Upload app.py

app.py

@@ -1,12 +1,14 @@
 import gradio as gr
 import os
-import pdfplumber
-from docx import Document
+import shutil
 from recommender import rank_resumes, summarize_resume_flan, extract_applicant_name
+from docx import Document
+import pdfplumber
 
 UPLOAD_FOLDER = "uploads"
 os.makedirs(UPLOAD_FOLDER, exist_ok=True)
 
+
 def process_resumes(job_description, uploaded_files):
     if not job_description.strip():
         return "Please provide a job description.", None
@@ -14,60 +16,44 @@ def process_resumes(job_description, uploaded_files):
     resume_texts = []
 
     for uploaded_file in uploaded_files:
-        filename = getattr(uploaded_file, "name", None)
-        if filename is None:
-            return "One of the uploaded files is missing a filename. Please upload files, not text.", None
-
+        # uploaded_file is a file path string from gr.Files
+        filename = os.path.basename(uploaded_file)
         ext = filename.lower().split(".")[-1]
 
+        # Copy the file from Gradio temp folder to your uploads folder
         file_path = os.path.join(UPLOAD_FOLDER, filename)
-        # Save uploaded file to disk
-        try:
-            with open(file_path, "wb") as f:
-                content = uploaded_file.read() if hasattr(uploaded_file, "read") else uploaded_file
-                if isinstance(content, str):
-                    content = content.encode("utf-8")
-                f.write(content)
-        except Exception as e:
-            return f"Failed to save file {filename}: {str(e)}", None
-
-        # Verify file saved correctly
-        if not os.path.exists(file_path):
-            return f"File was not saved correctly: {filename}", None
-        size = os.path.getsize(file_path)
-        print(f"Saved file {filename} size={size} bytes at {file_path}")
-        if size == 0:
-            return f"File {filename} saved but is empty!", None
-
-        # Extract text depending on file type
-        try:
-            if ext == "txt":
-                with open(file_path, "r", encoding="utf-8") as f:
-                    text = f.read()
-
-            elif ext == "pdf":
+        shutil.copy(uploaded_file, file_path)
+
+        # Read content based on extension
+        if ext == "txt":
+            with open(file_path, "r", encoding="utf-8") as f:
+                text = f.read()
+
+        elif ext == "pdf":
+            try:
                 with pdfplumber.open(file_path) as pdf:
                     pages = [page.extract_text() for page in pdf.pages if page.extract_text() is not None]
                     if not pages:
                         return f"No extractable text found in PDF: {filename}. Is it scanned or image-only?", None
                     text = "\n".join(pages)
+            except Exception as e:
+                return f"Failed to process PDF {filename}: {str(e)}", None
 
-            elif ext == "docx":
+        elif ext == "docx":
+            try:
                 doc = Document(file_path)
                 text = "\n".join([p.text for p in doc.paragraphs])
+            except Exception as e:
+                return f"Failed to process DOCX {filename}: {str(e)}", None
 
-            else:
-                return f"Unsupported file format: {filename}", None
-
-        except Exception as e:
-            return f"Failed to process {ext.upper()} {filename}: {str(e)}", None
+        else:
+            return f"Unsupported file format: {filename}", None
 
         resume_texts.append((filename, text))
 
-    # Rank resumes
+    # Rank resumes and generate summaries
     results = rank_resumes(job_description, resume_texts)
 
-    # Generate summaries
     for candidate in results:
         candidate["summary"] = summarize_resume_flan(candidate["text"], job_description)
 
@@ -82,6 +68,7 @@ def process_resumes(job_description, uploaded_files):
 
     return "", table_data
 
+
 with gr.Blocks() as demo:
     gr.Markdown("## Candidate Recommendation Engine")
     with gr.Row():
@@ -96,5 +83,6 @@ with gr.Blocks() as demo:
 
     btn.click(process_resumes, inputs=[job_desc, resumes], outputs=[msg, output_table])
 
+
 if __name__ == "__main__":
     demo.launch()