Update app.py

app.py

@@ -2,20 +2,25 @@ import gradio as gr
 import pandas as pd
 import json
 import os
+import requests
 import re 
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
 from functools import lru_cache
 
 # ----------------------------
 # CONFIG
 # ----------------------------
 JSON_FILE = "form-submissions-1.json"
+# 🚩 CHANGE: Switched to a more capable, instruction-tuned model for semantic matching
+MODEL_ID = "google/flan-t5-large" 
+# NOTE: HF_API_TOKEN MUST be set in your environment variables/Space secrets.
+HF_API_TOKEN = os.environ.get("HF_API_TOKEN") 
 FILTERED_CSV = "/tmp/filtered_candidates.csv"
+OUTPUT_FILE = "/tmp/outputs.csv"
+BATCH_SIZE = 50 
 
-# Note: The following variables are no longer needed, as we are not using the Hugging Face API
-# MODEL_ID = "google/flan-t5-small" 
-# HF_API_TOKEN = os.environ.get("HF_API_TOKEN") 
+if not HF_API_TOKEN:
+    # Allow launch for demonstration, but function will warn if token is missing
+    pass 
 
 CATEGORIES = {
     "AI": [
@@ -32,38 +37,60 @@ CATEGORIES = {
 }
 
 # ----------------------------
-# Similarity Matching Function (REPLACING LLM)
+# LLM Call for Semantic Role Scoring
 # ----------------------------
-@lru_cache(maxsize=1) 
-def calculate_similarity_scores(df_candidates, category_name):
-    """
-    Calculates the cosine similarity between candidate roles and target job titles.
-    """
-    if df_candidates.empty:
-        return pd.Series([], dtype='float64')
-
-    # 1. Define the document corpus
-    target_roles = " ".join(CATEGORIES[category_name])
-    candidate_roles = df_candidates['Roles'].tolist()
+@lru_cache(maxsize=512) 
+def score_candidate(candidate_str, category_name, job_titles_tuple):
+    if not HF_API_TOKEN:
+         print("API Token is missing. Returning score 0.")
+         return 0
+         
+    # 🚩 PROMPT CHANGE: Focus on 'semantic relevance' and 'conceptual fit'
+    prompt = f"""
+You are an HR expert performing semantic matching. Your task is to rate a candidate's conceptual fit based ONLY on their previous job roles and the target roles.
+Rate the semantic relevance of the candidate's 'Roles' to the 'Target Roles' on a scale of 1 (Lowest Match) to 10 (Highest Semantic Match).
+The score must reflect the conceptual alignment and industry similarity, not just keyword presence.
+
+The target roles for the '{category_name}' category are: {list(job_titles_tuple)}
+
+Candidate JSON: {candidate_str}
+
+**Task**: Respond ONLY with the rating number (an integer from 1 to 10).
+"""
+    headers = {"Authorization": f"Bearer {HF_API_TOKEN}", "Content-Type": "application/json"}
     
-    # 2. Create the corpus for vectorization
-    corpus = [target_roles] + candidate_roles
+    payload = {
+        "inputs": prompt,
+        "parameters": {
+            "max_new_tokens": 5, 
+            "return_full_text": False,
+            "temperature": 0.1 
+        }
+    }
     
-    # 3. Vectorize using TF-IDF (converts text to numerical features)
-    # Uses unigrams and bigrams for better matching (e.g., 'Data Scientist' vs 'Data' 'Scientist')
-    vectorizer = TfidfVectorizer(ngram_range=(1, 2), stop_words='english')
-    tfidf_matrix = vectorizer.fit_transform(corpus)
-    
-    # 4. Extract the vector for the target roles (the first row)
-    target_vector = tfidf_matrix[0]
-    candidate_vectors = tfidf_matrix[1:]
-    
-    # 5. Calculate Cosine Similarity between the target and all candidates
-    # The result is a matrix where [0, i] is the similarity score for candidate i
-    similarity_scores = cosine_similarity(target_vector, candidate_vectors).flatten()
-    
-    # Return scores as a Pandas Series aligned with the DataFrame index
-    return pd.Series(similarity_scores, index=df_candidates.index)
+    try:
+        # Note: Flan-T5-Large is slower than small, but more powerful for this task
+        response = requests.post(
+            f"https://api-inference.huggingface.co/models/{MODEL_ID}",
+            headers=headers,
+            data=json.dumps(payload),
+            timeout=120 # Increased timeout for the larger model
+        )
+        response.raise_for_status()
+        result = response.json()
+             
+        generated_text = result[0].get("generated_text", "0").strip()
+        
+        match = re.search(r'\d+', generated_text)
+        if match:
+            score = int(match.group(0))
+            return max(1, min(10, score)) 
+        
+        return 0 
+            
+    except Exception as e:
+        print(f"LLM scoring call failed for candidate (API/Network Error): {e}")
+        return 0 
 
 # ----------------------------
 # Step 1: Filter by roles (Unchanged)
@@ -106,13 +133,15 @@ def filter_by_roles(category_name):
 
     df = pd.DataFrame(filtered)
     df.to_csv(FILTERED_CSV, index=False)
-    return df, f"{len(df)} candidates filtered by role for category '{category_name}'. Ready for Similarity Ranking."
+    return df, f"{len(df)} candidates filtered by role for category '{category_name}'. Ready for LLM Semantic Scoring."
 
