Sparrow

app.py

@@ -1,250 +1,3 @@
-# import gradio as gr
-# import spaces
-# from gradio.themes.base import Base
-# from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
-# from qwen_vl_utils import process_vision_info
-# from PIL import Image
-# from datetime import datetime
-# import os
-# import json
-# import fitz  # PyMuPDF
-
-# # Define a custom theme inheriting from the soft theme
-# class CustomTheme(Base):
-#     def __init__(self):
-#         super().__init__()
-#         self.primary_hue = "blue"
-#         self.secondary_hue = "sky"
-
-# custom_theme = CustomTheme()
-
-# DESCRIPTION = "A powerful vision-language model that can understand images and text to provide detailed analysis."
-
-# def array_to_image_path(image_filepath, max_width=1250, max_height=1750):
-#     if image_filepath is None:
-#         raise ValueError("No image provided.")
-
-#     img = Image.open(image_filepath)
-#     width, height = img.size
-#     if width > max_width or height > max_height:
-#         img.thumbnail((max_width, max_height))
-
-#     return os.path.abspath(image_filepath), img.width, img.height
-
-# def convert_pdf_to_images(pdf_path):
-#     """Opens a PDF and converts each page into a high-resolution PNG image."""
-#     image_paths = []
-#     doc = fitz.open(pdf_path)
-#     base_name = os.path.splitext(os.path.basename(pdf_path))[0]
-
-#     for i, page in enumerate(doc):
-#         pix = page.get_pixmap(dpi=200)
-#         timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-#         image_path = f"{base_name}_page_{i+1}_{timestamp}.png"
-#         pix.save(image_path)
-#         image_paths.append(image_path)
-
-#     doc.close()
-#     return image_paths
-
-# # Initialize the model and processor
-# model = Qwen2VLForConditionalGeneration.from_pretrained(
-#     "Qwen/Qwen2-VL-7B-Instruct",
-#     torch_dtype="auto",
-#     device_map="auto"
-# )
-# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
-
-# @spaces.GPU
-# def run_inference(uploaded_files, text_input):
-#     results = []
-#     temp_files_to_clean = []
-
-#     json_prompt = (
-#         f"{text_input}\n\nBased on the image and the query, respond ONLY with a single, "
-#         "valid JSON object. This object should be well-structured, using nested objects "
-#         "and arrays to logically represent the information."
-#     )
-
-#     if not uploaded_files:
-#         error_json = json.dumps({"error": "No file provided. Please upload an image or PDF."}, indent=4)
-#         return error_json, gr.Button(interactive=False)
-
-#     image_paths_to_process = []
-#     unsupported_files = []
-#     for file_obj in uploaded_files:
-#         file_path = file_obj.name
-#         temp_files_to_clean.append(file_path)
-
-#         if file_path.lower().endswith('.pdf'):
-#             pdf_page_images = convert_pdf_to_images(file_path)
-#             image_paths_to_process.extend(pdf_page_images)
-#             temp_files_to_clean.extend(pdf_page_images)
-#         elif file_path.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif', '.webp')):
-#             image_paths_to_process.append(file_path)
-#         else:
-#             unsupported_files.append(os.path.basename(file_path))
-
-#     if unsupported_files:
-#         unsupported_str = ", ".join(unsupported_files)
-#         results.append(json.dumps({
-#             "error": f"Unsupported file type(s) were ignored: {unsupported_str}",
-#             "details": "Please upload only images (PNG, JPG, etc.) or PDF files."
-#         }, indent=4))
-
-#     for image_file in image_paths_to_process:
-#         try:
-#             image_path, width, height = array_to_image_path(image_file)
-
-#             messages = [
-#                 {"role": "user", "content": [
-#                     {"type": "image", "image": image_path, "resized_height": height, "resized_width": width},
-#                     {"type": "text", "text": json_prompt}
-#                 ]}
-#             ]
-#             text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-#             image_inputs, video_inputs = process_vision_info(messages)
-#             inputs = processor(text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt").to("cuda")
-
-#             generated_ids = model.generate(**inputs, max_new_tokens=4096)
-#             generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
-#             raw_output = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-#             raw_text = raw_output[0]
-
-#             try:
-#                 start_index = raw_text.find('{')
-#                 end_index = raw_text.rfind('}') + 1
-#                 if start_index != -1 and end_index != 0:
-#                     json_string = raw_text[start_index:end_index]
-#                     parsed_json = json.loads(json_string)
-#                     parsed_json['source_page'] = os.path.basename(image_path)
-#                     formatted_json = json.dumps(parsed_json, indent=4)
-#                     results.append(formatted_json)
-#                 else:
-#                     results.append(f'{{"error": "Model did not return valid JSON.", "source_page": "{os.path.basename(image_path)}", "raw_response": "{raw_text}"}}')
