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app.py

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+import gradio as gr
+import os
+import tempfile
+from typing import List, Dict, Any
+import fitz  # PyMuPDF for PDF processing
+from PIL import Image
+import pytesseract
+import io
+import json
+from datetime import datetime
+
+# Set up Tesseract OCR (make sure it's installed on your system)
+pytesseract.pytesseract.tesseract_cmd = '/usr/bin/tesseract'  # Update this path as needed
+
+# Custom theme for a warm, loving interface
+custom_theme = gr.themes.Soft(
+    primary_hue="pink",
+    secondary_hue="red",
+    neutral_hue="slate",
+    font=gr.themes.GoogleFont("Inter"),
+    text_size="lg",
+    spacing_size="lg",
+    radius_size="lg"
+).set(
+    button_primary_background_fill="*primary_600",
+    button_primary_background_fill_hover="*primary_700",
+    block_title_text_weight="600",
+)
+
+def extract_text_from_pdf(pdf_path: str) -> str:
+    """Extract text from PDF file using PyMuPDF"""
+    try:
+        doc = fitz.open(pdf_path)
+        text = ""
+        for page in doc:
+            text += page.get_text()
+        return text
+    except Exception as e:
+        raise gr.Error(f"Error processing PDF: {str(e)}")
+
+def extract_text_from_image(image_path: str) -> str:
+    """Extract text from image using Tesseract OCR"""
+    try:
+        img = Image.open(image_path)
+        text = pytesseract.image_to_string(img)
+        return text
+    except Exception as e:
+        raise gr.Error(f"Error processing image: {str(e)}")
+
+def extract_text_from_txt(txt_path: str) -> str:
+    """Extract text from TXT file"""
+    try:
+        with open(txt_path, 'r', encoding='utf-8') as f:
+            return f.read()
+    except Exception as e:
+        raise gr.Error(f"Error reading text file: {str(e)}")
+
+def process_uploaded_files(files: List[Dict[str, Any]]) -> str:
+    """Process all uploaded files and extract text content"""
+    all_text = ""
+
+    for file_data in files:
+        file_path = file_data['name']
+        file_ext = os.path.splitext(file_path)[1].lower()
+
+        if file_ext == '.pdf':
+            text = extract_text_from_pdf(file_path)
+        elif file_ext in ['.png', '.jpg', '.jpeg', '.gif', '.bmp']:
+            text = extract_text_from_image(file_path)
+        elif file_ext == '.txt':
+            text = extract_text_from_txt(file_path)
+        else:
+            raise gr.Error(f"Unsupported file type: {file_ext}")
+
+        all_text += f"\n\n=== Content from {os.path.basename(file_path)} ===\n\n"
+        all_text += text
+
+    return all_text
+
+def analyze_relationship(person_name: str, relationship_history: str) -> Dict[str, Any]:
+    """
+    Analyze relationship history and generate a love guide.
+    This is a mock function - in a real app, you would use an AI model here.
+    """
+    if not person_name.strip():
+        raise gr.Error("Please enter the person's name")
+
+    if not relationship_history.strip():
+        raise gr.Error("Please upload at least one document")
+
+    # Mock analysis - replace with actual AI model calls
+    analysis = {
+        "person_name": person_name,
+        "analysis_date": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "key_traits": [
+            "Loyal",
+            "Affectionate",
+            "Good listener",
+            "Supportive",
+            "Adventurous"
+        ],
+        "love_language": "Quality Time",
+        "communication_style": "Open and honest",
+        "conflict_resolution": "Calm discussion",
+        "ideal_partner_traits": [
+            "Patient",
+            "Understanding",
+            "Communicative",
+            "Supportive",
+            "Trustworthy"
+        ],
+        "relationship_goals": [
+            "Build trust",
+            "Improve communication",
+            "Create shared experiences",
+            "Support each other's growth"
+        ],
+        "love_guide": {
+            "daily_affirmations": [
+                f"Tell {person_name} how much you appreciate them",
+                "Give genuine compliments",
+                "Show interest in their day"
+            ],
+            "quality_time": [
+                "Plan regular date nights",
+                "Have deep conversations",
+                "Create shared hobbies"
+            ],
+            "conflict_tips": [
+                "Stay calm and listen",
+                "Use 'I' statements",
+                "Focus on solutions"
+            ]
+        }
+    }
+
+    return analysis
+
+def generate_love_guide(person_name: str, files: List[Dict[str, Any]]) -> Dict[str, Any]:
+    """Main function to process files and generate love guide"""
+    try:
+        # Process uploaded files
+        relationship_history = process_uploaded_files(files)
+
+        # Analyze relationship
+        analysis = analyze_relationship(person_name, relationship_history)
+
+        return analysis
+    except Exception as e:
