Update a.py

a.py

@@ -1,166 +1,166 @@
-import streamlit as st
-import pandas as pd
-from fuzzywuzzy import process
-from langchain_community.llms import LlamaCpp
-from langchain_core.callbacks import StreamingStdOutCallbackHandler
-from langchain_core.prompts import PromptTemplate
-
-# Load the CSV files into DataFrames with Windows-1252 encoding
-df = pd.read_csv('location.csv', encoding='Windows-1252')
-df2 = pd.read_csv('train.csv')
-
-# Initialize the LlamaCpp model
-llm = LlamaCpp(
-    model_path="unsloth.Q5_K_M.gguf",
-    temperature=0.01,
-    max_tokens=500,
-    top_p=3,
-    callbacks=[StreamingStdOutCallbackHandler()],
-    verbose=False,
-    stop=["###"]
-)
-
-# Define the prompt template
-template = """Below is an instruction that describes a task, paired with an input that provides further context. Write a lengthy detailed response that appropriately completes the request.
-
-### Instruction:
-{instruction}
-
-### Input:
-{input}
-
-### Response:
-{response}"""
-
-prompt = PromptTemplate.from_template(template)
-
-
-# Function to find the best matching context based on user input
-def find_best_match(query):
-    questions = df2['Question'].tolist()
-    contexts = df2['Context'].tolist()
-
-    # Find the best match
-    best_match = process.extractOne(query, questions)
-    if best_match:
-        index = questions.index(best_match[0])
-        return contexts[index]
-    return "No relevant information found."
-
-
-# Function to truncate response at the nearest full stop
-def truncate_at_full_stop(text, max_length=500):
-    if len(text) <= max_length:
-        return text
-
-    truncated = text[:max_length]
-    print(f"Truncated text: {truncated}")
-
-    last_period = truncated.rfind('.')
-    print(f"Last period index: {last_period}")
-
-    if last_period != -1:
-        return truncated[:last_period + 1]
-
-    return truncated
-
-
-# Initialize session state for selected service, chat history, and AI history
-if 'selected_service' not in st.session_state:
-    st.session_state.selected_service = "Home"
-if 'chat_history' not in st.session_state:
-    st.session_state.chat_history = []
-if 'history' not in st.session_state:
-    st.session_state.history = []
-if 'input' not in st.session_state:
-    st.session_state['input'] = ''
-
-# Sidebar for selecting services
-with st.sidebar:
-    st.title("Select the Service")
-
-    # Create buttons for each service
-    if st.button('Medicine Services'):
-        st.session_state.selected_service = "Medicine Services"
-
-    if st.button('Kendra Locator'):
-        st.session_state.selected_service = "Kendra Locator"
-
-    if st.button('Assistant'):
-        st.session_state.selected_service = "Assistant"
-
-# Main content area based on selected service
-if st.session_state.selected_service == "Home":
-    st.title("Welcome to Medical Service Center")
-    st.write("Explore the options in the sidebar to get started.")
-
-elif st.session_state.selected_service == "Medicine Services":
-    st.title("Medicine Services")
-
-    # Display chat history
-    for chat in st.session_state.chat_history:
-        st.write(f"**User:** {chat['user']}")
-        st.write(f"**Bot:** {chat['bot']}")
-
-    # User input section
-    user_input = st.text_input("Enter medicine:")
-
-    # Handle input when the "Send" button is clicked
-    if st.button('Send'):
-        if user_input:
-            response = find_best_match(user_input)
-            st.session_state.chat_history.append({"user": user_input, "bot": response})
-
-elif st.session_state.selected_service == "Kendra Locator":
-    st.title("Kendra Locator")
-    display_option = st.selectbox("Select:", ["Address", "Email"])
-    pin_code_input = st.text_input("Enter Pin Code:")
-
-    if st.button("Locate"):
-        if pin_code_input:
-            result = df[df['Pin'].astype(str) == pin_code_input]
-            if not result.empty:
-                if display_option == "Address":
-                    st.write(f"Address: {result['Address'].values[0]}")
-                elif display_option == "Email":
-                    st.write(f"Email: {result['Email'].values[0]}")
-            else:
-                st.write("No results found.")
-        else:
-            st.write("Please enter a pin code.")
-
-elif st.session_state.selected_service == "Assistant":
-    st.title("Query Assistance")
-
-    # Display AI chat history
-    for chat in st.session_state.history:
-        st.write(f"**Medicine Query:** {chat['user']}")
-        st.write(f"**Chatbot:** {chat['bot']}")
-
-
-    # Function to handle user input
-    def handle_input():
-        user_input = st.session_state['input']
-        if user_input:
-            # Format the prompt
-            formatted_prompt = prompt.format(
-                instruction="You are an all-knowing Medical AI. Provide detailed responses to only medicine-related queries.",
-                input=user_input,
-                response=""  # Leave this blank for generation!