-#             except json.JSONDecodeError:
-#                 results.append(f'{{"error": "Failed to decode JSON.", "source_page": "{os.path.basename(image_path)}", "raw_response": "{raw_text}"}}')
-#         except Exception as e:
-#             results.append(f'{{"error": "An unexpected error occurred during processing.", "details": "{str(e)}"}}')
-
-#     for f in temp_files_to_clean:
-#         if os.path.exists(f):
-#             try:
-#                 os.remove(f)
-#             except OSError as e:
-#                 print(f"Error deleting file {f}: {e}")
-
-#     final_json = "\n---\n".join(results)
-#     is_error = '"error":' in final_json
-#     return final_json, gr.Button(interactive=not is_error)
-
-
-# @spaces.GPU
-# def generate_explanation(json_text):
-#     if not json_text or '"error":' in json_text:
-#         return "Cannot generate an explanation. Please produce a valid JSON output first. 🙁"
-
-#     explanation_prompt = (
-#         "You are an expert data analyst. Your task is to provide a comprehensive, human-readable explanation "
-#         "of the following JSON data, which may represent one or more pages from a document. First, provide a textual explanation. "
-#         "If the JSON contains data from multiple sources (pages), explain each one. Then, if the JSON data represents a table, "
-#         "a list of items, or a receipt, you **must** re-format the key information into a Markdown table for clarity.\n\n"
-#         f"JSON Data:\n```json\n{json_text}\n```"
-#     )
-
-#     messages = [{"role": "user", "content": explanation_prompt}]
-#     text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-#     inputs = processor(text=[text], return_tensors="pt").to("cuda")
-
-#     generated_ids = model.generate(**inputs, max_new_tokens=2048)
-#     generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
-#     explanation_output = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=True)[0]
-
-#     return explanation_output
-
-# # --- FINAL AND MOST ROBUST CSS FIX ---
-# css = """
-#   .gradio-container { font-family: 'IBM Plex Sans', sans-serif; }
-
-#   /* --- Light Mode Styles --- */
-#   #output-code, #output-code pre, #output-code code {
-#     background-color: #f0f0f0;
-#     border: 1px solid #e0e0e0;
-#     border-radius: 7px;
-#     color: #333;
-#   }
-#   #output-code .token.punctuation { color: #393a34; }
-#   #output-code .token.property, #output-code .token.string { color: #0b7500; }
-#   #output-code .token.number { color: #2973b7; }
-#   #output-code .token.boolean { color: #9a050f; }
-
-#   #explanation-box {
-#     min-height: 200px;
-#     border: 1px solid #e0e0e0;
-#     padding: 15px;
-#     border-radius: 7px;
-#   }
-
-#   /* --- Dark Mode Overrides targeting Gradio's .dark class --- */
-#   .dark #output-code, .dark #output-code pre, .dark #output-code code {
-#     background-color: #2b2b2b !important;
-#     border: 1px solid #444 !important;
-#     color: #f0f0f0 !important;
-#   }
-#   .dark #explanation-box {
-#     border: 1px solid #444 !important;
-#   }
-#   /* This is a catch-all to ensure all parts of the syntax start light-colored */
-#   .dark #output-code code span {
-#      color: #f0f0f0 !important;
-#   }
-#   /* Then, we apply specific colors for syntax highlighting on top */
-#   .dark #output-code .token.punctuation { color: #ccc !important; }
-#   .dark #output-code .token.property, .dark #output-code .token.string { color: #90ee90 !important; }
-#   .dark #output-code .token.number { color: #add8e6 !important; }
-#   .dark #output-code .token.boolean { color: #f08080 !important; }
-# """
-
-# with gr.Blocks(theme=custom_theme, css=css) as demo:
-#     gr.Markdown("# Sparrow Qwen2-VL-7B Vision AI 👁️")
-#     gr.Markdown(DESCRIPTION)
-
-#     with gr.Row():
-#         with gr.Column(scale=1):
-#             input_files = gr.Files(label="Upload Images or PDFs")
-#             text_input = gr.Textbox(
-#                 label="Your Query",
-#                 placeholder="e.g., Extract the total amount from this receipt."
-#             )
-#             submit_btn = gr.Button("Analyze File(s)", variant="primary")
-
-#         with gr.Column(scale=2):
-#             output_text = gr.Code(
-#                 label="Full JSON Response",
-#                 language="json",
-#                 elem_id="output-code",
-#                 interactive=False # This makes the output field read-only
-#             )
-#             explanation_btn = gr.Button("📄 Generate Detailed Explanation", interactive=False)
-#             explanation_output = gr.Markdown(label="Detailed Explanation", elem_id="explanation-box")
-
-#     submit_btn.click(
-#         fn=run_inference,
-#         inputs=[input_files, text_input],
-#         outputs=[output_text, explanation_btn]
-#     )
-
-#     explanation_btn.click(
-#         fn=generate_explanation,
-#         inputs=[output_text],
-#         outputs=[explanation_output],
-#         show_progress='full'
-#     )
-
-# demo.queue()
-# demo.launch(debug=True)
-
-
 import gradio as gr
 import spaces
 from gradio.themes.base import Base