Update app.py

app.py

@@ -6,43 +6,62 @@ from functools import lru_cache
 from PIL import Image
 import cv2
 import numpy as np
+import datetime
+import uuid
+import requests
+from reportlab.lib.pagesizes import letter
+from reportlab.platypus import SimpleDocTemplate, Image as RLImage, Paragraph, Spacer
+from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+from reportlab.lib.enums import TA_JUSTIFY
+from reportlab.lib import colors
 
-# OpenAI API Configuration
+# OpenAI ve GitHub Konfigürasyonları
 openai.api_key = os.getenv("OPENAI_API_KEY")
+GITHUB_TOKEN = os.getenv("GITHUB_TOKEN")
+REPO_OWNER = os.getenv("GITHUB_REPO_OWNER")
+REPO_NAME = os.getenv("GITHUB_REPO_NAME")
+
+# Sabitler
 ANALYSIS_MODEL = "gpt-4o"
 MAX_TOKENS = 4096
+PDF_DIR = "reports"
 
-# Persona Definitions
+# Persona Tanımları
 PERSONAS = {
     "Aggressive Trader": {
         "description": "High-risk, short-term gains, leverages volatile market movements.",
-        "prompt": "Focus on high-risk strategies, short-term gains, and leverage opportunities. Suggest aggressive entry and exit points."
+        "prompt": "Focus on high-risk strategies, short-term gains, and leverage opportunities. Suggest aggressive entry and exit points.",
+        "color": colors.red
     },
     "Conservative Trader": {
         "description": "Low-risk, long-term investments, prioritizes capital preservation.",
-        "prompt": "Focus on low-risk strategies, long-term investments, and capital preservation. Suggest safe entry points and strict stop-loss levels."
+        "prompt": "Focus on low-risk strategies, long-term investments, and capital preservation. Suggest safe entry points and strict stop-loss levels.",
+        "color": colors.blue
     },
     "Neutral Trader": {
         "description": "Balanced approach, combines short and long-term strategies.",
-        "prompt": "Focus on balanced strategies, combining short and long-term opportunities. Suggest moderate risk levels and trend-following approaches."
+        "prompt": "Focus on balanced strategies, combining short and long-term opportunities. Suggest moderate risk levels and trend-following approaches.",
+        "color": colors.green
     },
     "Reactive Trader": {
         "description": "Quick decisions based on market news and social media trends.",
-        "prompt": "Focus on quick decision-making, momentum trading, and reacting to market news. Suggest strategies based on current trends and FOMO opportunities."
+        "prompt": "Focus on quick decision-making, momentum trading, and reacting to market news. Suggest strategies based on current trends and FOMO opportunities.",
+        "color": colors.orange
     },
     "Systematic Trader": {
         "description": "Algorithm-based, rule-driven, and emotionless trading.",
-        "prompt": "Focus on algorithmic strategies, backtested rules, and quantitative analysis. Suggest data-driven entry and exit points."
+        "prompt": "Focus on algorithmic strategies, backtested rules, and quantitative analysis. Suggest data-driven entry and exit points.",
+        "color": colors.purple
     }
 }
 
-# System Prompt Configuration
+# Sistem Prompt'u
 SYSTEM_PROMPT = """Professional Crypto Technical Analyst:
 1. Identify all technical patterns in the chart
 2. Determine key support/resistance levels
 3. Analyze volume and momentum indicators
 4. Calculate risk/reward ratios
-5. Provide clear trading recommendations based on the selected persona
+5. Provide clear trading recommendations
 6. Include specific price targets
 7. Assess market sentiment
 8. Evaluate trend strength
@@ -52,276 +71,221 @@ SYSTEM_PROMPT = """Professional Crypto Technical Analyst:
 class ChartAnalyzer:
     def __init__(self):
         self.last_analysis = ""
+        os.makedirs(PDF_DIR, exist_ok=True)
 
     def validate_image(self, image_path: str) -> bool:
-        """Validate if the uploaded file is a valid image and looks like a chart"""
         try:
-            # Check if the file is an image
             with Image.open(image_path) as img:
-                img.verify()  # Verify that it is a valid image
+                img.verify()
 
-            # Check if the image looks like a chart (basic check)
             img = cv2.imread(image_path)
             if img is None:
                 return False
 
