Refactor app.py to streamline email generation process by removing unused memory initialization and integrating email history tracking. Added session state management for email history and improved display of previous email generations. Cleaned up comments and enhanced code readability.

app.py

@@ -1,6 +1,4 @@
 import streamlit as st
-from langchain.chains import ConversationChain
-from langchain.memory import ConversationBufferMemory
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 import PyPDF2
 from dotenv import load_dotenv
@@ -9,7 +7,7 @@ import os
 # Load environment variables from .env
 load_dotenv()
 
-# Job API keys and endpoints
+# API Key
 access_token = os.getenv("API_KEY")
 
 # Streamlit App Title
@@ -28,6 +26,9 @@ uploaded_file = st.sidebar.file_uploader("Upload your CV (PDF format):", type=["
 if "parsed_cv" not in st.session_state:
     st.session_state.parsed_cv = None
 
+if "email_history" not in st.session_state:
+    st.session_state.email_history = []
+
 if uploaded_file is not None:
     try:
         # Extract text from PDF
@@ -37,7 +38,6 @@ if uploaded_file is not None:
 
         # Parse CV details and save to session state
         def parse_cv(cv_text):
-            # Basic parsing logic (can be extended for specific details)
             return f"""
             Name: [Extracted Name]
             Contact Information: [Extracted Contact Info]
@@ -72,9 +72,9 @@ if access_token:
                 model=model,
                 tokenizer=tokenizer,
                 max_new_tokens=512,
-                temperature=0.7,  # Add temperature for better generation
-                do_sample=True,   # Enable sampling
-                top_p=0.95        # Add top_p for better text quality
+                temperature=0.7,
+                do_sample=True,
+                top_p=0.95
             )
         except Exception as e:
             st.error(f"Failed to initialize the model: {str(e)}")
@@ -82,14 +82,6 @@ if access_token:
 
     text_gen_pipeline = initialize_pipeline(access_token)
 
-    @st.cache_resource
-    def initialize_memory():
-        return ConversationBufferMemory(
-            input_key="input",
-            output_key="output",
-            return_messages=True
-        )
-
     # Input job description
     job_description = st.text_area("Enter the job description:", "")
 
@@ -97,8 +89,6 @@ if access_token:
     if st.button("Generate Email"):
         if st.session_state.parsed_cv and job_description.strip():
             try:
-                memory = initialize_memory()
-                
                 # Improved prompt template
                 prompt = f"""Task: Write a professional job application email.
 
@@ -114,7 +104,6 @@ Keep the tone professional and enthusiastic.
 
 Email:
 """
-                
                 # Generate email using the pipeline
                 if text_gen_pipeline:
                     response = text_gen_pipeline(
@@ -123,20 +112,23 @@ Email:
                         return_full_text=False
                     )[0]['generated_text']
 
-                    # Store in memory
-                    memory.save_context(
-                        {"input": f"Job Description: {job_description}"},
-                        {"output": response}
-                    )
+                    # Save response in history
+                    st.session_state.email_history.append({
+                        "job_description": job_description,
+                        "email": response
+                    })
 
                     # Display response
                     st.subheader("Generated Email:")
                     st.write(response)
 
                     # Display conversation history
-                    st.subheader("Previous Generations:")
-                    for entry in memory.load_memory_variables({})['history']:
-                        st.write(entry)
+                    if st.session_state.email_history:
+                        st.subheader("Previous Generations:")
+                        for idx, entry in enumerate(st.session_state.email_history, 1):
+                            st.write(f"### Email {idx}")
+                            st.write(f"**Job Description:** {entry['job_description']}")
+                            st.write(f"**Generated Email:** {entry['email']}")
                 else:
                     st.error("Text generation pipeline not properly initialized.")
             except Exception as e: