Update app.py

app.py

@@ -1,63 +1,73 @@
 import streamlit as st
-import csv
 import chromadb
 from chromadb.utils import embedding_functions
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 from transformers import pipeline
-from langchain.llms import HuggingFacePipeline
-
-
-chroma_client = chromadb.PersistentClient(path="./data_db")
+from langchain_community.llms import HuggingFacePipeline
 
+# Initialize ChromaDB client
+chroma_client = chromadb.PersistentClient(path="data_db")
 
+# Define the embedding function
 sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-mpnet-base-v2")
 
-
+# Get or create a collection
 collection = chroma_client.get_or_create_collection(name="my_collection", embedding_function=sentence_transformer_ef)
 
-
-# Streamlit app layout
+# Streamlit UI elements
 st.title("ChromaDB and HuggingFace Pipeline Integration")
-
 query = st.text_input("Enter your query:", value="director")
 
 if st.button("Search"):
+    # Query the collection
     results = collection.query(
         query_texts=[query],
-        n_results=3,
+        n_results=1,
         include=['documents', 'distances', 'metadatas']
     )
+
     st.write("Query Results:")
     st.write(results['metadatas'])
 
-    if results['documents']:
-        context = results['documents'][0][0]
-        st.write("Context:")
-        st.write(context)
-        tokenizer = AutoTokenizer.from_pretrained("MBZUAI/LaMini-T5-738M")
-        model = AutoModelForSeq2SeqLM.from_pretrained("MBZUAI/LaMini-T5-738M")
-
-        pipe = pipeline(
-            "text2text-generation",
-            model=model,
-            tokenizer=tokenizer,
-            max_length=512
-        )
-
-        local_llm = HuggingFacePipeline(pipeline=pipe)
-
-        l = f"""
-        Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
-
-        {context}
-
-        Question: {query}
-        Helpful Answer:
-        """
-
-        answer = local_llm(l)
-        st.write("Answer:")
-        st.write(answer)    
-
-    
-            
+    # Log the structure of results
+    st.write("Results Structure:")
+    st.write(results)
+
+    if 'documents' in results and results['documents']:
+        # Check if the structure of results['documents'] is as expected
+        if len(results['documents']) > 0 and isinstance(results['documents'][0], list) and len(results['documents'][0]) > 0:
+            context = results['documents'][0][0]
+            st.write("Context:")
+            st.write(context)
+
+            # Load tokenizer and model
+            tokenizer = AutoTokenizer.from_pretrained("MBZUAI/LaMini-T5-738M")
+            model = AutoModelForSeq2SeqLM.from_pretrained("MBZUAI/LaMini-T5-738M")
+
+            # Create pipeline
+            pipe = pipeline(
+                "text2text-generation",
+                model=model,
+                tokenizer=tokenizer,
+                max_length=512
+            )
+
+            local_llm = HuggingFacePipeline(pipeline=pipe)
+
+            l = f"""
+            Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+            {context}
+
+            Question: {query}
+            Helpful Answer:
+            """
+
+            # Generate answer
+            answer = local_llm(l)
+            st.write("Answer:")
+            st.write(answer)
+        else:
+            st.write("No valid context found in the results.")
+    else:
+        st.write("No documents found for the query.")