Update app.py

app.py

@@ -2,7 +2,12 @@ import gradio as gr
 import pdfplumber
 import re
 import openpyxl
-from transformers import pipeline
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+
+# Authenticate Hugging Face API (ensure you're logged in already)
+model_name = "meta-llama/Llama-3.1-70B-Instruct"  # Replace with your actual model name
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
 # Function to extract text from PDF
 def extract_text_from_pdf(pdf_path):
@@ -12,10 +17,6 @@ def extract_text_from_pdf(pdf_path):
             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
@@ -29,22 +30,24 @@ def parse_resume(text):
     results = {}
 
     for key, prompt in prompts.items():
-        # Get the model response for each field
-        response = nlp(prompt + text)
-        
+        # Generate model response for each field
+        inputs = tokenizer(prompt + text, return_tensors="pt")
+        outputs = model.generate(**inputs, max_length=500)
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
         if key == 'email':
             # Use regex to validate email format
-            email = re.findall(r'\S+@\S+', response[0]['generated_text'])
+            email = re.findall(r'\S+@\S+', response)
             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'])
+            phone = re.findall(r'\b\d{10,15}\b', response)
             results[key] = phone[0] if phone else None
         elif key == 'skills':
             # Extract technical skills
-            results[key] = response[0]['generated_text']
+            results[key] = response
         else:
-            results[key] = response[0]['generated_text']
+            results[key] = response
     
     return results
 
@@ -79,12 +82,15 @@ def process_pdfs(pdfs):
 
     return output_file
 
-# Gradio interface setup
+# Gradio interface setup with blank API space (Hugging Face integration)
 iface = gr.Interface(
     fn=process_pdfs,
     inputs=gr.File(file_count="multiple", type="file"),
     outputs=gr.File(),
-    live=True
+    live=True,
+    title="AI Resume Parser",
+    description="Upload PDF resumes, and the app will parse and extract Name, Email, Phone, and Skills from them.",
+    examples=[["path_to_sample_resume.pdf"]]  # Provide sample files if necessary
 )
 
 # Launch the Gradio app