Create app.py

app.py

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+import gradio as gr
+import random
+import time
+from sentence_transformers import SentenceTransformer
+import pinecone
+import os
+
+retriever = SentenceTransformer("sentence-transformers/all-MiniLM-L12-v2")
+
+pinecone_key = os.environ['PINECONE_SECRET']
+
+pinecone.init(
+    api_key=os.environ['PINECONE_SECRET'],
+    environment="eu-west1-gcp"
+)
+
+index_name = 'quran-ayah-search'
+
+index = pinecone.Index(index_name)
+
+
+def query_pinecone(query, top_k):
+  xq = retriever.encode([query]).tolist()
+
+  # search vector database for similar vector
+  xc = index.query(xq, top_k=top_k, include_metadata=True)
+
+  return xc['matches']
+
+def format_search_result(result):
+    data = result['metadata']
+    message = f"Ayah no: {data['ayah']}\nSurah no: {data['surah']}\nSentence:{data['arabic-text']}\nTranslation: {data['en-translation']}\n Tafsir:{data['en-tafsir-mokhtasar']}\n Relevant Tafsir: {data['vector-chunk']}"
+
+    return message
+
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.Button("Clear")
+
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
+
+    def bot(history):
+        
+        query = history[-1][0]
+
+        results = query_pinecone(query, top_k=3)
+
+        for match in results:
+            if history[-1][1] == None:
+                history[-1][1] = format_search_result(match)
+
+            else:
+                history.append([None, format_search_result(match)])  
+        
+        return history
+
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, chatbot, chatbot
+    )
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+
+demo.launch()