API
+API_URL = "https://api-inference.huggingface.co/models/EleutherAI/gpt-j-6B"
+headers = {"Authorization": "Bearer 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"}
+prompt = """Oh, my life 
+is changing every day
+Every possible way
+And oh, my dreams, 
+it's never quite as it seems
+Never quite as it seems"""
+
+#examples = [["mind"], ["memory"], ["sleep"],["wellness"],["nutrition"]]
+
+
+def poem2_generate(word):
+  p = word.lower() + "\n" + "poem using word: "
+  gr.Markdown("Prompt is :{p}")
+  json_ = {"inputs": p,
+            "parameters":
+            {
+            "top_p": 0.9,
+          "temperature": 1.1,
+          "max_new_tokens": 50,
+          "return_full_text": False
+          }}
+  response = requests.post(API_URL, headers=headers, json=json_)
+  output = response.json()
+  gr.Markdown("error? Reason is : {output}")
+  output_tmp = output[0]['generated_text']
+  gr.Markdown("GPTJ response without splits is: {output_tmp}")
+  poem = output[0]['generated_text'].split("\n\n")[0] # +"."
+  if "\n\n" not in output_tmp:
+    if output_tmp.find('.') != -1:
+      idx = output_tmp.find('.')
+      poem = output_tmp[:idx+1]
+    else:
+      idx = output_tmp.rfind('\n')
+      poem = output_tmp[:idx]
+  else:
+    poem = output_tmp.split("\n\n")[0] # +"."
+  poem = poem.replace('?','')
+  gr.Markdown("Returned is: {poem}")
+  return poem
+  
+
+def poem_generate(word):
+
+  p = prompt + word.lower() + "\n" + "poem using word: "
+  gr.Markdown("Generate - Prompt is :{p}")
+  json_ = {"inputs": p,
+            "parameters":
+            {
+            "top_p": 0.9,
+          "temperature": 1.1,
+          "max_new_tokens": 50,
+          "return_full_text": False
+          }}
+  response = requests.post(API_URL, headers=headers, json=json_)
+  output = response.json()
+  gr.Markdown("error? Reason is : {output}")
+  output_tmp = output[0]['generated_text']
+  gr.Markdown("Response without splits is: {output_tmp}")
+  poem = output[0]['generated_text'].split("\n\n")[0] # +"."
+  if "\n\n" not in output_tmp:
+    if output_tmp.find('.') != -1:
+      idx = output_tmp.find('.')
+      poem = output_tmp[:idx+1]
+    else:
+      idx = output_tmp.rfind('\n')
+      poem = output_tmp[:idx]
+  else:
+    poem = output_tmp.split("\n\n")[0] # +"."
+  poem = poem.replace('?','')
+  gr.Markdown("Returned is: {poem}")
+  return poem
+
+def poem_to_image(poem):
+  gr.Markdown("toimage")
+  poem = " ".join(poem.split('\n'))
+  poem = poem + " oil on canvas."
+  steps, width, height, images, diversity = '50','256','256','1',15
+  img = gr.Interface().load("spaces/multimodalart/latentdiffusion")(poem, steps, width, height, images, diversity)[0]
+  return img
+
+def set_example(example: list) -> dict:
+    return gr.Textbox.update(value=example[0]) 
 
 
 demo = gr.Blocks()
 
 with demo:
-    gr.Markdown(
-        """
-        # Playlist Generator 📻 🎵
-        """)
-
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown(
-                """
-                Enter a prompt and generate a playlist based on ✨semantic similarity✨
-                This was built using Sentence Transformers and Gradio – blog post coming soon!
-                """)
-
-            song_prompt = gr.TextArea(
-                value="Running wild and free",
-                placeholder="Enter a song prompt, or choose an example"
-            )
-            example_prompts = gr.Dataset(
-                components=[song_prompt],
-                samples=[
-                    ["I feel nostalgic for the past"],
-                    ["Running wild and free"],
-                    ["I'm deeply in love with someone I just met!"],
-                    ["My friends mean the world to me"],
-                    ["Sometimes I feel like no one understands"],
-                ]
-            )
-
-        with gr.Column():
-            fetch_songs = gr.Button(value="Generate Your Playlist 🧑🏽‍🎤").style(full_width=True)
-
-            with gr.Row():
-                tile1 = gr.Image(value="https://i.imgur.com/bgCDfT1.jpg", show_label=False, visible=True)
-                tile2 = gr.Image(value="https://i.imgur.com/bgCDfT1.jpg", show_label=False, visible=True)
-                tile3 = gr.Image(value="https://i.imgur.com/bgCDfT1.jpg", show_label=False, visible=True)
-            with gr.Row():
-                tile4 = gr.Image(value="https://i.imgur.com/bgCDfT1.jpg", show_label=False, visible=True)
-                tile5 = gr.Image(value="https://i.imgur.com/bgCDfT1.jpg", show_label=False, visible=True)
-                tile6 = gr.Image(value="https://i.imgur.com/bgCDfT1.jpg", show_label=False, visible=True)
-            with gr.Row():
-                tile7 = gr.Image(value="https://i.imgur.com/bgCDfT1.jpg", show_label=False, visible=True)
-                tile8 = gr.Image(value="https://i.imgur.com/bgCDfT1.jpg", show_label=False, visible=True)
-                tile9 = gr.Image(value="https://i.imgur.com/bgCDfT1.jpg", show_label=False, visible=True)
-
-            # Workaround because of the Gallery issues
-            tiles = [tile1, tile2, tile3, tile4, tile5, tile6, tile7, tile8, tile9]
-
-            song_option = gr.Radio(label="Songs", interactive=True, choices=None, type="value")
-
-        with gr.Column():
-            verse = gr.Textbox(label="Verse", placeholder="Select a song to see its lyrics")
-
-    fetch_songs.click(
-        fn=generate_playlist,
-        inputs=[song_prompt],
-        outputs=[song_option, *tiles],
-    )
-
-    example_prompts.click(
-        fn=set_example_prompt,
-        inputs=example_prompts,
-        outputs=example_prompts.components,
-    )
-
-    song_option.change(
-        fn=set_lyrics,
-        inputs=[song_option],
-        outputs=[verse]
-    )
-
-demo.launch()
+  gr.Markdown("<h1><center>Few Shot Learning Text to Word Image Search</center></h1>")
+  gr.Markdown("https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api,  https://github.com/EleutherAI/the-pile")
+  with gr.Row():
+    input_word = gr.Textbox(lines=7, value=prompt)
+    poem_txt = gr.Textbox(lines=7)
+    output_image = gr.Image(type="filepath", shape=(256,256))
+  
+  b1 = gr.Button("Generate Text")
+  b2 = gr.Button("Generate Image")
+  
+  b1.click(poem2_generate, input_word, poem_txt)
+  b2.click(poem_to_image, poem_txt, output_image)
+
+  examples=[["living, loving,"], ["I want to live.  I want to give."],["Ive been to Hollywood.  Ive been to Redwood"]]
+  example_text = gr.Dataset(components=[input_word], samples=examples)
+  example_text.click(fn=set_example,inputs = example_text,outputs= example_text.components)
+
+
+demo.launch(enable_queue=True, debug=True)