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app.py

@@ -72,8 +72,8 @@ pipe = pipeline(
 )
 
 
-def read_feed_data(feed_text: str) -> Dict[str, str]:
-    """Read the first row of feed data and return as dictionary.
+def read_feed_data(feed_text: str) -> List[Dict[str, str]]:
+    """Read all rows of feed data and return as list of dictionaries.
     Automatically detects the delimiter from common options (|, ,, ;, \t)."""
     feed_io = StringIO(feed_text)
     # Get first line to detect delimiter
@@ -95,8 +95,7 @@ def read_feed_data(feed_text: str) -> Dict[str, str]:
     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))
+    return [dict(zip(headers, row)) for row in reader]
 
 
 def overlay_text_on_image(
@@ -159,40 +158,47 @@ def generate_response(
     font_family: str = "Arial",
     max_new_tokens: int = 256,
     temperature: float = 0.7,
-) -> tuple[str, Image.Image]:
+) -> List[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
+    feed_data_list = read_feed_data(feed_text)
+    images = []
+
+    for feed_data in feed_data_list:
+        # 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"]
+        # Extract the generated text from the pipeline output
+        # The pipeline returns the text directly, not in a dictionary
+        generated_text = str(response[-1]["content"]) if response else ""
+
+        # Get image with text overlay
+        image_with_text = overlay_text_on_image(
+            image_url=feed_data.get("image_link", ""),
+            text=generated_text,
+            position=(text_x, text_y),
+            font_size=font_size,
+            font_color=font_color,
+            font_family=font_family,
+        )
+        images.append(image_with_text)
+
+    return images
 
 
 # Create Gradio interface
@@ -201,18 +207,9 @@ demo = gr.Interface(
     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),
         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||""",
+            label="Feed data (CSV with auto-detected delimiter)", lines=10, value=""
         ),
         gr.Number(label="Text X Position", value=10),
         gr.Number(label="Text Y Position", value=10),
@@ -227,8 +224,7 @@ demo = gr.Interface(
         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"),
+        gr.Gallery(label="Product Images with Text", columns=2),
     ],
 )