VIZSUM PRO REAL DEPLOY — Claude 3, Emoji Yok, PDF Fix

.env.example

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+OPENROUTER_API_KEY=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

.gitignore

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+*.png
+*.jpg
+*.jpeg
+*.mp3
+*.mp4
+*.pt
+*.ckpt
+*.bin
+__pycache__/
+*.py[cod]
+.env

.gradio/certificate.pem

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+-----END CERTIFICATE-----

README.md

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+---
+title: VizSum Pro
+emoji: 📄
+colorFrom: purple
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.19.2
+app_file: app.py
+pinned: false
+---
+
+
+# VizSum Pro
+
+AI-powered summarizer that supports images, PDFs, and manual text.
+
+- 🔍 OCR (Tesseract) for image text extraction  
+- 📄 PDF parser  
+- ✍️ Manual input  
+- 🎯 Mode selector for different summary styles
+
+Built with ❤️ using Python + Gradio.

app.py

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+import ui
+ui.demo.launch()

ocr_engine.py

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+import requests
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+ocr_api_key = os.getenv("OCR_SPACE_API_KEY")
+
+def extract_text_from_image(image_path):
+    try:
+        if not isinstance(image_path, str) or not os.path.exists(image_path):
+            return "❌ Geçersiz dosya yolu."
+
+        with open(image_path, 'rb') as image_file:
+            image_data = image_file.read()
+
+        response = requests.post(
+            url='https://api.ocr.space/parse/image',
+            files={'file': ('image.png', image_data)},
+            data={
+                'apikey': ocr_api_key.strip(),
+                'language': 'eng',
+                'isOverlayRequired': False,
+                'OCREngine': 2
+            }
+        )
+
+        # JSON dönüşüm kontrolü
+        try:
+            result = response.json()
+        except Exception:
+            return f"❌ API yanıtı JSON formatında değil:\n{response.text}"
+
+        if not isinstance(result, dict):
+            return f"❌ API çıktısı sözlük değil:\n{result}"
+
+        if result.get("IsErroredOnProcessing"):
+            return f"❌ OCR Hatası: {result.get('ErrorMessage', ['Bilinmeyen hata'])[0]}"
+
+        parsed_results = result.get("ParsedResults")
+        if not parsed_results or not isinstance(parsed_results, list):
+            return "❌ OCR sonucu boş veya biçimsiz."
+
+        return parsed_results[0].get('ParsedText', '').strip()
+
+    except Exception as e:
+        return f"❌ Sistemsel Hata: {str(e)}"

pdf_reader.py

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+import fitz  # PyMuPDF
+
+def extract_text_from_pdf(pdf_input):
+    try:
+        if isinstance(pdf_input, str):
+            doc = fitz.open(pdf_input)
+        else:
+            doc = fitz.open(stream=pdf_input.read(), filetype="pdf")
+
+        text = "\n".join([page.get_text() for page in doc])
+        doc.close()
+        return text
+
+    except Exception as e:
+        return f"❌ PDF İşleme Hatası: {str(e)}"

requirements.txt

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+gradio
+pytesseract
+python-dotenv
+requests
+PyMuPDF
+Pillow

summarizer.py

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+import os
+import requests
+from dotenv import load_dotenv
+
+load_dotenv()
+api_key = os.getenv("OPENROUTER_API_KEY").strip()
+
+
+def build_prompt(text, mode):
+    if "Sade" in mode:
+        instruction = "Bu metni herkesin anlayabileceği şekilde sadeleştir."
+    elif "Eleştir" in mode:
+        instruction = "Metni eleştir, eksiklerini ve güçlü yönlerini değerlendir."
+    elif "Başlık" in mode:
+        instruction = "Metne uygun birkaç başlık öner."
+    elif "Not" in mode:
+        instruction = "Metinden önemli notlar çıkar."
+    else:
+        instruction = "Kısa ve teknik bir özet ver."
+
+    return f"{instruction}\n\nMetin:\n{text}"
+
+def summarize_text(text, mode):
+    url = "https://openrouter.ai/api/v1/chat/completions"
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "Content-Type": "application/json"
+    }
+
+    data = {
+        "model": "openai/gpt-3.5-turbo",
+        "messages": [
+            {"role": "user", "content": build_prompt(text, mode)}
+        ]
+    }
+
+    response = requests.post(url, headers=headers, json=data)
+    response.raise_for_status()
+    return response.json()['choices'][0]['message']['content']

summarizer_test.py

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+from summarizer import summarize_text
+
+text = "Tokenization, metni daha küçük parçalara ayırma işlemidir."
+mode = "📘 Teknik Özet"  # Varsayılan modlardan biri
+
+print(summarize_text(text, mode))

tests/notepad_test_ocr.py

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+from ocr_engine import extract_text_from_image
+
+print(extract_text_from_image("c:/Users/sinan/Desktop/vizsum-pro/test.png"))

tests/python_test_key.py

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+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+key = os.getenv("OCR_SPACE_API_KEY")
+print(f"KEY: '{key}'")

tests/summarizer_test.py

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+from summarizer import summarize_text
+
+text = "Tokenization, metni daha küçük parçalara ayırma işlemidir."
+mode = "📘 Teknik Özet"  # Varsayılan modlardan biri
+
+print(summarize_text(text, mode))

ui.py

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+import gradio as gr
+from ocr_engine import extract_text_from_image
+from pdf_reader import extract_text_from_pdf
+from summarizer import summarize_text
+
+def process_input(pdf, image, manual_text, mode):
+    if pdf is not None:
+        text = extract_text_from_pdf(pdf)
+    elif image is not None:
+        text = extract_text_from_image(image)
+    elif manual_text.strip() != "":
+        text = manual_text
+    else:
+        return "Lütfen bir giriş türü seçin.", ""
+
+
+    summary = summarize_text(text, mode)
+    return text, summary
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 📚 VizSum Pro+: AI Destekli Özetleyici")
+
+    with gr.Row():
+        pdf_input = gr.File(label="📄 PDF Yükle", file_types=[".pdf"])
+        image_input = gr.Image(type="filepath", label="🖼️ Görsel Yükle")
+
+    manual_input = gr.Textbox(lines=5, label="✍️ Metni Manuel Gir")
+
+    # BURAYA AL
+    mode_selector = gr.Dropdown(
+        choices=["📘 Teknik Özet", "🧒 Sade Anlatım", "🧠 Eleştir ve Değerlendir", "📝 Başlık Çıkar", "📎 Not Formatı"],
+        label="🧩 Özetleme Modu",
+        value="📘 Teknik Özet"
+    )
+
+    with gr.Row():
+        submit_btn = gr.Button("Özetle")
+
+    with gr.Row():
+        text_output = gr.Textbox(label="📜 Metin")
+        summary_output = gr.Textbox(label="🧠 AI Özeti")
+
+    # EN SONDA KALACAK
+    submit_btn.click(
+        fn=process_input,
+        inputs=[pdf_input, image_input, manual_input, mode_selector],
+        outputs=[text_output, summary_output]
+    )
+
+
+
+if __name__ == "__main__":
+    demo.launch(share=True)