Create app.py

app.py

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+import gradio as gr
+import os
+import openai
+import pandas as pd
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate
+from langchain.chains import LLMChain
+from langchain.output_parsers import StrOutputParser
+from langchain.chat_models import ChatOpenAI
+
+# Set up the Hugging Face model and embeddings
+model_name = "BAAI/bge-large-en-v1.5"
+model_kwargs = {'device':'cuda'}
+encode_kwargs = {'normalize_embeddings':True}
+
+embedding_function = HuggingFaceBgeEmbeddings(
+    model_name = model_name,
+    model_kwargs = model_kwargs,
+    encode_kwargs = encode_kwargs
+)
+
+# Set the OpenAI API key
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# Load the FAISS index using LangChain's FAISS implementation
+db = FAISS.load_local("Faiss", embedding_function, allow_dangerous_deserialization=True)
+parser = StrOutputParser()
+
+# Load your data (e.g., a DataFrame)
+df = pd.read_pickle('df_news.pkl')
+
+# Search function to retrieve relevant documents
+def search(query):
+    query_embedding = embedding_function.embed_query(query).reshape(1, -1).astype('float32')
+    D, I = db.similarity_search_with_score(query_embedding, k=10)
+    results = []
+    for idx in I[0]:
+        if idx < 3327:  # Adjust this based on your indexing
+            doc_index = idx
+            results.append({
+                'type': 'metadata',
+                'title': df.iloc[doc_index]['title'],
+                'author': df.iloc[doc_index]['author'],
+                'full_text': df.iloc[doc_index]['full_text'],
+                'source': df.iloc[doc_index]['url']
+            })
+        else:
+            chunk_index = idx - 3327
+            metadata = metadata_info[chunk_index]
+            doc_index = metadata['index']
+            chunk_text = metadata['chunk']
+            results.append({
+                'type': 'content',
+                'title': df.iloc[doc_index]['title'],
+                'author': df.iloc[doc_index]['author'],
+                'content': chunk_text,
+                'source': df.iloc[doc_index]['url']
+            })
+
+    return results
+
+# Generate an answer based on the retrieved documents
+def generate_answer(query):
+    context = search(query)
+    context_str = "\n\n".join([f"Title: {doc['title']}\nContent: {doc.get('content', doc.get('full_text', ''))}" for doc in context])
+
+    prompt = f"""
+    Answer the question based on the context below. If you can't answer the question, answer with "I don't know".
+    Context: {context_str}
+    Question: {query}
+    """
+
+    # Set up the ChatOpenAI model with temperature and other parameters
+    chat = ChatOpenAI(
+        model="gpt-4",
+        temperature=0.2,
+        max_tokens=1500,
+        api_key=openai.api_key
+    )
+
+    messages = [
+        SystemMessagePromptTemplate.from_template("You are a helpful assistant."),
+        HumanMessagePromptTemplate.from_template(prompt)
+    ]
+
+    chat_chain = LLMChain(
+        llm=chat,
+        prompt=ChatPromptTemplate.from_messages(messages)
+    )
+
+    # Get the response from the chat model
+    response = chat_chain.run(messages)
+    return response.strip()
+
+# Gradio chat interface
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    response = generate_answer(message)
+    yield response
+
+# Gradio demo setup
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch()