add report generate api

a.py

@@ -0,0 +1,97 @@
+import fitz  # PyMuPDF
+import requests
+from openai import OpenAI
+from fpdf import FPDF
+import cloudinary
+import cloudinary.uploader
+
+
+def download_pdf(url, save_path):
+    """Download a PDF from a given URL and save it locally."""
+    response = requests.get(url)
+    with open(save_path, "wb") as f:
+        f.write(response.content)
+
+
+def extract_text_from_pdf(pdf_path):
+    """Extract text from a PDF file."""
+    doc = fitz.open(pdf_path)
+    text = "".join(page.get_text() for page in doc)
+    return text
+
+
+def generate_structured_summary(text):
+    client = OpenAI(
+        base_url="https://openrouter.ai/api/v1",
+        api_key="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",
+    )
+    print(text)
+    prompt = (
+        "Generate a well-structured professional report from the following text. "
+        "Include appropriate headings, subheadings, and bullet points, but do NOT include any instructions on converting the output into a PDF or formatting recommendations.Dont add conclusion"
+        f" /n/n{text}"
+    )
+
+    completion = client.chat.completions.create(
+
+        extra_body={},
+        model="deepseek/deepseek-r1:free",
+        messages=[
+            {"role": "user", "content": prompt}
+        ]
+    )
+
+    return completion.choices[0].message.content
+
+
+def save_structured_pdf(structured_text, output_pdf):
+    print(structured_text)
+    """Save the structured text into a well-formatted PDF file using FPDF."""
+    pdf = FPDF()
+    pdf.set_auto_page_break(auto=True, margin=15)
+    pdf.add_page()
+    pdf.set_font("Arial", size=12)
+
+    for line in structured_text.split("\n"):
+        print(line)
+        pdf.multi_cell(0, 10, line)
+
+    pdf.output(output_pdf)
+
+
+def upload_to_cloudinary(file_path):
+
+    cloudinary.config(
+        cloud_name="dfdu3nobj",
+        api_key="521777423999182",
+        api_secret="cYnNmTOJahnLxTz80wrzzXuvZ88"
+    )
+
+    upload_result = cloudinary.uploader.upload(
+        "requirements.txt", resource_type="raw")
+    print(upload_result)
+    return upload_result.get("secure_url")
+
+
+def main(pdf1_url, pdf2_url, output_pdf):
+    """Download, extract, summarize, and save summaries in a structured format to a PDF, then upload it."""
+    pdf1_path = "pdf1.pdf"
+    pdf2_path = "pdf2.pdf"
+
+    download_pdf(pdf1_url, pdf1_path)
+    download_pdf(pdf2_url, pdf2_path)
+
+    text1 = extract_text_from_pdf(pdf1_path)
+    text2 = extract_text_from_pdf(pdf2_path)
+
+    structured_summary1 = generate_structured_summary(text1)
+    structured_summary2 = generate_structured_summary(text2)
+
+    full_summary = f"Report 1:\n{structured_summary1}\n\nReport 2:\n{structured_summary2}"
+    save_structured_pdf(full_summary, output_pdf)
+    print(f"Structured summaries saved to {output_pdf}")
+
+    # Upload to Cloudinary
+    pdf_url = upload_to_cloudinary(output_pdf)
+    print(f"PDF uploaded to: {pdf_url}")
+    return pdf_url

app.py

@@ -19,6 +19,14 @@ import tempfile
 import os
 import numpy as np
 import requests
+import fitz  # PyMuPDF
+import requests
+from openai import OpenAI
+from fpdf import FPDF
+import cloudinary
+import cloudinary.uploader
+from PyPDF2 import PdfMerger
+from a import main
 
 # Initialize Flask app
 app = Flask(__name__)
@@ -48,15 +56,15 @@ def fetchImage():
 
     # Save the image to the current directory
     if response.status_code == 200:
-                file_name = "downloaded_image.jpg"
-            
-                image = Image.open(io.BytesIO(response.content))
-            
-                if image.mode == "RGBA":
-                    image = image.convert("RGB")
-            
-                image.save(file_name, "JPEG", quality=100)
-                print(f"Image downloaded and saved as {file_name}")
+        file_name = "downloaded_image.jpg"
+
+        image = Image.open(io.BytesIO(response.content))
+
+        if image.mode == "RGBA":
+            image = image.convert("RGB")
+
+        image.save(file_name, "JPEG", quality=100)
+        print(f"Image downloaded and saved as {file_name}")
     else:
         print(f"Failed to download image. Status code: {response.status_code}")
     # Load image
@@ -114,7 +122,7 @@ def fetchImage():
 
     # Iterate through damages
     for i in range(num_damages):
-        x1, y1, x2, y2 = coordinates[i * 4 : (i + 1) * 4]
+        x1, y1, x2, y2 = coordinates[i * 4: (i + 1) * 4]
 
         # Ensure the coordinates are within image bounds
         x1, y1 = max(0, x1), max(0, y1)
@@ -133,7 +141,8 @@ def fetchImage():
         cv2.imwrite(damage_image_path, cropped_damage)
 
         # Run the parts detection model on the cropped damage
-        result_parts = model_parts.predict(damage_image_path, confidence=15).json()
+        result_parts = model_parts.predict(
+            damage_image_path, confidence=15).json()
         detections_parts = sv.Detections.from_inference(result_parts)
 
         # Calculate repair cost for each detected part
@@ -159,7 +168,8 @@ def fetchImage():
         annotated_parts_image = part_annotator.annotate(
             scene=cropped_damage, detections=detections_parts
         )
-        annotated_parts_path = os.path.join(temp_dir, f"annotated_parts_{i}.png")
+        annotated_parts_path = os.path.join(
+            temp_dir, f"annotated_parts_{i}.png")
         cv2.imwrite(annotated_parts_path, annotated_parts_image)
 
     # Save the overall annotated image
@@ -173,5 +183,21 @@ def fetchImage():
     return jsonify(result)
 
 
+@app.route("/generate-report", methods=["POST"])
+def generate_report():
+    file = None
+    if "url" in request.form:
+        report_url = request.form["report_url"]
+        insurance_url = request.form["insurance_url"]
+        url = main(report_url, insurance_url, "output.pdf")
+        result = {"url": url}
+        return jsonify(result)
+
+    elif "file" in request.files:
+        file = request.files["file"]
+        with open("uploaded_report.pdf", "wb") as f:
+            f.write(file.read())
+
+
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=7860)

requirements.txt

@@ -16,4 +16,9 @@ scikit-image
 roboflow 
 supervision 
 opencv-python
-requests
+requests
+PyMuPDF
+openai
+fpdf
+cloudinary
+PyPDF2