Update app.py

app.py

@@ -1,43 +1,55 @@
 import gradio as gr
 from sentence_transformers import CrossEncoder
 import torch
-
-# Load MS MARCO CrossEncoder (query-document relevance)
-MODEL_NAME = "cross-encoder/ms-marco-MiniLM-L-12-v2"
-model = CrossEncoder(MODEL_NAME)
-
-def predict_similarity(s1, s2):
-    # Get raw score (logit)
-    score = model.predict([(s1, s2)])[0]
-    # Apply sigmoid to map to 0–1
-    similarity = torch.sigmoid(torch.tensor(score)).item()
-    return round(similarity, 4)
-
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("## 🔎 Query–Document Relevance (CrossEncoder)")
-    gr.Markdown(
-        f"Model: **{MODEL_NAME}**\n\n"
-        "Scores are mapped with sigmoid to the range **0 (irrelevant) → 1 (highly relevant)**."
-    )
-
-    with gr.Row():
-        s1 = gr.Textbox(label="Query", placeholder="Enter your search query...")
-        s2 = gr.Textbox(label="Document Chunk", placeholder="Enter a document chunk...")
+import requests
+
+# -------------------------------
+# CONFIG
+# -------------------------------
+HF_MODEL = "cross-encoder/ms-marco-MiniLM-L-12-v2"
+JINA_MODEL = "jina-reranker-m0"
+JINA_API_KEY = "jina_4075150fa702471c85ddea0a9ad4b306ouE7ymhrCpvxTxX3mScUv5LLDPKQ"
+JINA_ENDPOINT = "https://api.jina.ai/v1/rerank"
+
+# -------------------------------
+# Load Hugging Face CrossEncoder
+# -------------------------------
+hf_model = CrossEncoder(HF_MODEL)
+
+def compare_models(query, doc):
+    # Hugging Face score
+    raw_score = hf_model.predict([(query, doc)])[0]
+    hf_score = torch.sigmoid(torch.tensor(raw_score)).item()
+
+    # Jina reranker score
+    headers = {
+        "Authorization": f"Bearer {JINA_API_KEY}",
+        "Content-Type": "application/json",
+    }
+    payload = {
+        "model": JINA_MODEL,
+        "query": query,
+        "documents": [doc],
+    }
+    try:
+        r = requests.post(JINA_ENDPOINT, headers=headers, json=payload, timeout=20)
+        r.raise_for_status()
+        jina_score = r.json()["results"][0]["relevance_score"]
+    except Exception as e:
+        jina_score = f"Error: {str(e)}"
+
+    return f"Hugging Face ({HF_MODEL}): {round(hf_score,4)}\nJina ({JINA_MODEL}): {jina_score}"
+
+# -------------------------------
+# Simple Lite UI
+# -------------------------------
+with gr.Blocks() as demo:
+    gr.Markdown("### 🔎 Query vs Document Similarity (HF vs Jina)")
+    query = gr.Textbox(label="Query", lines=3, placeholder="Paste your query here...")
+    doc = gr.Textbox(label="Document Chunk", lines=6, placeholder="Paste your document chunk here...")
+    out = gr.Textbox(label="Scores", lines=3)
 
     btn = gr.Button("Compute Similarity 🚀")
-    out = gr.Number(label="Relevance Score (0–1)")
-
-    btn.click(fn=predict_similarity, inputs=[s1, s2], outputs=out)
-
-    gr.Examples(
-        examples=[
-            ["What is the capital of France?", "Paris is the capital city of France."],
-            ["I am happy today", "I am feeling joyful and excited right now."],
-            ["Python programming", "Bananas are yellow fruits."],
-            ["Machine learning applications", "ML is widely used in healthcare and finance."],
-        ],
-        inputs=[s1, s2],
-    )
+    btn.click(compare_models, inputs=[query, doc], outputs=out)
 
-# 🚀 Launch without enable_queue (new Gradio)
 demo.launch()