Create app.py

app.py

@@ -0,0 +1,45 @@
+import streamlit as st
+from transformers import AutoModel
+from pinecone import IndexClient
+import os  # For environment variable access
+
+# Replace with your Space's environment variable name for API key
+API_KEY = os.environ.get("PINECONE_API_KEY")
+
+# Connect to Pinecone using the API key retrieved from the Space's environment variable
+client = IndexClient(api_key=API_KEY)
+
+# Load pre-trained model (replace with your chosen model)
+model = AutoModel.from_pretrained("sentence-transformers/all-mpnet-base-v2")
+
+def process_and_search(query):
+    # Preprocess user input (example: tokenization, normalization)
+    preprocessed_query = preprocess_query(query)  # Replace with your implementation
+
+    # Encode the preprocessed query using the pre-trained model
+    encoded_query = model.encode(preprocessed_query, return_tensors="pt")
+
+    # Perform vector search in Pinecone
+    results = client.query(INDEX_NAME, encoded_query.cpu().numpy())
+
+    # Process search results (example: extract answers, format display)
+    processed_results = []
+    for result in results:
+        # Example processing: extract answer from metadata
+        answer = result["metadata"]["answer"]  # Adapt based on your data structure
+        processed_results.append(answer)
+
+    return processed_results
+st.title("Pinecone Search App")
+
+user_query = st.text_area("Enter your question:", height=100)
+
+if st.button("Search"):
+    if user_query:
+        # Process, search, and display results (call the process_and_search function)
+        answers = process_and_search(user_query)
+        st.write("Search Results:")
+        for answer in answers:
+            st.write(f"- {answer}")
+    else:
+        st.error("Please enter a question.")