Adding app and requirements

gradio_app.py

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+from sentence_transformers import SentenceTransformer
+from langchain.prompts import ChatPromptTemplate
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain.schema import AIMessage, HumanMessage
+from langchain_chroma import Chroma
+import gradio as gr
+
+# Load environment variables
+CHROMA_PATH = "chroma"
+
+# Hugging Face API setup
+repo_id = "Qwen/Qwen2.5-Coder-32B-Instruct"
+
+PROMPT_TEMPLATE = """
+Answer the question based on the context provided. If no relevant information is found, state so.
+
+Context:
+{context}
+
+Question:
+{question}
+
+Answer:
+"""
+
+# Initialize the local embedding model
+embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+
+
+class LocalEmbeddingFunction:
+    def embed_documents(self, texts):
+        # Generate embeddings for a list of texts
+        embeddings = embedding_model.encode(texts)
+        return embeddings.tolist() if hasattr(embeddings, 'tolist') else embeddings
+
+    def embed_query(self, query):
+        # Generate an embedding for a single query string
+        query_embedding = embedding_model.encode(query)
+        return query_embedding.tolist() if hasattr(query_embedding, 'tolist') else query_embedding
+
+
+class LLM:
+    llm = HuggingFaceEndpoint(
+        repo_id=repo_id,
+        temperature=0.2,
+    )
+
+    def generate_response(self, prompt):
+        return self.llm.invoke(prompt)
+
+
+def get_embedding_function():
+    return LocalEmbeddingFunction()
+
+def get_chat_response(query, history):
+    # Retrieve embeddings from the Chroma DB
+    embedding_function = get_embedding_function()
+    db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embedding_function)
+
+    # Search the DB
+    results = db.similarity_search_with_score(query, k=5)
+    context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
+
+    # Create prompt with context and query
+    prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
+    prompt = prompt_template.format(context=context_text, question=query)
+
+    # Generate response using the LLM class and Hugging Face model
+    model = LLM()
+    response_text = model.generate_response(prompt)
+
+    # Update the history
+    history.append(AIMessage(content = response_text))
+
+    return response_text
+
+
+# Gradio Interface
+def predict(message, history):
+    # Initialize history if not provided
+    history_langchain_format = []
+
+    for msg in history:
+
+        if msg['role'] == "user":
+            history_langchain_format.append(HumanMessage(content=msg['content']))
+
+        elif msg['role'] == "assistant":
+            history_langchain_format.append(AIMessage(content=msg['content']))
+
+    history_langchain_format.append(HumanMessage(content=message))
+
+    # Get response from the model
+    response = get_chat_response(message, history_langchain_format)
+
+    return response
+
+gr.ChatInterface(predict, type="messages").launch()

requirements.txt

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+sentence-transformers
+langchain
+langchain-huggingface
+gradio
+chromadb