Update app.py

app.py

@@ -1,53 +1,60 @@
+import os
 import gradio as gr
-import numpy as np
 import tensorflow as tf
 import spacy
-from huggingface_hub import InferenceClient
+from transformers import InferenceClient
+from jinja2 import Environment, FileSystemLoader
 
-# Load the LLaMA model
-llama_model = InferenceClient("meta-llama/Meta-Llama-3.1-8B-Instruct", 
-                               token='YOUR_HUGGING_FACE_API_KEY')
+# Load spaCy model
+nlp = spacy.load("en_core_web_sm")
 
 # Load your TensorFlow model
 model = tf.keras.models.load_model("resume_generator_model.h5")
 
-# Load spaCy model for NLP processing
-nlp = spacy.load("en_core_web_sm")
+# Initialize the InferenceClient for LLaMA
+llama_client = InferenceClient(model="meta-llama/Meta-Llama-3.1-8B-Instruct")
 
-def enhance_resume(uploaded_resume, job_title):
-    # Step 1: Extract text from the uploaded resume
-    resume_text = uploaded_resume.read().decode("utf-8")
+# Configure Jinja2 for template rendering
+env = Environment(loader=FileSystemLoader("templates"))
 
-    # Step 2: Process the resume text with spaCy
+# Helper function to enhance the resume content using spaCy and LLaMA
+def enhance_resume(resume_text, job_title):
+    # 1. Analyze the resume using spaCy
     doc = nlp(resume_text)
+    entities = [(ent.text, ent.label_) for ent in doc.ents]
     
-    # Extract entities or specific features you need for enhancement
-    skills = [ent.text for ent in doc.ents if ent.label_ == "SKILL"]
-    education = [ent.text for ent in doc.ents if ent.label_ == "EDUCATION"]
-
-    # Step 3: Generate enhancement suggestions using your TensorFlow model
-    # Assuming your model takes in the skills and job title and outputs enhancements
-    # Here, you'll need to define the actual input shape your model expects
-    # This is a simplified example:
-    resume_features = np.array([skills, job_title])
-    enhancements = model.predict(resume_features)
-
-    # Step 4: Generate enhancements using LLaMA
-    llama_response = llama_model("Suggest enhancements for a resume for the job title: {}".format(job_title))
-
-    # Step 5: Combine outputs
-    enhanced_resume = f"Original Resume:\n{resume_text}\n\nEnhancements:\n{enhancements}\n\nLLaMA Suggestions:\n{llama_response}"
-
-    return enhanced_resume
-
-# Gradio interface
+    # 2. Use LLaMA model to generate new content based on the job title
+    prompt = f"Enhance the following resume text for a {job_title} position: {resume_text}"
+    llama_response = llama_client(prompt)
+    
+    # 3. Use your custom model for additional predictions (e.g., key skills, professional summary)
+    # Here, we simulate predictions based on your model's capabilities
+    predictions = model.predict([[job_title]])  # Adjust this based on your model's input requirements
+    enhanced_skills = ["Skill A", "Skill B", "Skill C"]  # Replace with model-predicted skills
+
+    # 4. Populate the HTML template with the enhanced content
+    template = env.get_template("resume_template.html")
+    rendered_resume = template.render(
+        name="John Doe",
+        email="john.doe@example.com",
+        phone="123-456-7890",
+        location="San Francisco, CA",
+        summary=llama_response["generated_text"],
+        skills=enhanced_skills,
+        experiences=["Experience 1", "Experience 2"],
+        educations=["Bachelor's in Computer Science", "Master's in Data Science"]
+    )
+
+    return rendered_resume
+
+# Define the Gradio interface
 iface = gr.Interface(
     fn=enhance_resume,
-    inputs=[gr.inputs.File(label="Upload your resume (txt or pdf)"), 
-            gr.inputs.Textbox(label="Job Title")],
-    outputs=gr.outputs.Textbox(label="Enhanced Resume"),
+    inputs=["text", "text"],  # Upload resume text and specify job title
+    outputs="html",  # Rendered HTML output
     title="Resume Enhancer",
-    description="Upload your resume and specify the job title to get an enhanced version."
+    description="Upload a resume and specify a job title to enhance it.",
 )
 
+# Launch the Gradio app
 iface.launch()