-            # Simple check for chart-like features (e.g., axes, grid lines)
             gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
             edges = cv2.Canny(gray, 50, 150)
-            if np.sum(edges) < 1000:  # Arbitrary threshold for edge detection
-                return False
-
-            return True
+            return np.sum(edges) >= 1000
         except Exception:
             return False
 
     def optimize_image(self, image_path: str) -> str:
-        """Optimize image size and quality"""
         try:
             img = Image.open(image_path)
-            # Resize if the image is too large
-            if img.size[0] > 1024 or img.size[1] > 1024:
-                img = img.resize((1024, 1024), Image.ANTIALIAS)
-            # Save optimized image
-            optimized_path = "optimized_chart.png"
+            original_width, original_height = img.size
+            max_size = 1024
+
+            if original_width > max_size or original_height > max_size:
+                ratio = min(max_size/original_width, max_size/original_height)
+                new_size = (int(original_width * ratio), int(original_height * ratio))
+                img = img.resize(new_size, Image.LANCZOS)
+
+            unique_id = uuid.uuid4().hex
+            optimized_path = f"{PDF_DIR}/optimized_chart_{unique_id}.png"
             img.save(optimized_path, "PNG", optimize=True, quality=85)
             return optimized_path
         except Exception as e:
             print(f"Image optimization error: {str(e)}")
-            return image_path  # Fallback to original image
+            return image_path
 
     def encode_image(self, image_path: str) -> str:
-        """Encode image to base64 with validation"""
         if not os.path.exists(image_path):
             raise FileNotFoundError("File not found")
-        if not image_path.lower().endswith(('.png', '.jpg', '.jpeg')):
-            raise ValueError("Unsupported file format")
+        
         if os.path.getsize(image_path) > 5 * 1024 * 1024:
             raise ValueError("Maximum file size is 5MB")
             
         with open(image_path, "rb") as image_file:
             return base64.b64encode(image_file.read()).decode('utf-8')
 
-    @lru_cache(maxsize=100)  # Cache up to 100 recent analyses
+    @lru_cache(maxsize=100)
     def analyze_chart(self, image_path: str, persona: str) -> str:
-        """Core analysis function with caching and persona-based recommendations"""
         try:
-            # Optimize image before analysis
             optimized_path = self.optimize_image(image_path)
             base64_image = self.encode_image(optimized_path)
             
-            # Add persona-specific instructions to the system prompt
             persona_prompt = PERSONAS.get(persona, {}).get("prompt", "")
             full_system_prompt = f"{SYSTEM_PROMPT}\n\n{persona_prompt}"
             
             response = openai.ChatCompletion.create(
                 model=ANALYSIS_MODEL,
                 messages=[
-                    {
-                        "role": "system",
-                        "content": full_system_prompt
-                    },
-                    {
-                        "role": "user",
-                        "content": [
-                            {"type": "text", "text": "Perform detailed technical analysis of this chart:"},
-                            {
-                                "type": "image_url",
-                                "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}
-                            }
-                        ]
-                    }
+                    {"role": "system", "content": full_system_prompt},
+                    {"role": "user", "content": [
+                        {"type": "text", "text": "Perform detailed technical analysis of this chart:"},
+                        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}
+                        }
+                    ]}
                 ],
                 max_tokens=MAX_TOKENS
             )
             
-            return response.choices[0].message.content
+            analysis_text = response.choices[0].message.content
+            self.last_analysis = analysis_text
+            return analysis_text
             
-        except openai.error.APIError as e:
-            return f"OpenAI API Error: {str(e)}"
         except Exception as e:
-            return f"Analysis Error: {str(e)}"
+            return f"Error: {str(e)}"
 
-# Custom CSS for optimized layout
-custom_css = """
-:root {
-    --primary-color: #2563eb;
-    --secondary-color: #1e40af;
-    --background-color: #f8fafc;
-    --text-color: #1e293b;
-    --border-color: #e2e8f0;
-}
-
-body {
-    background-color: var(--background-color);
-    color: var(--text-color);
-}
+def create_pdf_styles():
+    styles = getSampleStyleSheet()
+    styles.add(ParagraphStyle(
+        'Justify',
+        parent=styles['BodyText'],
+        alignment=TA_JUSTIFY,
+        spaceAfter=6
+    ))
+    styles.add(ParagraphStyle(
+        'PersonaTitle',
+        fontSize=14,
+        textColor=colors.white,
+        backColor=colors.darkblue,
+        alignment=1,
+        spaceAfter=12
+    ))
+    return styles
 
