UPDATE GEMINI FOR FAST INF

app.py

@@ -1,15 +1,123 @@
+# import gradio as gr
+# from langchain.prompts import PromptTemplate
+# from langchain_community.llms import CTransformers
+# from langchain_community.vectorstores import Pinecone as LangchainPinecone
+# from langchain.chains import RetrievalQA
+# from pinecone import Pinecone
+# from dotenv import load_dotenv
+# import os
+
+# # Load environment variables
+# load_dotenv()
+# PINECONE_API_KEY = os.getenv('PINECONE_API_KEY')
+# index_name = "apple-chatbot"
+
+# class AppleChatbot:
+#     def __init__(self, k=2, max_tokens=512, temperature=0.8):
+#         self.k = k
+#         self.max_tokens = max_tokens
+#         self.temperature = temperature
+#         self.qa_chain = self.initialize_chatbot()
+
+#     def download_hf_embeddings(self):
+#         from langchain_community.embeddings import HuggingFaceEmbeddings
+#         return HuggingFaceEmbeddings()
+
+#     def initialize_chatbot(self):
+#         embeddings = self.download_hf_embeddings()
+#         model_path = "TheBloke/Llama-2-7B-Chat-GGML"
+#         llm = CTransformers(
+#             model=model_path,
+#             model_type="llama",
+#             config={
+#                 'max_new_tokens': self.max_tokens,
+#                 'temperature': self.temperature
+#             }
+#         )
+        
+#         # Initialize pinecone
+#         pc = Pinecone(api_key=PINECONE_API_KEY)
+#         index = pc.Index(index_name)
+
+#         # Use the same prompt template from your original application
+#         prompt_template = """
+#         You are an expert in apple cultivation and orchard management. 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: {question}
+#         Answer:"""
+#         PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+#         chain_type_kwargs = {"prompt": PROMPT}
+        
+#         docsearch = LangchainPinecone(index, embeddings.embed_query, "text")
+#         qa = RetrievalQA.from_chain_type(
+#             llm=llm,
+#             chain_type="stuff",
+#             retriever=docsearch.as_retriever(search_kwargs={'k': self.k}),
+#             return_source_documents=True,
+#             chain_type_kwargs=chain_type_kwargs
+#         )
+#         return qa
+
+#     def get_response(self, question):
+#         try:
+#             result = self.qa_chain({"query": question})
+#             return result["result"]
+#         except Exception as e:
+#             return f"Error: {str(e)}"
+
+# # Initialize the chatbot
+# chatbot = AppleChatbot()
+
+# # Define the Gradio interface
+# def respond(message, history):
+#     response = chatbot.get_response(message)
+#     return response
+
+# # Create the Gradio interface
+# demo = gr.ChatInterface(
+#     respond,
+#     chatbot=gr.Chatbot(height=600),
+#     textbox=gr.Textbox(placeholder="Ask me anything about apple cultivation...", container=False),
+#     title="Apple Orchard Expert Chatbot",
+#     description="Ask questions about apple cultivation and orchard management. Built with Langchain, Pinecone, and Llama-2.",
+#     theme=gr.themes.Soft(),
+#     examples=[
+#         "What are the ideal conditions for growing apples?",
+#         "How do I prevent common apple diseases?",
+#         "What is the best time to harvest apples?",
+#     ],
+#     cache_examples=False,
+# )
+
+# # Launch the interface
+# if __name__ == "__main__":
+#     demo.queue()  # Enable queuing
+#     demo.launch(
+#         server_name="0.0.0.0",
+#         server_port=7860,
+#         share=True
+#     )
+
+
 import gradio as gr
 from langchain.prompts import PromptTemplate
-from langchain_community.llms import CTransformers
 from langchain_community.vectorstores import Pinecone as LangchainPinecone
 from langchain.chains import RetrievalQA
 from pinecone import Pinecone
 from dotenv import load_dotenv
 import os
+import google.generativeai as genai
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 # Load environment variables
 load_dotenv()
 PINECONE_API_KEY = os.getenv('PINECONE_API_KEY')
+GEMINI_API_KEY = os.getenv('GEMINI_API_KEY')
 index_name = "apple-chatbot"
 
 class AppleChatbot:
@@ -25,17 +133,12 @@ class AppleChatbot:
 
     def initialize_chatbot(self):
         embeddings = self.download_hf_embeddings()
-        model_path = "TheBloke/Llama-2-7B-Chat-GGML"
-        llm = CTransformers(
-            model=model_path,
-            model_type="llama",
-            config={
-                'max_new_tokens': self.max_tokens,
-                'temperature': self.temperature
-            }
-        )
         
-        # Initialize pinecone
+        # Initialize Gemini
+        genai.configure(api_key=GEMINI_API_KEY)
+        llm = genai.GenerativeModel('gemini-pro')
+        
+        # Initialize Pinecone
         pc = Pinecone(api_key=PINECONE_API_KEY)
         index = pc.Index(index_name)
 
@@ -80,7 +183,7 @@ demo = gr.ChatInterface(
     chatbot=gr.Chatbot(height=600),
     textbox=gr.Textbox(placeholder="Ask me anything about apple cultivation...", container=False),
     title="Apple Orchard Expert Chatbot",
-    description="Ask questions about apple cultivation and orchard management. Built with Langchain, Pinecone, and Llama-2.",
+    description="Ask questions about apple cultivation and orchard management. Built with Langchain, Pinecone, and Gemini.",
     theme=gr.themes.Soft(),
     examples=[
         "What are the ideal conditions for growing apples?",