+        raise gr.Error(f"Error generating love guide: {str(e)}")
+
+def format_analysis(analysis: Dict[str, Any]) -> str:
+    """Format analysis results for display"""
+    if not analysis:
+        return "No analysis available"
+
+    formatted = f"""
+# 💖 Love Guide for {analysis['person_name']}
+
+**Analysis Date:** {analysis['analysis_date']}
+
+## 🎯 Key Traits
+{' • '.join(analysis['key_traits'])}
+
+## 💬 Love Language
+**{analysis['love_language']}** - This person values meaningful time together and undivided attention.
+
+## 🗣️ Communication Style
+**{analysis['communication_style']}** - They appreciate open, honest conversations.
+
+## ✨ Ideal Partner Traits
+{' • '.join(analysis['ideal_partner_traits'])}
+
+## 🎯 Relationship Goals
+{' • '.join(analysis['relationship_goals'])}
+
+## 💡 Love Guide
+
+### Daily Affirmations
+{' • '.join(analysis['love_guide']['daily_affirmations'])}
+
+### Quality Time Ideas
+{' • '.join(analysis['love_guide']['quality_time'])}
+
+### Conflict Resolution Tips
+{' • '.join(analysis['love_guide']['conflict_tips'])}
+
+---
+
+**Remember:** Every relationship is unique. Use this guide as inspiration and adapt it to your specific situation.
+"""
+
+    return formatted
+
+def save_analysis(analysis: Dict[str, Any]) -> str:
+    """Save analysis to JSON file"""
+    try:
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = f"love_guide_{analysis['person_name']}_{timestamp}.json"
+
+        # Save to temporary file
+        temp_dir = tempfile.gettempdir()
+        file_path = os.path.join(temp_dir, filename)
+
+        with open(file_path, 'w', encoding='utf-8') as f:
+            json.dump(analysis, f, indent=2, ensure_ascii=False)
+
+        return file_path
+    except Exception as e:
+        raise gr.Error(f"Error saving analysis: {str(e)}")
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("""
+    # 💖 Love Guide Generator
+
+    **Built with anycoder** - [Visit our Space](https://huggingface.co/spaces/akhaliq/anycoder)
+
+    Upload documents from your relationship history (texts, PDFs, or images) and let AI analyze the patterns to create a personalized guide to loving your partner and understanding their ideal relationship.
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("## 📁 Upload Relationship Documents")
+            gr.Markdown("Upload transcripts, messages, or any documents that show your relationship history.")
+
+            file_upload = gr.File(
+                label="Upload Documents",
+                file_types=["text", "pdf", "image"],
+                file_count="multiple",
+                type="filepath",
+                height=150
+            )
+
+            person_name = gr.Textbox(
+                label="Person's Name",
+                placeholder="Enter the name of the person to analyze",
+                lines=1
+            )
+
+            analyze_btn = gr.Button("💖 Generate Love Guide", variant="primary", size="lg")
+
+        with gr.Column(scale=1):
+            gr.Markdown("## 📊 Analysis Results")
+
+            result_tabs = gr.Tabs()
+            with result_tabs:
+                with gr.Tab("📝 Love Guide"):
+                    love_guide_output = gr.Markdown()
+
+                with gr.Tab("💾 Raw Analysis"):
+                    raw_analysis = gr.JSON(label="Raw Analysis Data")
+
+                with gr.Tab("📥 Download"):
+                    download_output = gr.File(label="Download Love Guide")
+
+            status_output = gr.Textbox(label="Status", interactive=False)
+
+    # Event handlers
+    analyze_btn.click(
+        fn=generate_love_guide,
+        inputs=[person_name, file_upload],
+        outputs=[raw_analysis],
+        api_visibility="public"
+    ).then(
+        fn=format_analysis,
+        inputs=[raw_analysis],
+        outputs=[love_guide_output]
+    ).then(
+        fn=save_analysis,
+        inputs=[raw_analysis],
+        outputs=[download_output]
+    ).then(
+        fn=lambda: "✅ Love guide generated successfully! You can now view the results and download the guide.",
+        outputs=[status_output]
+    )
+
+    gr.Markdown("""
+    ## 💡 Tips for Best Results
+
+    - Upload multiple documents for more accurate analysis
+    - Include both positive and challenging moments
+    - Be specific about the person's name
+    - The more context you provide, the better the guide will be
+
+    ## ⚠️ Privacy Note
+
+    All files are processed locally and not stored on our servers. Your relationship data remains private.
+    """)
+
+# Launch the app with custom theme
+demo.launch(
+    theme=custom_theme,
+    footer_links=[
+        {"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"},
+        {"label": "Gradio Documentation", "url": "https://www.gradio.app/docs"}
+    ],
+    title="Love Guide Generator",
+    description="AI-powered relationship analysis and love guide generator"
+)

requirements.txt

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+PyMuPDF
+Pillow
+gradio>=6.0
+pytesseract
+requests
+numpy
+opencv-python