-.container {
-    max-width: 1200px;
-    margin: 0 auto;
-    padding: 20px;
-}
-
-.analysis-box {
-    background-color: white;
-    border-radius: 12px;
-    padding: 24px;
-    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
-    border: 1px solid var(--border-color);
-    height: 400px; /* Initial height */
-    overflow-y: auto;
-    width: 100%;
-    transition: height 0.3s ease;
-}
-
-.upload-box {
-    background-color: white;
-    border-radius: 12px;
-    padding: 24px;
-    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
-    border: 1px solid var(--border-color);
-    width: 100%;
-}
-
-.primary-button {
-    background-color: var(--primary-color) !important;
-    color: white !important;
-    border-radius: 8px !important;
-    padding: 12px 24px !important;
-    font-weight: 500 !important;
-    width: 100%;
-}
-
-.primary-button:hover {
-    background-color: var(--secondary-color) !important;
-}
-
-.markdown-container {
-    max-width: 100%;
-    margin: 0 auto;
-    padding: 0 15px;
-    word-wrap: break-word;
-}
-
-.loading-container {
-    display: flex;
-    justify-content: center;
-    align-items: center;
-    height: 100%;
-}
-
-.loading-text {
-    color: var(--primary-color);
-    font-size: 1.2rem;
-    text-align: center;
-    margin-top: 2rem;
-}
+def generate_pdf(image_path: str, analysis_text: str, persona: str) -> str:
+    styles = create_pdf_styles()
+    timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+    filename = f"{PDF_DIR}/report_{timestamp}_{uuid.uuid4().hex[:6]}.pdf"
+    
+    doc = SimpleDocTemplate(filename, pagesize=letter)
+    story = []
 
-.loading-spinner {
-    border: 4px solid #f3f3f3;
-    border-top: 4px solid var(--primary-color);
-    border-radius: 50%;
-    width: 40px;
-    height: 40px;
-    animation: spin 1s linear infinite;
-}
+    # Resim ekleme
+    try:
+        img = Image.open(image_path)
+        img_width, img_height = img.size
+        aspect = img_height / float(img_width)
+        target_width = 400
+        target_height = target_width * aspect
+        
+        if target_height > 600:
+            target_height = 600
+            target_width = target_height / aspect
+            
+        story.append(RLImage(image_path, width=target_width, height=target_height))
+        story.append(Spacer(1, 20))
+    except Exception as e:
+        print(f"PDF image error: {str(e)}")
 
-@keyframes spin {
-    0% { transform: rotate(0deg); }
-    100% { transform: rotate(360deg); }
-}
+    # Persona bilgisi
+    persona_color = PERSONAS.get(persona, {}).get("color", colors.black)
+    story.append(Paragraph(f"Persona: {persona}", ParagraphStyle(
+        'PersonaTitle',
+        fontSize=14,
+        textColor=colors.white,
+        backColor=persona_color,
+        alignment=1
+    )))
+    story.append(Spacer(1, 20))
 
-.row {
-    display: flex;
-    flex-wrap: wrap;
-    gap: 20px;
-}
+    # Analiz metni
+    analysis_style = styles['Justify']
+    for line in analysis_text.split('\n'):
+        if line.strip():
+            p = Paragraph(line.replace('•', '&#8226;'), analysis_style)
+            story.append(p)
+            story.append(Spacer(1, 12))
 
-.column {
-    flex: 1;
-    min-width: 300px;
-}
+    doc.build(story)
+    return filename
 
-@media (max-width: 768px) {
-    .row {
-        flex-direction: column;
-    }
-    
-    .column {
-        width: 100%;
-    }
-}
-"""
+def upload_to_github(file_path: str) -> bool:
+    try:
+        with open(file_path, "rb") as f:
+            content = f.read()
+        
+        file_name = os.path.basename(file_path)
+        url = f"https://api.github.com/repos/{REPO_OWNER}/{REPO_NAME}/contents/{PDF_DIR}/{file_name}"
+        
+        headers = {
+            "Authorization": f"token {GITHUB_TOKEN}",
+            "Accept": "application/vnd.github.v3+json"
+        }
+        
+        # Check if file exists
+        response = requests.get(url, headers=headers)
+        sha = response.json().get("sha") if response.status_code == 200 else None
+        
+        data = {
+            "message": f"Add report {file_name}",
+            "content": base64.b64encode(content).decode("utf-8"),
+            "branch": "main"
+        }
+        
+        if sha:
+            data["sha"] = sha
+            
+        response = requests.put(url, headers=headers, json=data)
+        return response.status_code in [200, 201]
+    except Exception as e:
+        print(f"GitHub upload error: {str(e)}")
+        return False
 
