Update app.py

app.py

@@ -0,0 +1,68 @@
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+from langchain_core.prompts import PromptTemplate
+from langchain_community.llms import OpenAI
+from langchain_core.chains import LLMChain
+
+
+# Fetch API Keys from environment variables
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+# Verify required environment variables are set
+if not OPENAI_API_KEY:
+    raise ValueError("Missing required environment variable: 'OPENAI_API_KEY'")
+
+
+# Title of the App
+st.title("ReAct Agent")
+
+# Step 1: User Input Collection
+user_input = st.text_area("Enter the input prompt for the LLM:", placeholder="Describe what you want...")
+num_intermediate_steps = st.number_input("Number of Intermediate Steps to Display:", min_value=1, value=3)
+
+# Check if inputs are provided
+if st.button("Run Processing"):
+    if not api_key or not user_input:
+        st.error("Please provide all required inputs!")
+    else:
+        st.success("Inputs received! Processing...")
+
+        # Step 2: LLM Initialization and Template
+        st.header("Step 2: LLM Processing - Intermediate Outputs")
+        try:
+            llm = OpenAI(api_key=api_key, temperature=0.7)
+            prompt = PromptTemplate(
+                input_variables=["text"],
+                template="Process the following text step-by-step: {text}"
+            )
+
+            chain = LLMChain(llm=llm, prompt=prompt)
+            intermediate_outputs = []
+
+            # Simulate multiple intermediate outputs
+            for step in range(num_intermediate_steps):
+                response = chain.run(user_input + f" Step {step+1}")
+                intermediate_outputs.append(response)
+                st.write(f"**Intermediate Step {step+1}:**")
+                st.info(response)
+
+            # Step 3: Final Processing
+            st.header("Step 3: Visualization")
+            # Convert responses to a DataFrame
+            df = pd.DataFrame({"Step": [f"Step {i+1}" for i in range(num_intermediate_steps)],
+                               "Output": intermediate_outputs})
+            st.dataframe(df)
+
+            # Plot the outputs' length as a graph
+            st.subheader("Output Length per Step")
+            df['Length'] = df['Output'].apply(len)
+            fig, ax = plt.subplots()
+            ax.bar(df['Step'], df['Length'])
+            ax.set_xlabel("Steps")
+            ax.set_ylabel("Output Length")
+            ax.set_title("Text Length at Each Step")
+            st.pyplot(fig)
+
+        except Exception as e:
+            st.error(f"An error occurred: {str(e)}")