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README.md

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+
+---
+title: Causal Loop Diagram Generator
+emoji: ♻️
+colorFrom: blue
+colorTo: green
+sdk: streamlit
+app_file: app.py
+pinned: true
+---
+
+# ♻️ Causal Loop Diagram Generator (Simple Version)
+
+Enter a system description or paragraph. The app extracts causal links and draws an interactive Causal Loop Diagram (CLD).
+
+Powered by DeepSeek 7B Instruct (HF Inference Endpoint).

app.py

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+
+import streamlit as st
+import requests
+import networkx as nx
+import matplotlib.pyplot as plt
+
+# Replace with your actual HF Inference Endpoint URL
+INFERENCE_API_URL = "https://your-inference-endpoint-url"
+
+# Replace with your actual Hugging Face Access Token
+HF_TOKEN = "Bearer YOUR-HUGGINGFACE-TOKEN"
+
+headers = {
+    "Authorization": HF_TOKEN,
+    "Content-Type": "application/json"
+}
+
+st.set_page_config(page_title="Causal Loop Diagram Generator", page_icon="♻️")
+st.title("♻️ Causal Loop Diagram (CLD) Generator (Simple Version)")
+st.markdown("Enter a system description (even a paragraph). The app will extract causal links and draw the CLD diagram.")
+
+user_input = st.text_area("Enter System Description:", height=200)
+
+if st.button("Generate CLD Diagram"):
+    if user_input.strip() == "":
+        st.error("Please enter a valid system description.")
+    else:
+        try:
+            prompt_text = f"Extract causal relationships from the paragraph and format them like [Cause] → [Effect] (+/-):\n\n{user_input}"
+            response = requests.post(
+                INFERENCE_API_URL,
+                headers=headers,
+                json={"inputs": prompt_text}
+            )
+
+            if response.status_code == 200:
+                generated_text = response.json()[0]['generated_text']
+                causal_links = []
+
+                lines = generated_text.split("\n")
+                for line in lines:
+                    line = line.strip()
+                    if "→" in line and ("(+)" in line or "(-)" in line):
+                        if line.endswith("(+)") or line.endswith("(-)"):
+                            causal_links.append(line)
+
+                if causal_links:
+                    st.success("✅ Extracted Causal Relationships:")
+                    st.text("\n".join(causal_links))
+
+                    G = nx.DiGraph()
+
+                    for line in causal_links:
+                        parts = line.split("→")
+                        if len(parts) == 2:
+                            cause = parts[0].strip()
+                            rest = parts[1].strip()
+
+                            if rest.endswith("(+)"):
+                                effect = rest[:-3].strip()
+                                polarity = "+"
+                            elif rest.endswith("(-)"):
+                                effect = rest[:-3].strip()
+                                polarity = "-"
+                            else:
+                                continue
+
+                            G.add_edge(cause, effect, polarity=polarity)
+
+                    pos = nx.spring_layout(G, seed=42)
+                    plt.figure(figsize=(10, 8))
+                    nx.draw(G, pos, with_labels=True, arrows=True, node_color="skyblue", edge_color="black", node_size=2000, font_size=12, font_weight='bold')
+
+                    edge_labels = {(u, v): G.edges[u, v]['polarity'] for u, v in G.edges()}
+                    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_color="red", font_size=14)
+
+                    st.pyplot(plt)
+
+                else:
+                    st.warning("⚠️ No causal relationships detected.")
+            else:
+                st.error(f"❌ API Error: {response.status_code} - {response.text}")
+
+        except Exception as e:
+            st.error(f"❌ Connection Error: {e}")

requirements.txt

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+
+streamlit
+networkx
+matplotlib
+requests