-# Loading HTML template
-loading_html = """
-<div class="loading-container">
-    <div class="loading-spinner"></div>
-    <div class="loading-text">Generating report...</div>
-</div>
+# Gradio Arayüzü
+custom_css = """
+:root { --primary-color: #2563eb; --secondary-color: #1e40af; }
+.container { max-width: 1200px; margin: 0 auto; }
+.analysis-box { background: white; border-radius: 12px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); }
+.loading-spinner { border: 4px solid #f3f3f3; border-top: 4px solid var(--primary-color); }
+@keyframes spin { 0% { transform: rotate(0deg); } 100% { transform: rotate(360deg); } }
 """
 
-# Gradio Interface
-analyzer = ChartAnalyzer()
-
 with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
-    # Header Section
+    analyzer = ChartAnalyzer()
+    
     with gr.Column(elem_classes=["container"]):
-        gr.Markdown("""
-        <div style="text-align: center; margin-bottom: 32px;">
-            <h1 style="color: var(--primary-color); font-size: 2.5rem; margin-bottom: 16px;">
-                🚀 CryptoVision Pro
-            </h1>
-            <p style="color: var(--text-color); font-size: 1.1rem;">
-                Advanced AI-powered cryptocurrency technical analysis
-            </p>
-        </div>
-        """)
+        gr.Markdown("""<div style="text-align: center;"><h1>🚀 CryptoVision Pro</h1></div>""")
         
-        # Main Content
         with gr.Row():
-            # Upload Column
-            with gr.Column(elem_classes=["column"]):
-                with gr.Column(elem_classes=["upload-box"]):
-                    gr.Markdown("### 📤 Upload Your Chart")
-                    chart_input = gr.Image(
-                        type="filepath",
-                        label="",
-                        sources=["upload"],
-                        height=300
-                    )
-                    persona_dropdown = gr.Dropdown(
-                        choices=list(PERSONAS.keys()),
-                        label="Select Trading Persona",
-                        value="Neutral Trader"
-                    )
-                    analyze_btn = gr.Button(
-                        "Analyze Chart",
-                        variant="primary",
-                        elem_classes=["primary-button"]
-                    )
-            
-            # Analysis Column
-            with gr.Column(elem_classes=["column"]):
-                with gr.Column(elem_classes=["analysis-box"]):
-                    gr.Markdown("### 📊 Analysis Report")
-                    analysis_output = gr.Markdown(
-                        label="",
-                        value="Analysis report will appear here",
-                        elem_classes=["markdown-container"]
-                    )
+            with gr.Column():
+                with gr.Box(elem_classes=["analysis-box"]):
+                    chart_input = gr.Image(type="filepath", label="Chart", sources=["upload"])
+                    persona_dropdown = gr.Dropdown(list(PERSONAS.keys()), label="Trading Persona", value="Neutral Trader")
+                    analyze_btn = gr.Button("Analyze", variant="primary")
+                    
+            with gr.Column():
+                with gr.Box(elem_classes=["analysis-box"]):
+                    analysis_output = gr.Markdown("Analysis will appear here...")
+                    pdf_status = gr.HTML()
 
-    # Event Handling
     analyze_btn.click(
-        fn=lambda: loading_html,  # Show loading spinner immediately
-        outputs=analysis_output,
+        lambda: (gr.update(visible=False), gr.update(value="<div class='loading-spinner'></div>")),
+        outputs=[analysis_output, pdf_status],
         queue=False
     ).then(
-        fn=analyzer.analyze_chart,
-        inputs=[chart_input, persona_dropdown],
-        outputs=analysis_output,
-        api_name="analyze"
+        analyzer.analyze_chart,
+        [chart_input, persona_dropdown],
+        analysis_output
+    ).then(
+        lambda img, text, persona: generate_pdf(img, text, persona),
+        [chart_input, analysis_output, persona_dropdown],
+        None
+    ).then(
+        lambda path: upload_to_github(path) if all([GITHUB_TOKEN, REPO_OWNER, REPO_NAME]) else None,
+        None,
+        pdf_status
     )
 
 if __name__ == "__main__":