Create app.py

app.py

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+import os
+import json
+import csv
+from dotenv import load_dotenv
+import streamlit as st
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain_core.output_parsers import StrOutputParser
+
+# Load environment variables
+load_dotenv()
+hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+# Initialize session state variables
+if "past" not in st.session_state:
+    st.session_state["past"] = []
+if "generated" not in st.session_state:
+    st.session_state["generated"] = []
+
+# Initialize parser and model
+parser = StrOutputParser()
+st.title("Llama-3.2-3B-Instruct")
+
+# Sidebar for LLM parameters
+st.sidebar.header("LLM Parameters")
+temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.8, 0.1)
+max_length = st.sidebar.slider("Max Length", 100, 500, 100, 50)
+
+llm = HuggingFaceEndpoint(
+    repo_id="meta-llama/Llama-3.2-3B-Instruct",
+    max_length=max_length,
+    temperature=temperature,
+    token_id=hf_token
+)
+
+# Input section
+col1, col2 = st.columns(2)
+
+with col1:
+    user_input = st.text_area("Enter your query:")
+
+with col2:
+    uploaded_file = st.file_uploader("Upload a file", type=["txt", "csv", "json"])
+
+submit = st.button("Submit")
+
+def parse_file(file):
+    try:
+        if file.type == "application/json":
+            return json.loads(file.read().decode("utf-8"))
+        elif file.type == "text/csv":
+            return list(csv.reader(file.read().decode("utf-8").splitlines()))
+        else:
+            return file.read().decode("utf-8")
+    except Exception as e:
+        st.error(f"Error reading file: {e}")
+        return None
+
+if submit:
+    if user_input:
+        with st.spinner("Thinking..."):
+            try:
+                result = llm.invoke(user_input)
+                output = parser.parse(result)
+                st.session_state["past"].append(user_input)
+                st.session_state["generated"].append(output)
+                st.success("Response generated.")
+            except Exception as e:
+                st.error(f"Error invoking LLM: {e}")
+    elif uploaded_file:
+        file_content = parse_file(uploaded_file)
+        if file_content:
+            with st.spinner("Thinking..."):
+                try:
+                    result = llm.invoke(str(file_content))
+                    output = parser.parse(result)
+                    st.session_state["past"].append(file_content)
+                    st.session_state["generated"].append(output)
+                    st.success("Response generated.")
+                except Exception as e:
+                    st.error(f"Error invoking LLM: {e}")
+    else:
+        st.warning("Please enter a query or upload a file.")
+
+# Display chat history
+if st.session_state["generated"]:
+    st.markdown("### Chat History")
+    for i in range(len(st.session_state["generated"])-1, -1, -1):
+        st.markdown(f"**You:** {st.session_state['past'][i]}")
+        st.markdown(f"**Llama:** {st.session_state['generated'][i]}")