Create main.py

main.py

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+import streamlit as st
+from langchain import memory as lc_memory
+from langsmith import Client
+from streamlit_feedback import streamlit_feedback
+from utils1 import get_expression_chain, get_retriever
+from langchain_core.tracers.context import collect_runs
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+GROQ_API_KEY = os.getenv('GROQ_API_KEY')
+HF_API_KEY = os.getenv("HF_API_KEY")
+COHERE_API_KEY = os.getenv("COHERE_API_KEY")
+
+
+
+
+LANGSMITH_TRACING="true"
+LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
+LANGSMITH_API_KEY=os.getenv("LANGSMITH_API_KEY")
+LANGSMITH_PROJECT="pr-smug-rancher-51"
+
+
+client = Client()
+st.set_page_config(page_title = "MEDICAL CHATBOT")
+st.subheader(f"Hello! How can I assist you today!")
+
+memory = lc_memory.ConversationBufferMemory(
+    chat_memory=lc_memory.StreamlitChatMessageHistory(key="langchain_messages"),
+    return_messages=True,
+    memory_key="chat_history",
+)
+
+st.sidebar.markdown("## Feedback Scale")
+feedback_option = (
+    "thumbs" if st.sidebar.toggle(label="`Faces` ⇄ `Thumbs`", value=False) else "faces"
+)
+
+with st.sidebar:
+    model_name = st.selectbox("**Model**", options=["llama-3.1-70b-versatile","gemma2-9b-it","gemma-7b-it","llama-3.2-3b-preview", "llama3-70b-8192", "mixtral-8x7b-32768"])
+    temp = st.slider("**Temperature**", min_value=0.0, max_value=1.0, step=0.001)
+    n_docs = st.number_input("**Number of retrieved documents**", min_value=0, max_value=10, value=5, step=1)
+ 
+if st.sidebar.button("Clear message history"):
+    print("Clearing message history")
+    memory.clear()
+
+retriever = get_retriever(n_docs=n_docs)
+chain = get_expression_chain(retriever, model_name, temp)
+
+for msg in st.session_state.langchain_messages:
+    avatar = "🦜" if msg.type == "ai" else None
+    with st.chat_message(msg.type, avatar=avatar):
+        st.markdown(msg.content)
+
+prompt = st.chat_input(placeholder="Describe your symptoms or medical questions ?")
+
+if prompt:
+    with st.chat_message("user"):
+        st.write(prompt)
+    
+    with st.chat_message("assistant", avatar="🐒"):
+        message_placeholder = st.empty()
+        full_response = ""
+        input_dict = {"input": prompt.lower()}
+        used_docs = retriever.get_relevant_documents(prompt.lower())
+
+        with collect_runs() as cb:
+            for chunk in chain.stream(input_dict, config={"tags": ["MEDICAL CHATBOT"]}):
+                full_response += chunk.content
+                message_placeholder.markdown(full_response + "▌") 
+                memory.save_context(input_dict, {"output": full_response})
+
+            st.session_state.run_id = cb.traced_runs[0].id
+        message_placeholder.markdown(full_response)
+        
+        if used_docs:
+            docs_content = "\n\n".join(
+                [
+                    f"Doc {i+1}:\n"
+                    f"Source: {doc.metadata['source']}\n"
+                    f"Title: {doc.metadata['title']}\n"
+                    f"Content: {doc.page_content}\n"
+                    for i, doc in enumerate(used_docs)
+                ]
+            )
+            with st.sidebar:
+                st.download_button(
+                    label="Consulted Documents",
+                    data=docs_content,
+                    file_name="Consulted_documents.txt",
+                    mime="text/plain",
+                )
+
+if st.session_state.get("run_id"):
+    run_id = st.session_state.run_id
+    feedback = streamlit_feedback(
+        feedback_type=feedback_option,
+        optional_text_label="[Optional] Please provide an explanation",
+        key=f"feedback_{run_id}",
+    )
+
+    score_mappings = {
+        "thumbs": {"👍": 1, "👎": 0},
+        "faces": {"😀": 1, "🙂": 0.75, "😐": 0.5, "🙁": 0.25, "😞": 0},
+    }
+
+    scores = score_mappings[feedback_option]
+
+    if feedback:
+        score = scores.get(feedback["score"])
+
+        if score is not None:
+            feedback_type_str = f"{feedback_option} {feedback['score']}"
+
+            feedback_record = client.create_feedback(
+                run_id,
+                feedback_type_str,
+                score=score,
+                comment=feedback.get("text"),
+            )
+            st.session_state.feedback = {
+                "feedback_id": str(feedback_record.id),
+                "score": score,
+            }
+        else:
+            st.warning("Invalid feedback score.")