feat: implement Gradio interface for misinformation detection and enhance document analysis functionality

.gitignore

@@ -8,3 +8,11 @@ wheels/
 
 # Virtual environments
 .venv
+service-account.json
+*.ipynb
+.ipynb_checkpoints/
+data/
+notebooks/
+.env
+.continue/
+.vscode/

main.py

@@ -1,6 +1,147 @@
-def main():
-    print("Hello from fhoc!")
+"""
+Simplified Gradio interface for misinformation detection.
+This is the minimal version for quick prototyping.
+"""
+
+import logging
+import pprint
+from pathlib import Path
+import gradio as gr
+from src.utils.parser import MarkdownConverter
+from src.utils.chunking import get_base_chunks
+from src.api.apis import classify_text
+from src.api.rebuttal import RebuttalStructure
+
+logging.basicConfig(
+    format="%(asctime)s %(levelname)s %(message)s",
+    datefmt="%m/%d/%Y %I:%M:%S %p",
+    level=logging.INFO,
+)
+logger = logging.getLogger(__name__)
+
+
+def analyze_document(pdf_file, url_text):
+    """
+    Simple analysis function that processes PDF or URL input.
+
+    Returns formatted HTML with highlighted misinformation and rebuttals.
+    """
+    # Step 1: Convert to markdown
+    md = MarkdownConverter()
+
+    if pdf_file is not None:
+        logger.info(f"📄 Processing PDF file: {pdf_file}")
+        document = md.run(pdf_file)
+        source = Path(pdf_file).name
+    elif url_text and url_text.strip():
+        logger.info(f"🔗 Processing URL: {url_text}")
+        document = md.run(url_text)
+        source = url_text
+    else:
+        return "<p style='color: red;'>❌ Please provide a PDF or URL</p>"
+
+    # Step 2: Chunk the document
+    chunks = get_base_chunks(document, chunk_size=1000, chunk_overlap=200)
+    logger.info(f"✅ Created {len(chunks)} chunks")
+    logger.info(f"✅ First chunk: {pprint.pformat(chunks[0], indent=4)}")
+
+    # Step 3: Classify chunks
+    # Handle both Document objects and dicts
+    responses = []
+    for chunk in chunks:
+        # Extract text from Document object or dict
+        if hasattr(chunk, "page_content"):
+            # LangChain Document object
+            chunk_text = chunk.page_content
+        elif isinstance(chunk, dict):
+            # Dictionary format
+            chunk_text = chunk.get("metadata", {}).get("chunk") or chunk.get("text", "")
+        else:
+            # Fallback: try to convert to string
+            chunk_text = str(chunk)
+
+        if chunk_text:
+            responses.append(classify_text(chunk_text))
+
+    # Step 4: Keep only positive misinformation detections
+    positive_responses = [r for r in responses if r.category != "0"]
+
+    # If no misinformation found
+    if not positive_responses:
+        return f"""
+        <div style='padding: 30px; background: #d4edda; border-radius: 8px; text-align: center;'>
+            <h2>✅ No Misinformation Detected</h2>
+            <p>Source: {source}</p>
+        </div>
+        """
+
+    # Step 5: Generate rebuttals
+    rebuttal_gen = RebuttalStructure()
+    rebuttals = [rebuttal_gen.run(misinfo) for misinfo in positive_responses]
+
+    # Step 6: Build output HTML
+    output = f"""
+    <div style='max-width: 900px; margin: 0 auto; padding: 20px; font-family: Arial, sans-serif;'>
+        <h2>📋 Misinformation Analysis</h2>
+        <p><strong>Source:</strong> {source}</p>
+        <p><strong>Issues Found:</strong> {len(positive_responses)} out of {len(chunks)} sections</p>
+        <hr>
+    """
+
+    # Map responses to chunks
+    response_index = 0
+    for i, chunk in enumerate(chunks):
+        # Extract text from Document object or dict
+        if hasattr(chunk, "page_content"):
+            chunk_text = chunk.page_content
+        elif isinstance(chunk, dict):
+            chunk_text = chunk.get("metadata", {}).get("chunk") or chunk.get("text", "")
+        else:
+            chunk_text = str(chunk)
+
+        # Check if this chunk has misinformation
+        if i < len(responses) and responses[i].category != "0":
+            # Highlighted section with rebuttal
+            output += f"""
+            <div style='background: #fff3cd; padding: 20px; margin: 20px 0; border-left: 4px solid #ff9800; border-radius: 4px;'>
+                <p style='margin: 0; font-size: 16px;'>{chunk_text}</p>
+                <div style='margin-top: 15px; padding: 15px; background: white; border-left: 3px solid #2196F3; border-radius: 4px;'>
+                    <strong style='color: #2196F3;'>🔍 Fact Check:</strong>
+                    <p style='margin: 5px 0 0 0;'>{rebuttals[response_index]}</p>
+                </div>
+            </div>
+            """
+            response_index += 1
+        else:
+            # Normal section
+            output += f"""
+            <div style='padding: 15px; margin: 15px 0; background: #f5f5f5; border-radius: 4px;'>
+                <p style='margin: 0;'>{chunk_text}</p>
+            </div>
+            """
+
+    output += "</div>"
+    return output
+
+
+# Create simple Gradio interface
+demo = gr.Interface(
+    fn=analyze_document,
+    inputs=[
+        gr.File(label="📄 Upload PDF (optional)", file_types=[".pdf"]),
+        gr.Textbox(
+            label="🔗 Or Enter URL (optional)",
+            placeholder="https://example.com/article",
+        ),
+    ],
+    outputs=gr.HTML(label="Analysis Results"),
+    title="🔍 Misinformation Detector",
+    description="Upload a PDF or enter a URL to detect and fact-check potential misinformation.",
+)
 
 
 if __name__ == "__main__":
-    main()
+    demo.launch(
+        server_name="127.0.0.1",  # Use 127.0.0.1 instead of 0.0.0.0 for Safari
+        server_port=7860,
+    )

pyproject.toml

@@ -13,6 +13,7 @@ dependencies = [
     "langchain-google-genai>=4.1.2",
     "langchain-openai>=1.1.6",
     "langchain-text-splitters>=1.1.0",
+    "markdown>=3.10",
     "pip>=25.3",
     "python-dotenv>=1.2.1",
     "requests>=2.32.5",