Update app.py

app.py

@@ -1,10 +1,114 @@
 import gradio as gr
+import PyPDF2
+import docx
+from io import BytesIO
+import json
+from mistral_inference import MistralInference  # Hypothetical API wrapper for Mistral model
 
-with gr.Blocks(fill_height=True) as demo:
+# Function to extract text from PDF
+def extract_text_from_pdf(file):
+    pdf_reader = PyPDF2.PdfReader(file)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+# Function to extract text from Word document
+def extract_text_from_docx(file):
+    doc = docx.Document(file)
+    text = "\n".join([para.text for para in doc.paragraphs])
+    return text
+
+# Function to process uploaded file based on type
+def process_uploaded_file(file):
+    if file.name.endswith(".pdf"):
+        return extract_text_from_pdf(file)
+    elif file.name.endswith(".docx"):
+        return extract_text_from_docx(file)
+    else:
+        raise ValueError("Unsupported file format. Please upload a PDF or Word document.")
+
+# Hypothetical Mistral API wrapper (replace with actual API call)
+class MistralInference:
+    def __init__(self, model="mistralai/Mistral-7B-Instruct-v0.3", provider="together"):
+        self.model = model
+        self.provider = provider
+
+    def analyze(self, resume_text, job_description):
+        # Simulated prompt to Mistral model
+        prompt = f"""
+        Analyze the following resume against the job description for ATS compatibility.
+        Provide a detailed breakdown of ATS parameters (keywords, formatting, skills match, experience relevance, education) 
+        and assign a score out of 100 for each, along with an overall score. Return the result in JSON format.
+
+        Resume:
+        {resume_text}
+
+        Job Description:
+        {job_description}
+        """
+        
+        # Simulated response (replace with actual API call)
+        response = {
+            "ats_analysis": {
+                "keywords": {
+                    "score": 85,
+                    "details": "Key terms like 'Python', 'Machine Learning' found, missing 'AWS'."
+                },
+                "formatting": {
+                    "score": 90,
+                    "details": "Simple layout, readable by ATS, no complex tables."
+                },
+                "skills_match": {
+                    "score": 80,
+                    "details": "Strong match for programming skills, lacks cloud skills."
+                },
+                "experience_relevance": {
+                    "score": 75,
+                    "details": "Relevant experience in data science, but duration slightly short."
+                },
+                "education": {
+                    "score": 95,
+                    "details": "Matches required degree in Computer Science."
+                },
+                "overall_score": 85
+            }
+        }
+        return json.dumps(response, indent=2)
+
+# Main function to analyze resume
+def analyze_resume(file, job_description):
+    try:
+        resume_text = process_uploaded_file(file)
+        mistral = MistralInference()
+        result = mistral.analyze(resume_text, job_description)
+        return result
+    except Exception as e:
+        return json.dumps({"error": str(e)}, indent=2)
+
+# Gradio interface
+with gr.Blocks(fill_height=True, title="Smart ATS Resume Analyzer") as demo:
     with gr.Sidebar():
-        gr.Markdown("# Inference Provider")
-        gr.Markdown("This Space showcases the mistralai/Mistral-7B-Instruct-v0.3 model, served by the together API. Sign in with your Hugging Face account to use this API.")
-        button = gr.LoginButton("Sign in")
+        gr.Markdown("# Smart ATS Resume Analyzer")
+        gr.Markdown("Upload your resume (PDF/Word) and enter a job description to get an ATS compatibility score.")
+        button = gr.LoginButton("Sign in with Hugging Face")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            resume_upload = gr.File(label="Upload Resume (PDF or Word)", file_types=[".pdf", ".docx"])
+            job_desc = gr.Textbox(label="Job Description", lines=10, placeholder="Paste the job description here...")
+            submit_btn = gr.Button("Analyze Resume")
+        with gr.Column(scale=2):
+            output = gr.JSON(label="ATS Analysis Result")
+
+    # Connect the button to the analysis function
+    submit_btn.click(
+        fn=analyze_resume,
+        inputs=[resume_upload, job_desc],
+        outputs=output
+    )
+
+    # Load Mistral model (hypothetical, adjust based on actual integration)
     gr.load("models/mistralai/Mistral-7B-Instruct-v0.3", accept_token=button, provider="together")
-    
+
 demo.launch()