app

app.py

@@ -1,7 +1,46 @@
 import gradio as gr
+import os
+import pinecone
+from dotenv import load_dotenv
+from langchain.chains import RetrievalQA
+from langchain.vectorstores import Pinecone
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings
 
-def greet(name):
-    return "Hello " + name + "!!"
+
+
+load_dotenv()
+
+
+# APIS
+OPENAI_API_KEY=os.getenv("OPENAI_API_KEY")
+PINECONE_API_KEY=os.getenv("PINECONE_API_KEY")
+PINECONE_ENV=os.getenv("PINECONE_ENV")
+PINECONE_INDEX=os.getenv("PINECONE_INDEX")
+TEXT_EMBEDDING_MODEL=os.getenv("TEXT_EMBEDDING_MODEL")
+HF_MODEL=os.getenv("HF_MODEL")
+HF_API=os.getenv("HF_API")
+
+
+def model(query):
+    pinecone.init(
+    api_key=PINECONE_API_KEY,  # find at app.pinecone.io
+    environment=PINECONE_ENV,  # next to api key in console
+    )
+
+    embeddings = HuggingFaceInferenceAPIEmbeddings(api_key=HF_API ,model_name=HF_MODEL)
+    vectorstore = Pinecone.from_existing_index(PINECONE_INDEX, embeddings)
+
+    docs = vectorstore.similarity_search(query,k=5)
+
+    llm = ChatOpenAI(model_name="gpt-3.5-turbo",temperature=0.76, max_tokens=100, model_kwargs={"seed":235, "top_p":0.01})
+
+    chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=vectorstore.as_retriever())
+    answer=chain.run({"query": query + "you are a therapist who help people with personal development and self improvement"+ "You can only make conversations related to the provided context. If a response cannot be formed strictly using the context, politely say you don’t have knowledge about that topic."+"[strictly within 75 words]"})
+    return answer
+
+def greet(query):
+    return model(query)
 
 iface = gr.Interface(fn=greet, inputs="text", outputs="text")
 iface.launch()

requirements.txt

@@ -4,4 +4,5 @@ pdfminer.six
 tiktoken
 pinecone-client
 python-dotenv
-gradio
+gradio
+setuptools