nit fix

app.py

@@ -90,8 +90,6 @@ LANGUAGE_MODEL = HuggingFaceHub(
     huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN
 )
 
-
-
 def save_uploaded_file(uploaded_file):
     file_path = PDF_STORAGE_PATH + uploaded_file.name
     with open(file_path, "wb") as file:

trial.py

@@ -1,39 +0,0 @@
-import streamlit as st
-from langchain.llms import HuggingFaceHub
-from langchain.chains import ConversationChain
-from langchain.memory import ConversationBufferMemory
-import os
-
-HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
-
-# Model to use
-MODEL_REPO = "mistralai/Mixtral-8x7B-Instruct-v0.1"
-
-# Setup the LLM using LangChain + Hugging Face Inference API
-llm = HuggingFaceHub(
-    repo_id=MODEL_REPO,
-    model_kwargs={"temperature": 0.7, "max_new_tokens": 2000},
-    huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN
-)
-
-# Add memory to remember the chat history
-memory = ConversationBufferMemory()
-
-# Setup the conversation chain
-conversation = ConversationChain(
-    llm=llm,
-    memory=memory,
-    verbose=False
-)
-
-# Streamlit app
-st.set_page_config(page_title="DeepSeek LLM (LangChain API)", page_icon="🤖")
-st.title("🤖 DeepSeek Chatbot via LangChain (API)")
-
-user_input = st.text_input("You:", "")
-
-if user_input:
-    response = conversation.predict(input=user_input)
-    st.markdown(f"**🤖 DeepSeek:** {response}")
-    print(response)
-

trial.txt

@@ -0,0 +1,39 @@
+# import streamlit as st
+# from langchain.llms import HuggingFaceHub
+# from langchain.chains import ConversationChain
+# from langchain.memory import ConversationBufferMemory
+# import os
+
+# HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+# # Model to use
+# MODEL_REPO = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+
+# # Setup the LLM using LangChain + Hugging Face Inference API
+# llm = HuggingFaceHub(
+#     repo_id=MODEL_REPO,
+#     model_kwargs={"temperature": 0.7, "max_new_tokens": 2000},
+#     huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN
+# )
+
+# # Add memory to remember the chat history
+# memory = ConversationBufferMemory()
+
+# # Setup the conversation chain
+# conversation = ConversationChain(
+#     llm=llm,
+#     memory=memory,
+#     verbose=False
+# )
+
+# # Streamlit app
+# st.set_page_config(page_title="DeepSeek LLM (LangChain API)", page_icon="🤖")
+# st.title("🤖 DeepSeek Chatbot via LangChain (API)")
+
+# user_input = st.text_input("You:", "")
+
+# if user_input:
+#     response = conversation.predict(input=user_input)
+#     st.markdown(f"**🤖 DeepSeek:** {response}")
+#     print(response)
+