New Streamlit code for Hugging Face deployment

app.py

@@ -1,34 +1,100 @@
-# streamlit_app.py
 import streamlit as st
-import requests
-
-# Streamlit app title
-st.title("Top K Search with Vector DataBase")
-
-# FastAPI endpoint URL
-# url = "http://localhost:8084/search/"
-url = "https://huggingface.co/search/"
-
-# Input fields in Streamlit
-id = st.text_input("Enter ID:", value="1")
-prompt = st.text_input("Enter your prompt:")
-k = st.number_input("Top K results:", min_value=1, max_value=100, value=3)
-
-# Trigger the search when the button is clicked
-if st.button("Search"):
-    # Construct the request payload
-    payload = {
-        "id": id,
-        "prompt": prompt,
-        "k": k
-    }
-
-    # Make the POST request
-    response = requests.post(url, json=payload)
-
-    # Handle the response
-    if response.status_code == 200:
-        results = response.json()
-        st.write(results)
+from models.vectorizer import Vectorizer
+from models.prompt_search_engine import PromptSearchEngine
+from models.data_reader import load_prompts_from_jsonl
+from models.Query import Query, SimilarPrompt, SearchResponse, PromptVector, VectorResponse
+from sentence_transformers import SentenceTransformer
+import os
+
+# Path to your prompts data (you need to upload this file to your Hugging Face space)
+prompt_path = "models/prompts_data.jsonl"  # Update this to the correct path in your space
+
+# Initialize search engine and model
+prompts = load_prompts_from_jsonl(prompt_path)
+search_engine = PromptSearchEngine()
+search_engine.add_prompts_to_vector_database(prompts)
+
+# Streamlit App Interface
+st.title("Prompt Search Engine")
+st.write("Search for similar prompts using the local search engine.")
+
+# Input for the user's prompt
+query_input = st.text_input("Enter your prompt:")
+
+# Number of similar prompts to retrieve (k)
+k = st.number_input("Number of similar prompts to retrieve:", min_value=1, max_value=10, value=3)
+
+# Button to trigger search
+if st.button("Search Prompts"):
+    if query_input:
+        query = Query(prompt=query_input)
+        similar_prompts, distances = search_engine.most_similar(query.prompt, top_k=k)
+
+        # Format and display search results
+        response = [
+            SimilarPrompt(prompt=prompt, distance=float(distance))
+            for prompt, distance in zip(similar_prompts, distances)
+        ]
+        st.write("Search Results:")
+        for result in response:
+            st.write(f"Prompt: {result.prompt}, Distance: {result.distance}")
+    else:
+        st.error("Please enter a prompt.")
+
+# Additional functionality for vector similarity
+st.write("---")
+st.write("### Vector Similarities")
+
+if st.button("Retrieve All Vector Similarities"):
+    if query_input:
+        query = Query(prompt=query_input)
+        query_embedding = search_engine.model.encode([query.prompt])  # Encode the prompt to a vector
+        all_similarities = search_engine.cosine_similarity(query_embedding, search_engine.index)
+
+        # Format and display vector similarities
+        response = [
+            PromptVector(vector=index, distance=float(distance))
+            for index, distance in enumerate(all_similarities)
+        ]
+        st.write("Vector Similarities:")
+        for result in response:
+            st.write(f"Vector Index: {result.vector}, Distance: {result.distance}")
     else:
-        st.error(f"Error: {response.status_code} - {response.text}")
+        st.error("Please enter a prompt.")
+
+
+
+# # streamlit_app.py
+# import streamlit as st
+# import requests
+
+# # Streamlit app title
+# st.title("Top K Search with Vector DataBase")
+
+# # FastAPI endpoint URL
+# # url = "http://localhost:8084/search/"
+# url = "https://huggingface.co/search/"
+
+# # Input fields in Streamlit
+# id = st.text_input("Enter ID:", value="1")
+# prompt = st.text_input("Enter your prompt:")
+# k = st.number_input("Top K results:", min_value=1, max_value=100, value=3)
+
+# # Trigger the search when the button is clicked
+# if st.button("Search"):
+#     # Construct the request payload
+#     payload = {
+#         "id": id,
+#         "prompt": prompt,
+#         "k": k
+#     }
+
+#     # Make the POST request
+#     response = requests.post(url, json=payload)
+
+#     # Handle the response
+#     if response.status_code == 200:
+#         results = response.json()
+#         st.write(results)
+#     else:
+#         st.error(f"Error: {response.status_code} - {response.text}")