1: Initial start for huggingface space for FLUX LoRA hackathon

Dockerfile

@@ -0,0 +1,24 @@
+# Use Python 3.9 as the base image
+FROM python:3.9
+
+# Create a new user and set environment variables
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+# Set the working directory
+WORKDIR /app
+
+# Copy the requirements and install dependencies
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Copy the rest of the application code
+COPY --chown=user . /app
+
+# Expose both FastAPI and Gradio ports
+EXPOSE 7860
+EXPOSE 7861
+
+# Run the Gradio app
+CMD ["python", "app.py"]

README.md

@@ -9,3 +9,23 @@ license: mit
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+## Creating and starting a virtual environment for Python 3
+
+Create a directory for a virtual environment:
+
+```
+/AI-InstagramPhotos$ python3 -m venv ./venv/
+```
+
+Activate it:
+```
+/AI-InstagramPhotos$ source ./venv/bin/activate
+```
+You should see the prompt have a prefix `(venv)`.
+
+Deactivate it:
+```
+deactivate
+```

app.py

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+from fastapi import FastAPI
+import fal_client
+import gradio as gr
+import threading
+import os
+from pathlib import Path
+from public_instagram_loader import public_instagram_loader, InstagramData
+
+app = FastAPI()
+
+# Set up the FAL API Key
+FAL_KEY = os.getenv("FAL_KEY")
+#fal_client.set_api_key(FAL_KEY)
+
+# FastAPI route
+@app.get("/")
+def greet_json():
+    return {"AI-InstagramPhotos": "Generate photos from your favorite Instagram account using FAL API."}
+
+# Function to submit Instagram data to FAL API
+def submit_to_fal(instagram_data: InstagramData):
+    handler = fal_client.submit(
+        "fal-ai/flux-lora-fast-training",
+        arguments={
+            "images_data_url": instagram_data.image_urls,
+            "trigger_word": instagram_data.profile_info['username'],
+            "is_style": True
+        },
+    )
+    result = handler.get()
+    return result
+
+# Gradio interface for interacting with the Instagram loader and FAL
+def gradio_interface():
+    def generate_lora_from_instagram(username: str):
+        # Load Instagram data
+        instagram_data = public_instagram_loader(username)
+        if instagram_data is None:
+            return "Profile not found or an error occurred."
+        # Pass Instagram data to FAL API for LoRA training
+        fal_result = submit_to_fal(instagram_data)
+        # Return the URL of the trained LoRA weights
+        return f"Trained LoRA weights: {fal_result['diffusers_lora_file']['url']}"
+
+    iface = gr.Interface(
+        fn=generate_lora_from_instagram, 
+        inputs=gr.Textbox(label="Enter Instagram username"),
+        outputs=gr.Textbox(label="LoRA URL"), 
+        title="Instagram LoRA Generator using FAL"
+    )
+    
+    # Launch Gradio on a different port (7861)
+    iface.launch(server_name="0.0.0.0", server_port=7861)
+
+# Run Gradio in a separate thread
+thread = threading.Thread(target=gradio_interface)
+thread.start()

public_instagram_loader.py

@@ -0,0 +1,83 @@
+import tempfile
+import os
+from pathlib import Path
+from instaloader import Instaloader, Profile
+from dataclasses import dataclass, field
+from typing import List, Dict, Tuple
+import json
+import zipfile
+import io
+import fal_client
+
+@dataclass
+class InstagramData:
+    profile_info: Dict
+    posts: List[Dict] = field(default_factory=list)
+    image_urls: List[str] = field(default_factory=list)
+
+def public_instagram_loader(username: str) -> InstagramData:
+    with tempfile.TemporaryDirectory() as temp_dir:
+        profile_dir = Path(temp_dir) / username
+        profile_dir.mkdir(parents=True, exist_ok=True)
+
+        L = Instaloader(
+            download_pictures=True,
+            download_videos=False,
+            download_video_thumbnails=False,
+            download_geotags=False,
+            download_comments=False,
+            save_metadata=False,
+            compress_json=False,
+            dirname_pattern=str(profile_dir)
+        )
+
+        try:
+            profile = Profile.from_username(L.context, username)
+        except Exception:
+            return None
+
+        profile_info = {
+            'username': profile.username,
+            'full_name': profile.full_name,
+            'biography': profile.biography,
+            'followers': profile.followers,
+            'followees': profile.followees,
+            'mediacount': profile.mediacount,
+        }
+
+        instagram_data = InstagramData(profile_info=profile_info)
+
+        print(f"Downloading and uploading posts from {username}")
+        for post in profile.get_posts():
+            if post.typename == 'GraphImage':
+                post_data = {
+                    'shortcode': post.shortcode,
+                    'caption': post.caption,
+                    'date': post.date_local,
+                    'likes': post.likes,
+                    'filename': f"{post.date_utc:%Y-%m-%d_%H-%M-%S}_UTC.jpg",
+                }
+                
+                # Download the image
+                image_path = profile_dir / post_data['filename']
+                L.download_pic(filename=str(image_path), url=post.url, mtime=post.date_utc)
+                
+                # Upload the image to FAL
+                with open(image_path, 'rb') as img_file:
+                    url = fal_client.upload(img_file.read(), "image/jpeg")
+                
+                instagram_data.posts.append(post_data)
+                instagram_data.image_urls.append(url)
+
+        print(f"Data and images uploaded for {username}")
+        return instagram_data
+
+if __name__ == "__main__":
+    username = input("Enter Instagram username: ")
+    data = public_instagram_loader(username)
+    if data:
+        print(f"Collected data for {data.profile_info['username']}:")
+        print(f"Total posts: {len(data.posts)}")
+        print(f"Zip file size: {len(data.zip_file)} bytes")
+    else:
+        print("Profile not found or an error occurred.")

requirements.txt

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+fastapi
+uvicorn[standard]
+gradio
+fal-client
+pyyaml
+instaloader