Update app.py

app.py

@@ -3,15 +3,17 @@ from sentence_transformers import CrossEncoder
 import torch
 import requests
 import ast
+import os
 
 # -------------------------------
 # MODELS
 # -------------------------------
 CROSS_ENCODER_RERANK = "cross-encoder/ms-marco-MiniLM-L-12-v2"
 JINA_MODEL = "jina-reranker-m0"
-JINA_API_KEY = "jina_4075150fa702471c85ddea0a9ad4b306ouE7ymhrCpvxTxX3mScUv5LLDPKQ"
+JINA_API_KEY = os.getenv("JINA_API_KEY")  # set in HF Space settings
 JINA_ENDPOINT = "https://api.jina.ai/v1/rerank"
-NV_MODEL = "NV-RerankQA-Mistral-4B-v3"   # Hugging Face hosted
+NV_MODEL = "NV-RerankQA-Mistral-4B-v3"
+HF_API_KEY = os.getenv("HF_API_KEY")  # set in HF Space settings
 
 # -------------------------------
 # Load models
@@ -26,7 +28,7 @@ def evaluate_models(query, docs_str):
         docs = ast.literal_eval(docs_str)
         assert isinstance(docs, list), "Input must be a Python list of strings"
     except Exception as e:
-        return f"⚠️ Error parsing documents list: {e}"
+        return {"Error": f"⚠️ Error parsing documents list: {e}"}
 
     results = {}
 
@@ -36,27 +38,35 @@ def evaluate_models(query, docs_str):
     results["CrossEncoder (MS MARCO)"] = ce_rerank_scores
 
     # 2. Jina Reranker
-    headers = {"Authorization": f"Bearer {JINA_API_KEY}", "Content-Type": "application/json"}
-    payload = {"model": JINA_MODEL, "query": query, "documents": docs}
-    try:
-        r = requests.post(JINA_ENDPOINT, headers=headers, json=payload, timeout=30)
-        r.raise_for_status()
-        jina_scores = [round(res["relevance_score"], 4) for res in r.json()["results"]]
-        results["Jina Reranker"] = jina_scores
-    except Exception as e:
-        results["Jina Reranker"] = [f"Error: {e}"]
+    if JINA_API_KEY:
+        headers = {"Authorization": f"Bearer {JINA_API_KEY}", "Content-Type": "application/json"}
+        payload = {"model": JINA_MODEL, "query": query, "documents": docs}
+        try:
+            r = requests.post(JINA_ENDPOINT, headers=headers, json=payload, timeout=30)
+            r.raise_for_status()
+            jina_scores = [0] * len(docs)
+            for res in r.json()["results"]:
+                jina_scores[res["index"]] = round(res["relevance_score"], 4)
+            results["Jina Reranker"] = jina_scores
+        except Exception as e:
+            results["Jina Reranker"] = [f"Error: {e}"]
+    else:
+        results["Jina Reranker"] = ["Error: Missing JINA_API_KEY"]
 
     # 3. NV RerankQA Mistral-4B-v3 (HF Inference API)
-    try:
-        hf_endpoint = f"https://api-inference.huggingface.co/models/{NV_MODEL}"
-        headers = {"Authorization": f"Bearer YOUR_HF_API_KEY"}
-        payload = {"inputs": {"query": query, "documents": docs}}
-        r = requests.post(hf_endpoint, headers=headers, json=payload, timeout=60)
-        r.raise_for_status()
-        nv_scores = [round(res["score"], 4) for res in r.json()]
-        results["NV-RerankQA-Mistral-4B-v3"] = nv_scores
-    except Exception as e:
-        results["NV-RerankQA-Mistral-4B-v3"] = [f"Error: {e}"]
+    if HF_API_KEY:
+        try:
+            hf_endpoint = f"https://api-inference.huggingface.co/models/{NV_MODEL}"
+            headers = {"Authorization": f"Bearer {HF_API_KEY}"}
+            payload = {"inputs": {"query": query, "documents": docs}}
+            r = requests.post(hf_endpoint, headers=headers, json=payload, timeout=60)
+            r.raise_for_status()
+            nv_scores = [round(res["score"], 4) for res in r.json()]
+            results["NV-RerankQA-Mistral-4B-v3"] = nv_scores
+        except Exception as e:
+            results["NV-RerankQA-Mistral-4B-v3"] = [f"Error: {e}"]
+    else:
+        results["NV-RerankQA-Mistral-4B-v3"] = ["Error: Missing HF_API_KEY"]
 
     return results
 
@@ -64,13 +74,13 @@ def evaluate_models(query, docs_str):
 # Gradio UI
 # -------------------------------
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("## 👑 Ranking Battle (Scores Only)\nOutputs only **scores** from the 3 models.")
+    gr.Markdown("## 👑 Ranking Battle (Aligned Scores)\nOutputs only **scores aligned to input docs** from 3 models.")
 
     query = gr.Textbox(label="Query", lines=2, placeholder="Enter your search query...")
     docs = gr.Textbox(
         label="Documents (Python list)", 
         lines=6, 
-        placeholder='Example: ["Doc one text", "Doc two text", "Doc three text"]'
+        placeholder='Example: [\"Doc one text\", \"Doc two text\", \"Doc three text\"]'
     )
     out = gr.JSON(label="Model Scores")