Update app.py

app.py

@@ -21,6 +21,9 @@ SF_SECURITY_TOKEN = os.getenv('SF_SECURITY_TOKEN')
 # Initialize Salesforce connection
 sf = Salesforce(username=SF_USERNAME, password=SF_PASSWORD, security_token=SF_SECURITY_TOKEN)
 
+print(f"SF_USERNAME: {SF_USERNAME}, SF_PASSWORD: {SF_PASSWORD}, SF_SECURITY_TOKEN: {SF_SECURITY_TOKEN}")
+print("Salesforce Base URL:", sf.base_url)
+
 # Load BLIP model and processor
 processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
 model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
@@ -28,28 +31,35 @@ model.eval()
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model.to(device)
 
-# Function to generate image captions
+# Inference function to generate captions dynamically based on image content
 def generate_captions_from_image(image):
     if image.mode != "RGB":
         image = image.convert("RGB")
-
+    
+    # Preprocess the image and generate a caption
     inputs = processor(image, return_tensors="pt").to(device, torch.float16)
     output = model.generate(**inputs, max_new_tokens=50)
     caption = processor.decode(output[0], skip_special_tokens=True)
-
+    
     return caption
+    
 
 # Function to create PDF and upload to Salesforce
 def create_and_upload_pdf(dpr_content):
+    # Create PDF instance using FPDF
     pdf = FPDF()
     pdf.set_auto_page_break(auto=True, margin=15)
     pdf.add_page()
+
+    # Set title and content for the PDF
     pdf.set_font("Arial", size=12)
     pdf.cell(200, 10, txt="Daily Progress Report", ln=True, align='C')
-    pdf.ln(10)
+    pdf.ln(10)  # Add space between lines
+
+    # Add the content of the DPR text to the PDF
     pdf.multi_cell(0, 10, dpr_content)
 
-    # Save PDF to file
+    # Save PDF to a file (temporary storage)
     pdf_output_path = "/tmp/dpr_report.pdf"
     pdf.output(pdf_output_path)
 
@@ -57,74 +67,98 @@ def create_and_upload_pdf(dpr_content):
     with open(pdf_output_path, 'rb') as pdf_file:
         pdf_data = pdf_file.read()
 
-    # First, create metadata record
+    # Prepare request to Salesforce ContentVersion API
     content_version_payload = {
         "Title": "Daily Progress Report",
-        "PathOnClient": "DPR_Report.pdf"
+        "PathOnClient": "DPR_Report.pdf",
     }
-    
-    content_version_url = f"https://{sf.sf_instance}/services/data/v59.0/sobjects/ContentVersion/"
+
+    content_version_url = f"https://sathkruthatechsolutionspvt6-dev-ed.develop.my.salesforce.com/services/data/v59.0/sobjects/ContentVersion/"
     headers = {
-        "Authorization": f"Bearer {sf.session_id}"
+        "Authorization": f"Bearer {sf.session_id}",
+        "Content-Type": "application/json"  # For metadata
     }
 
-    # Create ContentVersion record metadata
-    metadata_response = requests.post(content_version_url, headers=headers, json=content_version_payload)
-    if metadata_response.status_code != 201:
-        raise Exception("Metadata creation error: " + metadata_response.text)
-    
-    content_version_id = metadata_response.json()["Id"]
-
-    # Upload file separately
+    # Pass the binary data in the files parameter (do not include it in the JSON payload)
     files = {
-        "VersionData": ("DPR_Report.pdf", pdf_data, "application/pdf")
+        "VersionData": ("DPR_Report.pdf", pdf_data)  # Tuple (filename, binary data)
     }
-    upload_response = requests.post(content_version_url, headers=headers, files=files)
-    
-    if upload_response.status_code == 201:
-        return f"/sfc/servlet.shepherd/version/download/{content_version_id}"
+
+    # Make the POST request to upload the file
+    response = requests.post(content_version_url, headers=headers, files=files)
+
+    # Check the response status
+    if response.status_code == 201:
+        content_version = response.json()
+        pdf_url = f"/sfc/servlet.shepherd/version/download/{content_version['Id']}"
+        return pdf_url
     else:
-        raise Exception("Error uploading PDF: " + upload_response.text)
+        raise Exception("Error uploading PDF to Salesforce: " + response.text)
 