-            )
-
-            # Generate response
-            response = llm.invoke(formatted_prompt)
-
-            # Truncate response if necessary
-            truncated_response = truncate_at_full_stop(response)
-
-            # Update the chat history
-            st.session_state.history.append({"user": user_input, "bot": truncated_response})
-
-            # Clear the input box
-            st.session_state['input'] = ''
-
-
-    # Persistent text input at the top
-    st.text_input("Enter Medicine Name:", key="input", on_change=handle_input)
+import streamlit as st
+import pandas as pd
+from fuzzywuzzy import process
+from langchain_community.llms import LlamaCpp
+from langchain_core.callbacks import StreamingStdOutCallbackHandler
+from langchain_core.prompts import PromptTemplate
+
+# Load the CSV files into DataFrames with Windows-1252 encoding
+df = pd.read_csv('location.csv', encoding='Windows-1252')
+df2 = pd.read_csv('train.csv')
+
+# Initialize the LlamaCpp model
+llm = LlamaCpp(
+    model_path="unsloth.Q5_K_M.gguf",
+    temperature=0.01,
+    max_tokens=500,
+    top_p=3,
+    callbacks=[StreamingStdOutCallbackHandler()],
+    verbose=False,
+    stop=["###"]
+)
+
+# Define the prompt template
+template = """Below is an instruction that describes a task, paired with an input that provides further context. Write a lengthy detailed response that appropriately completes the request.
+
+### Instruction:
+{instruction}
+
+### Input:
+{input}
+
+### Response:
+{response}"""
+
+prompt = PromptTemplate.from_template(template)
+
+
+# Function to find the best matching context based on user input
+def find_best_match(query):
+    questions = df2['Question'].tolist()
+    contexts = df2['Context'].tolist()
+
+    # Find the best match
+    best_match = process.extractOne(query, questions)
+    if best_match:
+        index = questions.index(best_match[0])
+        return contexts[index]
+    return "No relevant information found."
+
+
+# Function to truncate response at the nearest full stop
+def truncate_at_full_stop(text, max_length=500):
+    if len(text) <= max_length:
+        return text
+
+    truncated = text[:max_length]
+    print(f"Truncated text: {truncated}")
+
+    last_period = truncated.rfind('.')
+    print(f"Last period index: {last_period}")
+
+    if last_period != -1:
+        return truncated[:last_period + 1]
+
+    return truncated
+
+
+# Initialize session state for selected service, chat history, and AI history
+if 'selected_service' not in st.session_state:
+    st.session_state.selected_service = "Home"
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+if 'history' not in st.session_state:
+    st.session_state.history = []
+if 'input' not in st.session_state:
+    st.session_state['input'] = ''
+
+# Sidebar for selecting services
+with st.sidebar:
+    st.title("Select the Service")
+
+    # Create buttons for each service
+    if st.button('Medicine Services'):
+        st.session_state.selected_service = "Medicine Services"
+
+    if st.button('Kendra Locator'):
+        st.session_state.selected_service = "Kendra Locator"
+
+    if st.button('Assistant'):
+        st.session_state.selected_service = "Assistant"
+
+# Main content area based on selected service
+if st.session_state.selected_service == "Home":
+    st.title("Welcome to Medical Service Center")
+    st.write("Explore the options in the sidebar to get started.")
+
+elif st.session_state.selected_service == "Medicine Services":
+    st.title("Medicine Services")
+
+    # Display chat history
+    for chat in st.session_state.chat_history:
+        st.write(f"**User:** {chat['user']}")
+        st.write(f"**Bot:** {chat['bot']}")
+
+    # User input section
+    user_input = st.text_input("Enter medicine:")
+
+    # Handle input when the "Send" button is clicked
+    if st.button('Send'):
+        if user_input:
+            response = find_best_match(user_input)
+            st.session_state.chat_history.append({"user": user_input, "bot": response})
+
+elif st.session_state.selected_service == "Kendra Locator":
+    st.title("Kendra Locator")
+    display_option = st.selectbox("Select:", ["Address", "Email"])
+    pin_code_input = st.text_input("Enter Pin Code:")
+
+    if st.button("Locate"):
+        if pin_code_input:
+            result = df[df['Pin'].astype(str) == pin_code_input]
+            if not result.empty:
+                if display_option == "Address":
+                    st.write(f"Address: {result['Address'].values[0]}")
+                elif display_option == "Email":
+                    st.write(f"Email: {result['Email'].values[0]}")
+            else:
+                st.write("No results found.")
+        else:
+            st.write("Please enter a pin code.")
+
+elif st.session_state.selected_service == "Assistant":
+    st.title("Query Assistance")
+
+    # Display AI chat history
+    for chat in st.session_state.history:
+        st.write(f"**Medicine Query:** {chat['user']}")
+        st.write(f"**Chatbot:** {chat['bot']}")
+
+
+    # Function to handle user input
+    def handle_input():
+        user_input = st.session_state['input']
+        if user_input:
+            # Format the prompt
+            formatted_prompt = prompt.format(
+                instruction="You are an all-knowing Medical AI. Provide detailed responses to only medicine-related queries.",
+                input=user_input,
+                response=""  # Leave this blank for generation!
+            )
+
+            # Generate response
+            response = llm.invoke(formatted_prompt)
+
+            # Truncate response if necessary
+            truncated_response = truncate_at_full_stop(response)
+
+            # Update the chat history
+            st.session_state.history.append({"user": user_input, "bot": truncated_response})
+
+            # Clear the input box
+            st.session_state['input'] = ''
+
+
+    # Persistent text input at the top
+    st.text_input("Enter Medicine Query:", key="input", on_change=handle_input)