Create app.py

app.py

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+import gradio as gr
+import pdfplumber
+import re
+import openpyxl
+from transformers import pipeline
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_path):
+    with pdfplumber.open(pdf_path) as pdf:
+        text = ''
+        for page in pdf.pages:
+            text += page.extract_text()
+    return text
+
+# Load Llama model for parsing (replace with actual model path if available)
+model_name = "meta-llama/Llama-3.1-70B-Instruct"  # Replace with the model path or identifier
+nlp = pipeline("text2text-generation", model=model_name)
+
+# Function to parse the resume text for name, email, phone, and skills
+def parse_resume(text):
+    # Define the prompts for each type of information
+    prompts = {
+        "name": "Extract the name from this resume:\n",
+        "email": "Extract the email address from this resume:\n",
+        "phone": "Extract the phone number from this resume:\n",
+        "skills": "Extract the technical skills from this resume:\n"
+    }
+
+    results = {}
+
+    for key, prompt in prompts.items():
+        # Get the model response for each field
+        response = nlp(prompt + text)
+        
+        if key == 'email':
+            # Use regex to validate email format
+            email = re.findall(r'\S+@\S+', response[0]['generated_text'])
+            results[key] = email[0] if email else None
+        elif key == 'phone':
+            # Use regex to validate phone number format
+            phone = re.findall(r'\b\d{10,15}\b', response[0]['generated_text'])
+            results[key] = phone[0] if phone else None
+        elif key == 'skills':
+            # Extract technical skills
+            results[key] = response[0]['generated_text']
+        else:
+            results[key] = response[0]['generated_text']
+    
+    return results
+
+# Function to save parsed data to Excel file
+def save_to_excel(parsed_data, output_file):
+    wb = openpyxl.Workbook()
+    ws = wb.active
+    ws.append(["Name", "Email", "Phone", "Skills"])
+
+    for data in parsed_data:
+        ws.append([data["name"], data["email"], data["phone"], data["skills"]])
+    
+    wb.save(output_file)
+
+# Function to process PDF files and output an Excel file
+def process_pdfs(pdfs):
+    parsed_data = []
+    
+    for pdf in pdfs:
+        # Extract text from the PDF
+        text = extract_text_from_pdf(pdf.name)
+        
+        # Parse the text for relevant details
+        parsed_info = parse_resume(text)
+        
+        # Add parsed information to the list
+        parsed_data.append(parsed_info)
+
+    # Save the parsed data to an Excel file
+    output_file = "parsed_resumes.xlsx"
+    save_to_excel(parsed_data, output_file)
+
+    return output_file
+
+# Gradio interface setup
+iface = gr.Interface(
+    fn=process_pdfs,
+    inputs=gr.File(file_count="multiple", type="file"),
+    outputs=gr.File(),
+    live=True
+)
+
+# Launch the Gradio app
+iface.launch()