Upload folder using huggingface_hub

.gitignore

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+.gradio
+font_cache

README.md

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 ---
 title: Heeha
-emoji: 📊
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.20.1
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 5.20.0
 ---
+# Llama 3.2 3B Chat Interface
+
+This project provides a Gradio web interface for interacting with the Llama 3.2 3B model using Hugging Face Transformers.
+
+## Prerequisites
+
+- Python 3.8 or higher
+- CUDA-capable GPU (recommended for better performance)
+- Hugging Face account with access to Llama 3.2 models
+
+## Setup
+
+1. Clone this repository
+2. Install the required dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+3. Set up your Hugging Face token as an environment variable:
+   ```bash
+   export HF_TOKEN="your_huggingface_token_here"
+   ```
+   You can get your token from: https://huggingface.co/settings/tokens
+
+## Usage
+
+Run the application:
+
+```bash
+python app.py
+```
+
+The Gradio interface will be available at `http://localhost:7860` by default.
+
+## Features
+
+- Interactive chat interface using the Transformers pipeline
+- Adjustable generation parameters (max new tokens and temperature)
+- Example prompts for quick testing
+- Automatic GPU utilization when available
+- Uses bfloat16 precision for better performance
+- Secure token handling through environment variables
+
+## Note
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+You need to have access to the Llama 3.2 models on Hugging Face. You can request access at: https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct

app.py

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+import torch
+import gradio as gr
+from transformers import pipeline
+from typing import List, Dict, Any, Tuple
+import csv
+from io import StringIO
+from PIL import Image, ImageDraw, ImageFont
+import requests
+from io import BytesIO
+import os
+from pathlib import Path
+import logging
+
+# Create a font cache directory
+FONT_CACHE_DIR = Path("./font_cache")
+FONT_CACHE_DIR.mkdir(exist_ok=True)
+
+# Define common font URLs and their corresponding filenames
+FONT_SOURCES = {
+    "Arial": "https://github.com/matomo-org/travis-scripts/raw/master/fonts/Arial.ttf",
+    "Arial Bold": "https://github.com/matomo-org/travis-scripts/raw/master/fonts/Arial_Bold.ttf",
+    "Arial Bold Italic": "https://github.com/matomo-org/travis-scripts/raw/master/fonts/Arial_Bold_Italic.ttf",
+    "Arial Italic": "https://github.com/matomo-org/travis-scripts/raw/master/fonts/Arial_Italic.ttf",
+    "Courier New": "https://github.com/matomo-org/travis-scripts/raw/master/fonts/Courier_New.ttf",
+    "Verdana": "https://github.com/matomo-org/travis-scripts/raw/master/fonts/Verdana.ttf",
+    "Verdana Bold": "https://github.com/matomo-org/travis-scripts/raw/master/fonts/Verdana_Bold.ttf",
+    "Verdana Bold Italic": "https://github.com/matomo-org/travis-scripts/raw/master/fonts/Verdana_Bold_Italic.ttf",
+    "Verdana Italic": "https://github.com/matomo-org/travis-scripts/raw/master/fonts/Verdana_Italic.ttf",
+}
+
+# Font cache dictionary
+font_cache = {}
+
+
+def load_and_cache_fonts():
+    """Load and cache fonts from known sources."""
+    for font_name, url in FONT_SOURCES.items():
+        font_path = FONT_CACHE_DIR / f"{font_name}.ttf"
+
+        # Check if font is already cached
+        if font_path.exists():
+            try:
+                font_cache[font_name] = str(font_path)
+                logging.info(f"Loaded cached font: {font_name}")
+            except Exception as e:
+                logging.error(f"Error loading cached font {font_name}: {e}")
+            continue
+
+        # Download and cache font
+        try:
+            response = requests.get(url)
+            response.raise_for_status()
+
+            with open(font_path, "wb") as f:
+                f.write(response.content)
+
+            font_cache[font_name] = str(font_path)
+            logging.info(f"Downloaded and cached font: {font_name}")
+        except Exception as e:
+            logging.error(f"Error downloading font {font_name}: {e}")
+
+
+# Initialize font cache at startup
+load_and_cache_fonts()
+
+# Initialize the pipeline (doing it here means it will be loaded only once when the script starts)
+pipe = pipeline(
+    "text-generation",
+    model="alpindale/Llama-3.2-3B-Instruct",
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+)
+
+
+def read_feed_data(feed_text: str) -> Dict[str, str]:
+    """Read the first row of feed data and return as dictionary.
+    Automatically detects the delimiter from common options (|, ,, ;, \t)."""
+    feed_io = StringIO(feed_text)
+    # Get first line to detect delimiter
+    first_line = feed_io.readline().strip()
+
+    # Common delimiters to check
+    delimiters = ["|", ",", ";", "\t"]
+    delimiter = "|"  # default
+    max_count = 0
+
+    # Find the delimiter that splits the line into the most fields
+    for d in delimiters:
+        count = len(first_line.split(d))
+        if count > max_count:
+            max_count = count
+            delimiter = d
+
+    # Reset the StringIO buffer to start
+    feed_io.seek(0)
+    reader = csv.reader(feed_io, delimiter=delimiter)
+    headers = next(reader)  # Get header row
+    first_row = next(reader)  # Get first data row
+    return dict(zip(headers, first_row))
+
+
+def overlay_text_on_image(
+    image_url: str,
+    text: str,
+    position: Tuple[int, int],
+    font_size: int,
+    font_color: str,
+    font_family: str,
+) -> Image.Image:
+    """Add text overlay to image with specified properties."""
+    # Download image
+    response = requests.get(image_url)
+    img = Image.open(BytesIO(response.content))
+
+    # Create draw object
+    draw = ImageDraw.Draw(img)
+
+    try:
+        # Try to use cached font first
+        if font_family in font_cache:
+            font = ImageFont.truetype(font_cache[font_family], font_size)
+        else:
+            # Fallback to system font or default
+            font = ImageFont.truetype(font_family, font_size)
+    except OSError:
+        # Ultimate fallback to default font
+        font = ImageFont.load_default()
+        logging.warning(f"Failed to load font {font_family}, using default")
+
+    # Convert RGBA color format to hex if needed
+    if font_color.startswith("rgba"):
+        try:
+            # Parse RGBA values
+            rgba = font_color.strip("rgba()").split(",")
+            r = int(float(rgba[0]))
+            g = int(float(rgba[1]))
+            b = int(float(rgba[2]))
+            a = int(float(rgba[3]) * 255)  # Convert alpha from 0-1 to 0-255
+            font_color = f"#{r:02x}{g:02x}{b:02x}"
+        except (ValueError, IndexError):
+            logging.warning(
+                f"Invalid RGBA color format: {font_color}, falling back to white"
+            )
+            font_color = "#FFFFFF"
+
+    # Add text to image
+    draw.text(position, text, font=font, fill=font_color)
+
+    return img
+
+
+def generate_response(
+    prompt: str,
+    feed_text: str,
+    text_x: int = 10,
+    text_y: int = 10,
+    font_size: int = 24,
+    font_color: str = "#FFFFFF",
+    font_family: str = "Arial",
+    max_new_tokens: int = 256,
+    temperature: float = 0.7,
+) -> tuple[str, Image.Image]:
+    # Read feed data
+    feed_data = read_feed_data(feed_text)
+
+    # Format the prompt using the chat template and feed data
+    formatted_prompt = prompt.format(**feed_data)
+    system_prompt = "You are a helpful assistant that processes Meta Product Feeds."
+
+    print(formatted_prompt)
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": formatted_prompt},
+    ]
+
+    # Generate response
+    outputs = pipe(
+        messages,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+    )
+    response = outputs[0]["generated_text"]
+
+    # Get image with text overlay
+    image_with_text = overlay_text_on_image(
+        image_url=feed_data.get("image_link", ""),
+        text=response[-1]["content"],
+        position=(text_x, text_y),
+        font_size=font_size,
+        font_color=font_color,
+        font_family=font_family,
+    )
+
+    return response[-1]["content"], image_with_text
+
+
+# Create Gradio interface
+demo = gr.Interface(
+    title="Meta Product Feed Chat",
+    description="Chat with Llama 3.2 model using feed data. Use {field_name} in your prompt to include feed data. The feed should be in CSV format with headers in the first row.",
+    fn=generate_response,
+    inputs=[
+        gr.Textbox(
+            label="Enter your prompt (use {field_name} for feed data)",
+            lines=3,
+            value="""
+            Write an English slogan for "{title}", respond with slogan only.
+            """,
+        ),
+        gr.Textbox(
+            label="Feed data (CSV with auto-detected delimiter)",
+            lines=10,
+            value="""id|item_group_id|title|description|availability|condition|price|sale_price|sale_price_effective_date|link|image_link|additional_image_link|brand|google_product_category|product_type|gtin|mpn|gender|age_group|color|material|pattern|size|shipping|custom_label_0|custom_label_1|custom_label_2|custom_label_3|custom_label_4|ios_url|ios_app_store_id|ios_app_name|android_url|android_package|android_app_name|additional image 1|additional image 2
+93310981|100274271|Spangenpumps aus Leder|Klassischer Spangenpumps aus Leder|in stock|new|52,99 EUR|false|2011-03-01T13:00-0800/2030-12-31T15:30-0800|https://www.bonprix.de/produkt/spangenpumps-aus-leder-schwarz-933109/?fb_pid=93310981|https://image01.bonprix.de/assets/1400x1960/1729512044/24082077-slWAlGkv.jpg|https://image01.bonprix.de/assets/1400x1960/1729512076/24081283-ApoUcVxa.jpg,,https://image01.bonprix.de/assets/1400x1960/1729512046/24082348-PLsOBIrl.jpg|bonprix|187|Damen > Schuhe > Pumps|8964004145445|93310981|female|adult|schwarz|Leder|Einfarbig|36,37,38,42,39,40,41|DE:::4.99 EUR|nein|false|Damen Schuhe > Pumps > Pumps > Spangenpumps|raus|raus|bonprix://www.bonprix.de/produkt/spangenpumps-aus-leder-schwarz-933109/?fb_pid=93310981|1090412741|bonprix – Mode und Wohn-Trends online shoppen|bonprix://www.bonprix.de/produkt/spangenpumps-aus-leder-schwarz-933109/?fb_pid=93310981|de.bonprix|bonprix – Mode online shoppen||""",
+        ),
+        gr.Number(label="Text X Position", value=10),
+        gr.Number(label="Text Y Position", value=10),
+        gr.Number(label="Font Size", value=24),
+        gr.ColorPicker(label="Font Color", value="#FFFFFF"),
+        gr.Dropdown(
+            label="Font Family",
+            choices=list(FONT_SOURCES.keys()),
+            value="Arial",
+        ),
+        gr.Slider(minimum=1, maximum=512, value=256, step=1, label="Max New Tokens"),
+        gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
+    ],
+    outputs=[
+        gr.Textbox(label="Response", lines=5),
+        gr.Image(label="Product Image with Text"),
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch(share=True)

requirements.txt

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+torch
+numpy==1.26.0
+gradio
+sentencepiece
+ninja
+transformers
+pillow
+requests