added files

app.py

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+import gradio as gr
+from transformers import pipeline
+
+from glob import glob
+from PIL import Image
+import os
+from icrawler.builtin import GoogleImageCrawler
+
+def download_image(query, out_file):
+  google_crawler = GoogleImageCrawler(storage={'root_dir': './'})
+  google_crawler.crawl(keyword=query, max_num=1, overwrite=True)
+  os.rename(glob("./000001.*")[0], out_file)
+
+def generate_story(prompt):
+  story = storygen(f"{prompt}")[0]['generated_text']
+  return story
+
+def start_neural_style_transfer(img1_name, img2_name):
+  img1_filename, img2_filename = "style_transfer_1.jpg", "style_transfer_2.jpg"
+
+  download_image(img1_name, img1_filename)
+  download_image(img2_name, img2_filename)
+
+  styled_image = nst(img1_filename, img2_filename)
+
+  pil_img = Image.open(styled_image)
+
+  return pil_img
+
+def detect_objects(file_name):
+  out_img = detectron(file_name)
+  pil_img = Image.open(out_img) 
+  return pil_img
+
+def main(text_input):
+  text_output, image_output, metadata = None, None, None 
+
+  task_type_q = f"User: {text_input}\nWhat is the task the user is asking to do?\n \
+                - story generation task\n \
+                - image style transfer task\n \
+                - object detection task"
+  task = t0pp(task_type_q)
+  task = task.lower().replace('.', '')
+
+  if task=="story generation task":
+    story_prompt = t0pp(f"User: {text_input}\nWhat story is the user asking for?")
+    text_output = generate_story(story_prompt)
+    metadata = f"Prompt used to generate the story:\n{story_prompt}"
+
+  elif task=="image style transfer task":
+    img1_name = t0pp(f"User: {text_input}\nWhat is the name of the picture to which style is to be tranferred?")
+    img2_name = t0pp(f"User: {text_input}\nWhat is the name of the picture from which style is to be tranferred?")
+    image_output = start_neural_style_transfer(img1_name, img2_name)
+    metadata = f"Image from which style is to be transferred: {img2_name}\nImage to which style is to be transferred: {img1_name}"
+
+  elif task=="object detection task":
+    img_file = "object_detection.jpg"
+    img_name = t0pp(f"User: {text_input}\nWhat image is the user referring to?")
+    download_image(img_name, img_file)
+    image_output = detect_objects(img_file)
+    metadata = f"Image from which objects are to be detected: {img_name}"
+
+  return text_output, image_output
+
+if __name__=="__main__":
+    t0pp = gr.Interface.load("huggingface/bigscience/T0pp")
+    storygen = pipeline("text-generation", model="pranavpsv/genre-story-generator-v2")
+    nst = gr.Interface.load("spaces/luca-martial/neural-style-transfer")
+    detectron = gr.Interface.load("spaces/akhaliq/Detectron2")
+
+    gr.Interface(
+        main,
+        inputs=gr.inputs.Textbox(lines=5, label="Input"),
+        outputs=[gr.outputs.Textbox(label="Output"), gr.outputs.Image(label="Ouptut"),]
+    ).launch(debug=True)

requirements.txt

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+icrawler