Update app.py

app.py

@@ -8,6 +8,7 @@ import streamlit as st
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationChain
 from langchain.llms import OpenAI
+from langchain.chat_models import ChatOpenAI
 from langchain.vectorstores import Chroma
 from langchain.document_loaders import TextLoader
 from langchain.embeddings import OpenAIEmbeddings
@@ -20,6 +21,14 @@ st.set_page_config(
     layout="wide",
     initial_sidebar_state="expanded",
 )
+# Function to get content of Data.txt
+def get_data_txt_content():
+    file_path = "Data.txt"
+    if os.path.exists(file_path):
+        with open(file_path, "r") as file:
+            return file.read()
+    else:
+        return "Data.txt content is unavailable."
 
 # Session State Initialization
 if "detected_label" not in st.session_state:
@@ -90,11 +99,15 @@ def initialize_llm():
     documents = loader.load()
 
     # Create embeddings and vector store
-    embeddings = OpenAIEmbeddings(api_key=os.getenv('OPENAI_API_KEY'))
+    embeddings = OpenAIEmbeddings(api_key="sk-0iMBF0ndiFS-qsKzwMyY7E4q5U6-wM2Si3pXB3p-GNT3BlbkFJDu_xmtFGDTSpwg34CUjQC6_DTyEW-SQTH1pI08DtYA")
     vectorstore = Chroma.from_documents(documents, embeddings)
 
-    # Initialize LLM and memory
-    llm = OpenAI(api_key=os.getenv('OPENAI_API_KEY'), temperature=0)
+    # Initialize ChatOpenAI and memory
+    llm = ChatOpenAI(
+        model="gpt-4",  # Use gpt-4 or gpt-4o depending on your needs
+        temperature=0.7,
+        api_key="sk-0iMBF0ndiFS-qsKzwMyY7E4q5U6-wM2Si3pXB3p-GNT3BlbkFJDu_xmtFGDTSpwg34CUjQC6_DTyEW-SQTH1pI08DtYA"
+    )
     memory = ConversationBufferMemory(return_messages=True)
     conversation = ConversationChain(llm=llm, memory=memory, verbose=False)
     return conversation
@@ -165,7 +178,7 @@ if uploaded_file:
                 st.session_state.show_diagnosis = True
 
             if st.session_state.show_diagnosis:
-                query = f"Provide details about {st.session_state.detected_label}. Format: Detected Disease, Causes, Treatment, Precautions."
+                query = f"Provide details about {st.session_state.detected_label}. Format: Detected Disease, Causes, Treatment, Precautions"
                 with st.spinner("Fetching diagnosis..."):
                     diagnosis = st.session_state.conversation.predict(input=query)
                 st.subheader(f"Diagnosis for: {st.session_state.detected_label}")
@@ -176,19 +189,19 @@ if uploaded_file:
                 col1, col2, col3, col4 = st.columns(4)
 
                 with col1:
-                    if st.button("Compare Pesticides"):
-                        with st.spinner("Fetching comparison of pesticides..."):
+                    if st.button("Usable Pesticides"):
+                        with st.spinner("Fetching list of pesticides..."):
                             pesticides_info = st.session_state.conversation.predict(
-                                input=f"Provide a table comparing pesticides for {st.session_state.detected_label}."
+                                input=f"How do the pesticides for this {st.session_state.detected_label} disease vary based on environmental conditions and regional agricultural practices?"
                             )
-                            st.subheader("Comparison of Pesticides")
+                            st.subheader("List of Pesticides")
                             st.markdown(pesticides_info)
 
                 with col2:
                     if st.button("Detailed Causes and Effects"):
                         with st.spinner("Fetching detailed causes and effects..."):
                             detailed_info = st.session_state.conversation.predict(
-                                input=f"Provide a detailed explanation of causes and effects for {st.session_state.detected_label}."
+                                input=f"What are the long-term ecological and economic impacts of this {st.session_state.detected_label} disease on farming communities, and how can they be mitigated?"
                             )
                             st.subheader("Detailed Causes and Effects")
                             st.markdown(detailed_info)
@@ -197,7 +210,7 @@ if uploaded_file:
                     if st.button("Prevention Methods"):
                         with st.spinner("Fetching prevention methods..."):
                             prevention_info = st.session_state.conversation.predict(
-                                input=f"Provide prevention methods for {st.session_state.detected_label}."
+                                input=f"What innovative strategies, beyond traditional methods, can be adopted to prevent the recurrence of this {st.session_state.detected_label} disease in staple crops?"
                             )
                             st.subheader("Prevention Methods")
                             st.markdown(prevention_info)
@@ -206,7 +219,7 @@ if uploaded_file:
                     if st.button("Treatment Options"):
                         with st.spinner("Fetching treatment options..."):
                             treatment_info = st.session_state.conversation.predict(
-                                input=f"Provide detailed treatment options for {st.session_state.detected_label}."
+                                input=f"How can the integration of biological, chemical, and technological treatments improve the sustainability and effectiveness of managing this {st.session_state.detected_label} disease?"
                             )
                             st.subheader("Treatment Options")
                             st.markdown(treatment_info)