Update app.py

app.py

@@ -26,167 +26,6 @@ from helpers.text_extraction import *
 
 def authenticate(username, password):
     return username == os.getenv("HF_USERNAME") and password == os.getenv("HF_PASSWORD")
-    
-# Helper Functions
-def convert_to_rgb(image_path):
-    img = Image.open(image_path)
-    rgb_img = img.convert("RGB")
-    return rgb_img
-
-def preprocess_image(image):
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-    denoised = cv2.fastNlMeansDenoising(binary, None, 30, 7, 21)
-    resized = cv2.resize(denoised, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
-    return resized
-
-def extract_vertical_blocks(image):
-    image_np = np.array(image)
-    data = pytesseract.image_to_data(image_np, lang='fra', output_type=Output.DICT)
-
-    blocks = []
-    current_block = ""
-    current_block_coords = [float('inf'), float('inf'), 0, 0]
-    last_bottom = -1
-    line_height = 0
-
-    for i in range(len(data['text'])):
-        if int(data['conf'][i]) > 0:
-            text = data['text'][i]
-            x, y, w, h = data['left'][i], data['top'][i], data['width'][i], data['height'][i]
-
-            if line_height == 0:
-                line_height = h * 1.2
-
-            if y > last_bottom + line_height:
-                if current_block:
-                    blocks.append({
-                        "text": current_block.strip(),
-                        "coords": current_block_coords
-                    })
-                    current_block = ""
-                    current_block_coords = [float('inf'), float('inf'), 0, 0]
-
-            current_block += text + " "
-            current_block_coords[0] = min(current_block_coords[0], x)
-            current_block_coords[1] = min(current_block_coords[1], y)
-            current_block_coords[2] = max(current_block_coords[2], x + w)
-            current_block_coords[3] = max(current_block_coords[3], y + h)
-
-            last_bottom = y + h
-
-    if current_block:
-        blocks.append({
-            "text": current_block.strip(),
-            "coords": current_block_coords
-        })
-
-    return blocks
-
-def draw_blocks_on_image(image_path, blocks, output_path):
-    image = cv2.imread(image_path)
-    for block in blocks:
-        coords = block['coords']
-        cv2.rectangle(image, (coords[0], coords[1]), (coords[2], coords[3]), (0, 0, 255), 2)
-    cv2.imwrite(output_path, image)
-    return output_path
-
-def process_image(image, output_folder, page_number):
-    image = convert_to_rgb(image)
-    blocks = extract_vertical_blocks(image)
-    base_name = f'page_{page_number + 1}.png'
-    image_path = os.path.join(output_folder, base_name)
-    image.save(image_path)
-    annotated_image_path = os.path.join(output_folder, f'annotated_{base_name}')
-    annotated_image_path = draw_blocks_on_image(image_path, blocks, annotated_image_path)
-    return blocks, annotated_image_path
-
-def save_extracted_text(blocks, page_number, output_folder):
-    text_file_path = os.path.join(output_folder, 'extracted_text.txt')
-    with open(text_file_path, 'a', encoding='utf-8') as f:
-        f.write(f"[PAGE {page_number}]\n")
-        for block in blocks:
-            f.write(block['text'] + "\n")
-        f.write("[FIN DE PAGE]\n\n")
-    return text_file_path
-
-# Gemini Functions
-def initialize_gemini():
-    try:
-        genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
-        generation_config = {
-            "temperature": 1,
-            "top_p": 0.95,
-            "top_k": 40,
-            "max_output_tokens": 8192,
-            "response_mime_type": "text/plain",
-        }
-        model = genai.GenerativeModel(
-            model_name="gemini-1.5-pro",
-            generation_config=generation_config,
-        )
-        return model
-    except Exception as e:
-        raise gr.Error(f"Error initializing Gemini: {str(e)}")
-
-def create_prompt(extracted_text: str, path_to_data_to_extract: str) -> str:
-
-    # load data to extract
-    with open(path_to_data_to_extract, 'r', encoding='utf-8') as file:
-        data_to_extract = json.load(file)
-
-    prompt = f"""Tu es un assistant juridique expert en analyse de documents judiciaires français.
-    Je vais te fournir le contenu d'un document judiciaire extrait d'un PDF.
-    Ta tâche est d'analyser ce texte et d'en extraire les informations suivantes de manière précise :
-
-    {json.dumps(data_to_extract, indent=2, ensure_ascii=False)}
-
-    Voici quelques règles à suivre :
-    - Si une information n'est pas présente dans le texte, indique "Non spécifié" pour cette catégorie.
-    - Pour les noms des parties (demandeurs et défendeurs, et leurs avocats), liste tous ceux que tu trouves
-    - Assure-toi de différencier correctement les demandeurs des défendeurs.
-    - Si tu n'es pas sûr d'une information, indique-le clairement.
-
-    Présente tes résultats sous forme de JSON, en utilisant les catégories mentionnées ci-dessus.
-
-    Voici le contenu du document :
-
-    {extracted_text.strip()}
-
-    Analyse ce texte et fournis-moi les informations demandées au format JSON uniquement.""".strip()
-
-    return prompt
-
-def extract_data_with_gemini(text_file_path: str, path_to_data_to_extract: str) -> dict:
-    try:
-        # Initialize Gemini
-        model = initialize_gemini()
-
-        # Read the extracted text
-        with open(text_file_path, 'r', encoding='utf-8') as f:
-            extracted_text = f.read()
-
-        # Create prompt and get response
-        prompt = create_prompt(extracted_text, path_to_data_to_extract)
-        response = model.generate_content(prompt)
-
-        # Parse the JSON response
-        try:
-            # Extract JSON from the response text
-            json_str = response.text
-            if "json" in json_str.lower():
-                json_str = json_str.split("json")[1].split("```")[0]
-            elif "```" in json_str:
-                json_str = json_str.split("```")[1]
-            result = json.loads(json_str)
-        except:
-            result = {"error": "Failed to parse JSON response", "raw_response": response.text}
-
-        return result
-    except Exception as e:
-        raise gr.Error(f"Error in Gemini processing: {str(e)}")
-
-
             
 # Main Processing Function
 def process_pdf(pdf_file):