Update modules/analysis.py

modules/analysis.py

@@ -5,6 +5,111 @@ import requests
 import time
 import os
 
+#----
+import openai
+import os
+
+# Main function to combine static feedback and GPT-based feedback
+def analyze_linkedin_profile(row):
+    feedback = []
+
+    # Call the existing static feedback functions (reuse your original functions)
+    
+    # Static advice from the name and headline section
+    name_headline_feedback, name_headline_debug = clean_name_headline_section(row.get("headline", ""))
+    feedback.append(f"### 🧾 Name & Headline Feedback\n{name_headline_feedback}")
+    feedback.append(f"<details><summary>Debug</summary>\n{name_headline_debug}\n</details>")
+
+    # Static advice for the about section
+    about_feedback, about_debug = clean_about_section(row.get("about", ""))
+    feedback.append(f"### 📘 About Section\n{about_feedback}")
+    feedback.append(f"<details><summary>Debug</summary>\n{about_debug}\n</details>")
+
+    # Static advice for the experience section
+    experience_feedback, experience_debug, experience_skills = analyze_experience_section(row.get("experience", ""))
+    feedback.append(f"### 💼 Experience Section\n{experience_feedback}")
+    feedback.append(f"<details><summary>Debug</summary>\n{experience_debug}\n</details>")
+
+    # Static advice for the education section
+    education_feedback = analyze_education_section(row.get("education", ""))
+    feedback.append(f"### 🎓 Education Section\n{education_feedback}")
+
+    # Static advice for the skills section
+    skills_feedback, skills_debug = analyze_skills_section(row.get("skills", ""))
+    feedback.append(f"### 🧠 Skills Section\n{skills_feedback}")
+    feedback.append(f"<details><summary>Debug</summary>\n{skills_debug}\n</details>")
+
+    # Separator between static and dynamic feedback
+    feedback.append("\n---\n")
+
+    # Collect profile data for GPT-based analysis
+    profile_data = {
+        "fullName": row.get("fullName", ""),
+        "headline": row.get("headline", ""),
+        "about": row.get("about", ""),
+        "experience": row.get("experience", ""),
+        "education": row.get("education", ""),
+        "skills": row.get("skills", ""),
+        "career_goals": row.get("career_goals", "Data Science")  # Default career goal if not provided
+    }
+
+    # Dynamic feedback via GPT (calling `analyze_linkedin_profile_enriched()` for in-depth analysis)
+    gpt_feedback = analyze_linkedin_profile_enriched(profile_data)
+    feedback.append("### 🧠 GPT-based Suggestions:\n")
+    feedback.append(gpt_feedback)
+
+    return "\n".join(feedback)
+
+
+# Function to generate feedback using GPT (enriched suggestions for the entire profile)
+def analyze_linkedin_profile_enriched(profile_data):
+    openai.api_key = os.getenv("OPENAI_API_KEY")  # Ensure API key is securely loaded
+
+    # Prepare GPT prompt
+    prompt = f"""
+    Here is the LinkedIn profile data for {profile_data['fullName']}:
+    - Current Headline: {profile_data['headline']}
+    - Career Goals: {profile_data['career_goals']}
+    - About: {profile_data['about']}
+    - Experience: {profile_data['experience']}
+    - Education: {profile_data['education']}
+    - Skills: {profile_data['skills']}
+    
+    Based on this, generate the following:
+    1. Suggest 3 ways to improve the headline, focusing on including keywords, making it more compelling, and aligning with the user's career goals.
+    2. Provide feedback on how the 'About' section can be expanded for better engagement.
+    3. Review the experience section and suggest how to reword it for stronger impact, including achievements.
+    4. Recommend adding missing skills or certifications.
+    """
+
+    # Request GPT for feedback
+    response = openai.Completion.create(
+        engine="text-davinci-003",  # Can use GPT-4 if available
+        prompt=prompt,
+        max_tokens=500,
+        temperature=0.7
+    )
+
+    # Return GPT-generated feedback
+    return response.choices[0].text.strip()
+
+
+# Example usage with a mock profile
+example_profile = {
+    "fullName": "John Doe",
+    "headline": "Software Engineer at XYZ",
+    "about": "Passionate about software development, machine learning, and data science.",
+    "experience": "Software Engineer at XYZ Company",
+    "education": "BSc in Computer Science from University Y",
+    "skills": "Python, Java, C++",
+    "career_goals": "Data Scientist specializing in machine learning"
+}
+
+profile_analysis = analyze_linkedin_profile(example_profile)
+print(profile_analysis)
+
+
+#------------
 
 def render_text_roadmap(goal, steps, completed_tasks=None):
     if completed_tasks is None: