Merge branch 'main' of https://huggingface.co/spaces/klinic-hackupc/klinic into main

app.py

@@ -19,9 +19,13 @@ import numpy as np
 from sentence_transformers import SentenceTransformer
 
 
-begin = st.container()
-
+# variables to reveal next steps
+show_graph = False
+show_analyze_status = False
+show_overview = False
+show_details = False
 
+# IRIS connection
 username = "demo"
 password = "demo"
 hostname = os.getenv("IRIS_HOSTNAME", "localhost")
@@ -30,116 +34,145 @@ namespace = "USER"
 CONNECTION_STRING = f"iris://{username}:{password}@{hostname}:{port}/{namespace}"
 engine = create_engine(CONNECTION_STRING)
 
-begin.write("# Klìnic")
 
-description_input = begin.text_input(
-    label="Enter the disease description 👇",
-    placeholder="A disease that causes memory loss and other cognitive impairments.",
-)
-if begin.button("Analyze 🔎"):
-    # 1. Embed the textual description that the user entered using the model
-    # 2. Get 5 diseases with the highest cosine silimarity from the DB
-    encoder = SentenceTransformer("allenai-specter")
-    diseases_related_to_the_user_text = get_diseases_related_to_a_textual_description(
-        description_input, encoder
-    )
-    # for disease_label in diseases_related_to_the_user_text:
-    #    st.text(disease_label)
-    # 3. Get the similarities of the embeddings of those diseases (cosine similarity of the embeddings of the nodes of such diseases)
-    diseases_uris = [disease["uri"] for disease in diseases_related_to_the_user_text]
-    get_similarities_among_diseases_uris(diseases_uris)
-    print(diseases_related_to_the_user_text)
-    # 4. Potentially filter out the diseases that are not similar enough (e.g. similarity < 0.8)
-    # 5. Augment the set of diseases: add new diseases that are similar to the ones that are already in the set, until we get 10-15 diseases
-    augmented_set_of_diseases = augment_the_set_of_diseaces(diseases_uris)
-    print(augmented_set_of_diseases)
-    # 6. Query the embeddings of the diseases related to each clinical trial (also in the DB), to get the most similar clinical trials to our set of diseases
-    clinical_trials_related_to_the_diseases = get_clinical_trials_related_to_diseases(
-        augmented_set_of_diseases, encoder
-    )
-    print(f'clinical_trials_related_to_the_diseases: {clinical_trials_related_to_the_diseases}')
-    json_of_clinical_trials = get_clinical_records_by_ids(
-        [trial["nct_id"] for trial in clinical_trials_related_to_the_diseases]
-    )
-    print(f'json_of_clinical_trials: {json_of_clinical_trials}')
-    # 8. Use an LLM to extract numerical data from the clinical trials (e.g. number of patients, number of deaths, etc.). Get summary statistics out of that.
-    # 9. Show the results to the user: graph of the diseases chosen, summary of the clinical trials, summary statistics of the clinical trials, and list of the details of the clinical trials considered
-    graph_of_diseases = agraph(
-        nodes=[
-            Node(id="A", label="Node A", size=10),
-            Node(id="B", label="Node B", size=10),
-            Node(id="C", label="Node C", size=10),
-            Node(id="D", label="Node D", size=10),
-            Node(id="E", label="Node E", size=10),
-            Node(id="F", label="Node F", size=10),
-            Node(id="G", label="Node G", size=10),
-            Node(id="H", label="Node H", size=10),
-            Node(id="I", label="Node I", size=10),
-            Node(id="J", label="Node J", size=10),
-        ],
-        edges=[
-            Edge(source="A", target="B"),
-            Edge(source="B", target="C"),
-            Edge(source="C", target="D"),
-            Edge(source="D", target="E"),
-            Edge(source="E", target="F"),
-            Edge(source="F", target="G"),
-            Edge(source="G", target="H"),
-            Edge(source="H", target="I"),
-            Edge(source="I", target="J"),
-        ],
-        config=Config(height=500, width=500),
-    )
-# TODO: also when user clicks enter
-
-begin.write(":red[Here should be the graph]")  # TODO remove
-chart_data = pd.DataFrame(
-    np.random.randn(20, 3), columns=["a", "b", "c"]
-)  # TODO remove
-begin.scatter_chart(chart_data)  # TODO remove
-
-begin.write("## Disease Overview")
-disease_overview = ":red[lorem ipsum]"  # TODO
-begin.write(disease_overview)
-
-begin.write("## Clinical Trials Details")
-trials = []
-# TODO replace mock data
-with open("mock_trial.json") as f:
-    d = json.load(f)
-for i in range(0, 5):
-    trials.append(d)
-
-for trial in trials:
-    with st.expander(f"{trial['protocolSection']['identificationModule']['nctId']}"):
-        official_title = trial["protocolSection"]["identificationModule"][
-            "officialTitle"
-        ]
-        st.write(f"##### {official_title}")
-
-        brief_summary = trial["protocolSection"]["descriptionModule"]["briefSummary"]
-        st.write(brief_summary)
-
-        status_module = {
-            "Status": trial["protocolSection"]["statusModule"]["overallStatus"],
-            "Status Date": trial["protocolSection"]["statusModule"][
-                "statusVerifiedDate"
-            ],
-        }
-        st.write("###### Status")
-        st.table(status_module)
-
-        design_module = {
-            "Study Type": trial["protocolSection"]["designModule"]["studyType"],
-            # "Phases": trial["protocolSection"]["designModule"]["phases"], # breaks formatting because it is an array
-            "Allocation": trial["protocolSection"]["designModule"]["designInfo"][
-                "allocation"
+st.title("Klìnic")
+st.header("", divider='rainbow')
+st.text('') # dummy to add spacing
+
+with st.container(): # user input
+    col1, col2 = st.columns((6, 1))
+
+    with col1:
+        description_input = st.text_area(label="Enter the disease description 👇", placeholder='A disease that causes memory loss and other cognitive impairments.')
+
+    with col2:
+        st.text('') # dummy to center vertically
+        st.text('') # dummy to center vertically
+        st.text('') # dummy to center vertically
+        show_analyze_status = st.button("Analyze 🔎")