-# Function to generate the daily progress report
+# Function to generate the daily progress report (DPR) text
 def generate_dpr(files):
+    # Query the latest Daily Progress Report records to get the highest number
     last_report = sf.query("SELECT Name FROM Daily_Progress_Reports__c ORDER BY CreatedDate DESC LIMIT 1")
-    report_number = int(last_report['records'][0]['Name'].split()[-1]) + 1 if last_report['totalSize'] > 0 else 1
+    
+    # Generate the new report name
+    if last_report['totalSize'] > 0:
+        last_name = last_report['records'][0]['Name']
+        # Extract the number from the last report name, assuming format "Daily Progress Report X"
+        report_number = int(last_name.split()[-1]) + 1  # Increment the number
+    else:
+        # If no records exist, start with "1"
+        report_number = 1
+    
+    # Generate the new report name dynamically
     new_report_name = f"Daily Progress Report {report_number}"
 
+    # Prepare the current time and start the DPR text
     current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
     dpr_text = [f"Daily Progress Report\nGenerated on: {current_time}\n"]
 
+    # Process each uploaded file (image)
     for file in files:
-        image = Image.open(file.name)
+        # Open the image from the file path
+        image = Image.open(file.name)  # Using file.name for filepath
+        
         if image.mode != "RGB":
             image = image.convert("RGB")
+        
+        # Generate a caption for the image
         caption = generate_captions_from_image(image)
+        
+        # Generate a section of the DPR for this image
         dpr_section = f"\nImage: {file.name}\nDescription: {caption}\n"
         dpr_text.append(dpr_section)
 
+    # Join the DPR sections into one complete string
     dpr_content = "\n".join(dpr_text)
+
+    # Create and upload the PDF for this report to Salesforce
     pdf_url = create_and_upload_pdf(dpr_content)
 
+    # Create the new Daily Progress Report record in Salesforce
     new_report_data = {
-        "Name": new_report_name,
-        "Report_Date__c": current_time,
-        "Detected_Activities__c": dpr_content,
-        "Photo_Uploads__c": ", ".join([file.name for file in files]),
-        "PDF_URL__c": pdf_url
+        "Name": new_report_name,  # Set the dynamic name
+        "Report_Date__c": current_time,  # Set the report date to the current time
+        "Detected_Activities__c": dpr_content,  # Set the activities field to the generated text
+        "Photo_Uploads__c": ", ".join([file.name for file in files]),  # Upload the image names as Photo Uploads
+        "PDF_URL__c": pdf_url  # Set the PDF URL field with the generated PDF link
     }
 
-    sf.Daily_Progress_Reports__c.create(new_report_data)
+    # Insert the new report into Salesforce
+    new_report = sf.Daily_Progress_Reports__c.create(new_report_data)
+
     return f"New report created in Salesforce with PDF URL: {pdf_url}"
 
-# Gradio interface for uploading site images
+# Gradio interface for uploading multiple files and displaying the text-based DPR
 iface = gr.Interface(
     fn=generate_dpr,
-    inputs=gr.Files(type="filepath", label="Upload Site Photos"),
-    outputs="text",
+    inputs=gr.Files(type="filepath", label="Upload Site Photos"),  # Handle batch upload of images
+    outputs="text",  # Display the DPR as text in the output section
     title="Daily Progress Report Generator",
-    description="Upload up to 10 site photos. The AI model detects construction activities and generates a report.",
-    allow_flagging="never"
+    description="Upload up to 10 site photos. The AI model will dynamically detect construction activities, materials, and progress and generate a text-based Daily Progress Report (DPR).",
+    allow_flagging="never"  # Optional: Disable flagging
 )
 
-iface.launch()
+iface.launch()