 
 # ----------------------------
-# Step 2: Recommendations (Using Similarity Matching)
+# Step 2: LLM recommendations (Semantic Scoring, Sorting, and Output)
 # ----------------------------
-def similarity_recommendations(category_name):
+def llm_recommendations(category_name):
+    job_titles = CATEGORIES[category_name]
+
     if not os.path.exists(FILTERED_CSV):
         df_filtered, msg = filter_by_roles(category_name)
         if df_filtered.empty:
@@ -124,16 +153,35 @@ def similarity_recommendations(category_name):
     if df_filtered.empty:
         return f"No filtered candidates found for category '{category_name}'. Run Step 1 first."
 
-    # --- CORE CHANGE: Calculate Similarity Scores ---
-    df_filtered["Similarity_Score"] = calculate_similarity_scores(df_filtered, category_name)
+    df_filtered_clean = df_filtered.fillna('N/A')
+    filtered_candidates = df_filtered_clean.to_dict(orient="records")
     
-    # Filter out candidates with zero relevance (can happen if roles are too generic)
-    df_recommended = df_filtered[df_filtered["Similarity_Score"] > 0].copy()
+    scores = []
+    
+    for person in filtered_candidates:
+        candidate_info = {
+            "Name": person.get("Name"),
+            "Roles": person.get("Roles"),
+            "Skills": person.get("Skills") 
+        }
+        candidate_str = json.dumps(candidate_info)
+        
+        score = score_candidate(candidate_str, category_name, tuple(job_titles))
+        scores.append(score)
+
+    df_filtered["LLM_Score"] = scores
+    
+    # Only filter out scores of 0 if the token is present (0 means total irrelevance if token works)
+    if HF_API_TOKEN:
+        df_recommended = df_filtered[df_filtered["LLM_Score"] > 0].copy()
+    else:
+        df_recommended = df_filtered.copy() # Can't filter if all are 0 due to no token
 
     if df_recommended.empty:
-        return f"All candidates had zero text similarity to the target roles for '{category_name}'. The current roles are not specific enough to match."
+        if not HF_API_TOKEN:
+            return "❌ LLM failed: The HF_API_TOKEN is not set or is invalid. Set the token and try again."
+        return f"LLM scored all candidates 0. This indicates zero semantic relevance between the candidates' roles and the target roles for '{category_name}'."
     
-    # Define salary parsing for tie-breaker
     def parse_salary(s):
         try:
             return float(str(s).replace("$","").replace(",","").replace("N/A", str(float('inf'))))
@@ -142,9 +190,8 @@ def similarity_recommendations(category_name):
             
     df_recommended["Salary_sort"] = df_recommended["Salary"].apply(parse_salary)
 
-    # Sort: 1. Highest Similarity Score (descending), 2. Lowest Salary (ascending)
     df_top5 = df_recommended.sort_values(
-        by=['Similarity_Score', 'Salary_sort'],
+        by=['LLM_Score', 'Salary_sort'],
         ascending=[False, True] 
     ).head(5)
 
@@ -153,12 +200,10 @@ def similarity_recommendations(category_name):
     output_text = f"Top {len(final_names)} Recommended Candidates for the '{category_name}' Category:\n\n"
     
     for i, name in enumerate(final_names):
-        score = df_top5.iloc[i]['Similarity_Score']
-        # Display the score as a percentage for readability
-        score_percent = f"{score * 100:.2f}%"
-        output_text += f"{i+1}. {name} (Role Match: {score_percent})\n"
+        score = df_top5.iloc[i]['LLM_Score']
+        output_text += f"{i+1}. {name} (Semantic Role Match Score: {score}/10)\n"
     
-    output_text += "\nThese candidates were ranked objectively based on the **text similarity** of their previous job roles to the target roles, using expected salary as a tie-breaker."
+    output_text += "\nThese candidates were ranked by the LLM based on the **conceptual fit (semantic similarity)** of their previous job roles to the target roles, using expected salary as a tie-breaker."
     
     return output_text
 
@@ -179,8 +224,8 @@ def show_first_candidates():
 # Gradio interface (Updated Heading and Launch)
 # ----------------------------
 with gr.Blocks() as app:
-    gr.Markdown("# 🏆 Candidate Selection (Objective Similarity Matching)") 
-    gr.Markdown("### **Reliable ranking using text similarity (TF-IDF & Cosine Similarity) - No LLM API required.**")
+    gr.Markdown("# 🏆 Candidate Selection (Semantic Role Matching)") 
+    gr.Markdown("### **Uses a large instruction model to score conceptual fit and similarity between roles.**")
     
     gr.Markdown("#### 🔍 Raw JSON Preview: First 5 Candidates")
     gr.Dataframe(show_first_candidates(), label="First 5 JSON Entries")
@@ -196,11 +241,10 @@ with gr.Blocks() as app:
 
     gr.Markdown("---")
 
-    # Step 2: Recommendations
-    # Changed function name to reflect the new logic
-    recommend_button = gr.Button("3. Rank Candidates by Role Similarity")
-    recommend_output_text = gr.Textbox(label="Top Candidate Recommendations Summary", lines=10, placeholder="Click 'Rank Candidates by Role Similarity' after Step 2 completes.")
-    recommend_button.click(similarity_recommendations, inputs=[category_dropdown], outputs=[recommend_output_text])
+    # Step 2: LLM Recommendations
+    recommend_button = gr.Button("3. Rank Candidates by Semantic Role Match")
+    recommend_output_text = gr.Textbox(label="Top Candidate Recommendations Summary", lines=10, placeholder="Click 'Rank Candidates by Semantic Role Match' after Step 2 completes.")
+    recommend_button.click(llm_recommendations, inputs=[category_dropdown], outputs=[recommend_output_text])
 
 if __name__ == "__main__":
     app.launch(share=True)