Create app.py

app.py

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+import os
+import streamlit as st
+import networkx as nx
+import matplotlib.pyplot as plt
+from groq import Groq
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain.llms import OpenAI
+
+# Set up Groq API
+api_key = st.secrets["GROQ_API_KEY"]
+os.environ['GROQ_API_KEY'] = api_key
+client = Groq()
+
+def generate_response(prompt, context, max_tokens=1000):
+    messages = [
+        {"role": "system", "content": "You are a helpful coding assistant, specialized in code completion, debugging, and analysis. Provide concise and accurate responses."},
+        {"role": "user", "content": f"Context: {context}\n\nTask: {prompt}"}
+    ]
+    
+    chat_completion = client.chat.completions.create(
+        messages=messages,
+        model="llama2-70b-4096",
+        max_tokens=max_tokens
+    )
+    
+    return chat_completion.choices[0].message.content
+
+# Define a prompt template for entity extraction
+entity_extraction_prompt = PromptTemplate(
+    input_variables=["text"],
+    template="Extract the main entities and their relationships from the following text:\n{text}\n\nEntities and relationships:"
+)
+
+# Create an LLMChain for entity extraction
+llm = OpenAI(temperature=0)
+entity_chain = LLMChain(llm=llm, prompt=entity_extraction_prompt)
+
+def create_knowledge_graph(text):
+    # Extract entities and relationships
+    result = generate_response(entity_extraction_prompt.format(text=text), context="Entity extraction for knowledge graph")
+
+    # Parse the result and create a graph
+    G = nx.Graph()
+
+    # Simple parsing logic (you may need to adjust this based on the LLM's output format)
+    lines = result.split('\n')
+    for line in lines:
+        if '-' in line:
+            entity1, rest = line.split('-', 1)
+            relation, entity2 = rest.split(':', 1)
+            entity1 = entity1.strip()
+            entity2 = entity2.strip()
+            relation = relation.strip()
+
+            G.add_node(entity1)
+            G.add_node(entity2)
+            G.add_edge(entity1, entity2, relationship=relation)
+
+    return G
+
+def visualize_graph(G):
+    pos = nx.spring_layout(G)
+    plt.figure(figsize=(12, 8))
+    nx.draw(G, pos, with_labels=True, node_color='lightblue', node_size=3000, font_size=10, font_weight='bold')
+    edge_labels = nx.get_edge_attributes(G, 'relationship')
+    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
+    plt.title("Knowledge Graph")
+    plt.axis('off')
+    plt.tight_layout()
+    return plt
+
+# Streamlit app
+def main():
+    st.title("Knowledge Graph Generator")
+    
+    text = st.text_area("Enter the text for analysis:", height=200)
+    
+    if st.button("Generate Knowledge Graph"):
+        if text:
+            with st.spinner("Generating knowledge graph..."):
+                knowledge_graph = create_knowledge_graph(text)
+                st.success("Knowledge graph generated!")
+                
+                st.subheader("Visualization")
+                fig = visualize_graph(knowledge_graph)
+                st.pyplot(fig)
+        else:
+            st.warning("Please enter some text for analysis.")
+
+    # Footer
+    st.markdown("""
+    <style>
+    footer {
+        margin-top: 20px;
+        text-align: center;
+        color: #bb86fc;
+    }
+    footer a {
+        color: #bb86fc !important;
+        text-decoration: none;
+    }
+    footer a:hover {
+        text-decoration: underline;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+    footer_text = """
+    <footer>
+        <p>If you enjoyed the functionality of the app, please leave a like!<br>
+        Check out more on <a href="https://www.linkedin.com/in/girish-wangikar/" target="_blank">LinkedIn</a> |
+        <a href="https://girishwangikar.github.io/Girish_Wangikar_Portfolio.github.io/" target="_blank">Portfolio</a></p>
+    </footer>
+    """
+    st.markdown(footer_text, unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()