+
+
+# analyze
+with st.container():
+    if show_analyze_status:
+        with st.status("Analyzing...") as status:
+            # 1. Embed the textual description that the user entered using the model
+            # 2. Get 5 diseases with the highest cosine silimarity from the DB
+            encoder = SentenceTransformer("allenai-specter")
+            diseases_related_to_the_user_text = get_diseases_related_to_a_textual_description(
+                description_input, encoder
+            )
+            # for disease_label in diseases_related_to_the_user_text:
+            #    st.text(disease_label)
+            # 3. Get the similarities of the embeddings of those diseases (cosine similarity of the embeddings of the nodes of such diseases)
+            diseases_uris = [disease["uri"] for disease in diseases_related_to_the_user_text]
+            get_similarities_among_diseases_uris(diseases_uris)
+            #print(diseases_related_to_the_user_text)
+            # 4. Potentially filter out the diseases that are not similar enough (e.g. similarity < 0.8)
+            # 5. Augment the set of diseases: add new diseases that are similar to the ones that are already in the set, until we get 10-15 diseases
+            augmented_set_of_diseases = augment_the_set_of_diseaces(diseases_uris)
+            #print(augmented_set_of_diseases)
+            # 6. Query the embeddings of the diseases related to each clinical trial (also in the DB), to get the most similar clinical trials to our set of diseases
+            clinical_trials_related_to_the_diseases = get_clinical_trials_related_to_diseases(
+                augmented_set_of_diseases, encoder
+            )
+            #print(f'clinical_trials_related_to_the_diseases: {clinical_trials_related_to_the_diseases}')
+            json_of_clinical_trials = get_clinical_records_by_ids(
+                [trial["nct_id"] for trial in clinical_trials_related_to_the_diseases]
+            )
+            #print(f'json_of_clinical_trials: {json_of_clinical_trials}')
+            # 8. Use an LLM to extract numerical data from the clinical trials (e.g. number of patients, number of deaths, etc.). Get summary statistics out of that.
+            # 9. Show the results to the user: graph of the diseases chosen, summary of the clinical trials, summary statistics of the clinical trials, and list of the details of the clinical trials considered
+            status.update(label="Done!", state="complete")
+            time.sleep(1)
+            show_graph = True
+
+
+# graph
+with st.container():
+    if show_graph:
+        # TODO actual graph
+        graph_of_diseases = agraph(
+            nodes=[
+                Node(id="A", label="Node A", size=10),
+                Node(id="B", label="Node B", size=10),
+                Node(id="C", label="Node C", size=10),
+                Node(id="D", label="Node D", size=10),
+                Node(id="E", label="Node E", size=10),
+                Node(id="F", label="Node F", size=10),
+                Node(id="G", label="Node G", size=10),
+                Node(id="H", label="Node H", size=10),
+                Node(id="I", label="Node I", size=10),
+                Node(id="J", label="Node J", size=10),
             ],
-            "Participants": trial["protocolSection"]["designModule"]["enrollmentInfo"][
-                "count"
+            edges=[
+                Edge(source="A", target="B"),
+                Edge(source="B", target="C"),
+                Edge(source="C", target="D"),
+                Edge(source="D", target="E"),
+                Edge(source="E", target="F"),
+                Edge(source="F", target="G"),
+                Edge(source="G", target="H"),
+                Edge(source="H", target="I"),
+                Edge(source="I", target="J"),
             ],
-        }
-        st.write("###### Design")
-        st.table(design_module)
+            config=Config(height=500, width=500),
+        )
+        time.sleep(2)
+        show_overview = True
+
+
+# overview
+with st.container():
+    if show_overview:
+        st.write("## Disease Overview")
+        disease_overview = ":red[lorem ipsum]"  # TODO
+        st.write(disease_overview)
+        time.sleep(2)
+        show_details = True
+
+
+# details
+with st.container():
+    if show_details:
+        st.write("## Clinical Trials Details")
+        trials = []
+        # TODO replace mock data
+        with open("mock_trial.json") as f:
+            d = json.load(f)
+        for i in range(0, 5):
+            trials.append(d)
+
+        for trial in trials:
+            with st.expander(f"{trial['protocolSection']['identificationModule']['nctId']}"):
+                official_title = trial["protocolSection"]["identificationModule"][
+                    "officialTitle"
+                ]
+                st.write(f"##### {official_title}")
+
+                brief_summary = trial["protocolSection"]["descriptionModule"]["briefSummary"]
+                st.write(brief_summary)
+
+                status_module = {
+                    "Status": trial["protocolSection"]["statusModule"]["overallStatus"],
+                    "Status Date": trial["protocolSection"]["statusModule"][
+                        "statusVerifiedDate"
+                    ],
+                }
+                st.write("###### Status")
+                st.table(status_module)
+
+                design_module = {
+                    "Study Type": trial["protocolSection"]["designModule"]["studyType"],
+                    # "Phases": trial["protocolSection"]["designModule"]["phases"], # breaks formatting because it is an array
+                    "Allocation": trial["protocolSection"]["designModule"]["designInfo"][
+                        "allocation"
+                    ],
+                    "Participants": trial["protocolSection"]["designModule"]["enrollmentInfo"][
+                        "count"
+                    ],
+                }
+                st.write("###### Design")
+                st.table(design_module)
 
-        # TODO more modules?
+                # TODO more modules?