Create app.py

app.py

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+# app.py
+
+import gradio as gr
+import requests
+
+# Replace 'YOUR_GEMINI_API_KEY' with your actual Gemini API key.
+GEMINI_API_KEY = "AIzaSyB05WLxtH1x4fMvB87M-GggjQlBnm3YWeE"
+
+# This function sends the resume and job description to the Gemini API
+# and returns an optimized resume tailored to the job description.
+def optimize_resume(resume, job_description):
+    # Gemini API endpoint for text generation (update if needed)
+    url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent"
+    
+    # Prepare the prompt for the AI model
+    prompt = (
+        "You are a professional resume writer. "
+        "Given the following resume and job description, rewrite the resume to be optimized for the job. "
+        "Keep the formatting clear and professional. "
+        "Only output the improved resume.\n\n"
+        f"Resume:\n{resume}\n\n"
+        f"Job Description:\n{job_description}\n"
+    )
+    
+    # Prepare the request payload for Gemini API
+    data = {
+        "contents": [
+            {
+                "parts": [
+                    {"text": prompt}
+                ]
+            }
+        ]
+    }
+    
+    # Set the headers, including the API key for authentication
+    headers = {
+        "Content-Type": "application/json",
+        "x-goog-api-key": GEMINI_API_KEY
+    }
+    
+    # Send the POST request to Gemini API
+    response = requests.post(url, headers=headers, json=data)
+    
+    # If the request is successful, extract and return the optimized resume
+    if response.status_code == 200:
+        result = response.json()
+        # Extract the generated text from the response
+        try:
+            optimized_resume = result["candidates"][0]["content"]["parts"][0]["text"]
+            return optimized_resume
+        except (KeyError, IndexError):
+            return "Error: Unexpected response format from Gemini API."
+    else:
+        # If there's an error, return the error message
+        return f"Error: {response.status_code} - {response.text}"
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # AI Resume Optimizer
+        Paste your resume and the job description below.  
+        The AI will rewrite your resume to better match the job!
+        """
+    )
+    
+    # Input for the user's resume
+    resume_input = gr.Textbox(
+        label="Paste your Resume",
+        lines=15,
+        placeholder="Paste your resume here..."
+    )
+    
+    # Input for the job description
+    job_desc_input = gr.Textbox(
+        label="Paste the Job Description",
+        lines=10,
+        placeholder="Paste the job description here..."
+    )
+    
+    # Output box for the optimized resume
+    optimized_resume_output = gr.Textbox(
+        label="Optimized Resume",
+        lines=15
+    )
+    
+    # Button to submit the inputs and get the optimized resume
+    submit_btn = gr.Button("Optimize Resume")
+    
+    # When the button is clicked, call the optimize_resume function
+    submit_btn.click(
+        fn=optimize_resume,
+        inputs=[resume_input, job_desc_input],
+        outputs=optimized_resume_output
+    )
+
+# Run the Gradio app
+if __name__ == "__main__":
+    demo.launch()