Update app.py

app.py

@@ -0,0 +1,164 @@
+try:
+  from langchain_community.vectorstores import Chroma
+except:
+  from langchain_community.vectorstores import Chroma
+
+from langchain.chains import ConversationChain
+from langchain.chains.conversation.memory import ConversationBufferWindowMemory
+
+
+# Import the necessary libraries.
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_groq import ChatGroq
+
+
+
+
+import os
+import requests  # Or your Groq library
+
+groq_api_key = os.environ.get("my_groq_api_key")
+
+
+
+
+# Initialize a ChatGroq object with a temperature of 0 and the "mixtral-8x7b-32768" model.
+llm = ChatGroq(temperature=0, model_name="llama3-70b-8192",api_key=groq_api_key)
+
+from langchain_community.embeddings import SentenceTransformerEmbeddings
+
+embeddings = SentenceTransformerEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"trust_remote_code":True}) 
+
+
+
+
+
+memory = ConversationBufferWindowMemory(
+    memory_key="history", k=3, return_only_outputs=True
+)
+
+
+
+
+
+
+query_text="what did alice say to rabbit"
+
+# Prepare the DB.
+#embedding_function = OpenAIEmbeddings() # main
+
+CHROMA_PATH = "chroma8"
+# call the chroma generated in a directory
+db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embeddings)
+
+# Search the DB for similar documents to the query.
+results = db.similarity_search_with_relevance_scores(query_text, k=2)
+if len(results) == 0 or results[0][1] < 0.5:
+    print(f"Unable to find matching results.")
+
+
+
+
+
+
+
+
+
+from langchain import PromptTemplate
+query_text = "when did alice see mad hatter"
+
+results = db.similarity_search_with_relevance_scores(query_text, k=3)
+if len(results) == 0 or results[0][1] < 0.5:
+    print(f"Unable to find matching results.")
+
+
+context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results ])
+
+template = """
+The following is a conversation between a human an AI. Answer  question based only on the conversation.
+
+Current conversation:
+{history}
+
+"""
+
+
+
+s="""
+
+\n question: {input}
+
+\n answer:""".strip()
+
+
+prompt = PromptTemplate(input_variables=["history", "input"], template=template+context_text+'\n'+s)
+
+
+
+
+chain = ConversationChain(
+    llm=llm,
+    
+    prompt=prompt,
+    memory=memory,
+    verbose=True,
+)
+
+
+
+
+# Generate a response from the Llama model
+def get_llama_response(message: str, history: list) -> str:
+    """
+    Generates a conversational response from the Llama model.
+
+    Parameters:
+        message (str): User's input message.
+        history (list): Past conversation history.
+
+    Returns:
+        str: Generated response from the Llama model.
+    """
+    query_text =message
+
+    results = db.similarity_search_with_relevance_scores(query_text, k=2)
+    if len(results) == 0 or results[0][1] < 0.5:
+        print(f"Unable to find matching results.")
+
+
+    context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results ])
+
+    template = """
+    The following is a conversation between a human an AI. Answer  question based only on the conversation.
+
+    Current conversation:
+    {history}
+
+    """
+
+
+
+    s="""
+
+    \n question: {input}
+
+    \n answer:""".strip()
+
+
+    prompt = PromptTemplate(input_variables=["history", "input"], template=template+context_text+'\n'+s)
+
+    #print(template)
+    chain.prompt=prompt
+    res = chain.predict(input=query_text)
+    return res
+        #return response.strip()
+
+
+
+import gradio as gr
+iface = gr.Interface(fn=get_llama_response, inputs=gr.Textbox(),
+             outputs="textbox")
+iface.launch(share=True)
+
+
+