Create app.py

app.py

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+import fitz  # PyMuPDF
+import openai
+import streamlit as st
+from datetime import datetime
+import json
+import random
+from PIL import Image, ImageDraw, ImageFont
+from io import BytesIO
+
+# Initialize OpenAI API key
+openai.api_key = "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"
+# Metadata template
+metadata_template = {
+    "catalog_name": "MeData",
+    "file_name": "",
+    "file_directory": [],
+    "file_type": [],
+    "page_count": [],
+    "storage_type": ["local"],
+    "last_modified": [],
+    "chunks": {}
+}
+
+# Function to extract text from PDF
+def extract_text_from_pdf(file):
+    doc = fitz.open(stream=file.read(), filetype="pdf")
+    pages_content = []
+    for page_num in range(doc.page_count):
+        page = doc[page_num]
+        pages_content.append(page.get_text())
+    return pages_content
+
+# Function to create metadata template
+def create_metadata_template(file_name):
+    metadata = metadata_template.copy()
+    metadata["file_name"] = file_name
+    metadata["last_modified"] = [datetime.now().isoformat()]
+    return metadata
+
+# Function to detect tags using OpenAI API
+def detect_tags_with_openai(text):
+    prompt = (
+        "Extract key information as JSON format where each key has a 'value' and 'evidence'. "
+        f"Text: {text}"
+    )
+
+    response = openai.ChatCompletion.create(
+        model="gpt-4",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant that extracts structured data."},
+            {"role": "user", "content": prompt}
+        ],
+        max_tokens=500,
+        temperature=0
+    )
+
+    response_text = response.choices[0].message['content'].strip()
+    try:
+        extracted_tags = json.loads(response_text)
+    except json.JSONDecodeError:
+        st.error("Error: Unable to parse JSON response from OpenAI")
+        extracted_tags = {}
+
+    return extracted_tags
+
+# Parse PDF and generate metadata
+def parse_to_metadata(file, file_name):
+    metadata = create_metadata_template(file_name)
+
+    if file_name.endswith(".pdf"):
+        pages_content = extract_text_from_pdf(file)
+        metadata["page_count"] = [len(pages_content)]
+
+        for i, page_text in enumerate(pages_content):
+            chunk_key = f"{i}"
+            metadata["chunks"][chunk_key] = {"page_range": [str(i + 1)]}
+            extracted_tags = detect_tags_with_openai(page_text)
+
+            for tag, tag_data in extracted_tags.items():
+                metadata["chunks"][chunk_key][tag] = tag_data
+
+                if tag not in metadata:
+                    metadata[tag] = []
+                metadata[tag].append(tag_data.get("value", ""))
+
+    return metadata
+
+# Generate random color
+def random_color():
+    return (random.randint(100, 255), random.randint(100, 255), random.randint(100, 255))
+
+# Generate image with colored tag wrappers
+def generate_colored_tags_image(metadata):
+    img_width, img_height = 800, 1000
+    img = Image.new("RGB", (img_width, img_height), "white")
+    draw = ImageDraw.Draw(img)
+
+    font = ImageFont.load_default()
+    y_position = 20
+
+    for chunk in metadata["chunks"].values():
+        for tag, tag_data in chunk.items():
+            if "value" in tag_data:
+                value = tag_data["value"]
+                color = random_color()
+                text = f"{tag}: {value}"
+
+                text_bbox = draw.textbbox((20, y_position), text, font=font)
+                text_width = text_bbox[2] - text_bbox[0]
+                text_height = text_bbox[3] - text_bbox[1]
+
+                draw.rectangle(
+                    [(20, y_position), (20 + text_width + 10, y_position + text_height + 10)],
+                    fill=color
+                )
+
+                draw.text((25, y_position + 5), text, fill="black", font=font)
+                y_position += text_height + 20
+
+                if y_position > img_height - 40:
+                    img = img.resize((img_width, y_position + 40))
+                    draw = ImageDraw.Draw(img)
+
+    return img
+
+# Streamlit UI
+st.title("Metadata Generator Tool")
+
+uploaded_file = st.file_uploader("Upload a PDF or Image", type=["pdf", "jpg", "png"])
+
+if uploaded_file:
+    file_name = uploaded_file.name
+    file_type = file_name.split(".")[-1]
+
+    st.write(f"**File Name:** {file_name}")
+    st.write(f"**File Type:** {file_type}")
+
+    if file_type == "pdf":
+        metadata = parse_to_metadata(uploaded_file, file_name)
+
+        # Save metadata as JSON
+        output_json = json.dumps(metadata, indent=4)
+        st.download_button(
+            label="Download Metadata as JSON",
+            data=output_json,
+            file_name="metadata.json",
+            mime="application/json"
+        )
+
+        # Display metadata
+        st.subheader("Extracted Metadata:")
+        st.json(metadata)
+
+        # Generate and display image with tags
+        st.subheader("Visualized Tags:")
+        tag_image = generate_colored_tags_image(metadata)
+        img_bytes = BytesIO()
+        tag_image.save(img_bytes, format="PNG")
+        st.image(tag_image, caption="Tags Visualization")
+
+        # Download tag image
+        st.download_button(
+            label="Download Tag Visualization Image",
+            data=img_bytes.getvalue(),
+            file_name="tag_visualization.png",
+            mime="image/png"
+        )
+    else:
+        st.error("Currently, only PDF files are supported for metadata generation.")