Update app.py

app.py

@@ -1,404 +1,404 @@
-import os
-import gradio as gr
-import pandas as pd
-
-from classifier import classify
-from statistics import mean
-from qa_summary import generate_answer
-
-
-HFTOKEN = os.environ["HF_TOKEN"]
-
-
-
-js = """
-    async () => {
-        // Load Twitter Widgets script
-        const script = document.createElement("script");
-        script.onload = () => console.log("Twitter Widgets.js loaded");
-        script.src = "https://platform.twitter.com/widgets.js";
-        document.head.appendChild(script);
-
-        // Define a global function to reload Twitter widgets
-        globalThis.reloadTwitterWidgets = () => {
-            if (window.twttr && twttr.widgets) {
-                twttr.widgets.load();
-            }
-        };
-    }
-"""
-
-def T_on_select(evt: gr.SelectData):
-    
-    if evt.index[1] == 3:
-        html = """<blockquote class="twitter-tweet" data-dnt="true" data-theme="dark">""" + \
-               f"""\n<a href="https://twitter.com/anyuser/status/{evt.value}"></a></blockquote>"""
-    else:
-        html = f"""<h2>{evt.value}</h2>"""
-    return gr.update(value=html)
-
-def single_classification(text, event_model, threshold):
-    res = classify(text, event_model, HFTOKEN, threshold)
-    return res["event"], res["score"]
-    
-def load_and_classify_csv(file, text_field, event_model, threshold):
-    filepath = file.name
-    if ".csv" in filepath:
-        df = pd.read_csv(filepath)
-    else:
-        df = pd.read_table(filepath)
-    
-    if text_field not in df.columns:
-        raise gr.Error(f"Error: Enter text column'{text_field}' not in CSV file.")
-
-    labels, scores = [], []
-    for post in df[text_field].to_list():
-        res = classify(post, event_model, HFTOKEN, threshold)
-        labels.append(res["event"])
-        scores.append(res["score"])
-
-    df["model_label"] = labels
-    df["model_score"] = scores
-    
-    # model_confidence = round(mean(scores), 5)   
-    model_confidence = mean(scores)
-    fire_related = gr.CheckboxGroup(choices=df[df["model_label"]=="fire"][text_field].to_list()) 
-    flood_related = gr.CheckboxGroup(choices=df[df["model_label"]=="flood"][text_field].to_list())
-    not_related = gr.CheckboxGroup(choices=df[df["model_label"]=="none"][text_field].to_list())
-    
-    return flood_related, fire_related, not_related, model_confidence, len(df[text_field].to_list()), df, gr.update(interactive=True), gr.update(interactive=True)
-
-def load_and_classify_csv_dataframe(file, text_field, event_model, threshold): #, filter
-    
-    filepath = file.name
-    if ".csv" in filepath:
-        df = pd.read_csv(filepath)
-    else:
-        df = pd.read_table(filepath)
-    
-    if text_field not in df.columns:
-        raise gr.Error(f"Error: Enter text column'{text_field}' not in CSV file.")
-
-    labels, scores = [], []
-    for post in df[text_field].to_list():
-        res = classify(post, event_model, HFTOKEN, threshold)
-        labels.append(res["event"])
-        scores.append(round(res["score"], 5))
-
-    df["event_label"] = labels
-    df["model_score"] = scores
-    
-    result_df = df[[text_field, "event_label", "model_score", "tweet_id"]].copy()
-    result_df["tweet_id"] = result_df["tweet_id"].astype(str)
-    
-    filters = list(result_df["event_label"].unique())
-    extra_filters = ['Not-'+x for x in filters]+['All']
-    
-    return gr.update(value=result_df), result_df, gr.update(choices=sorted(filters+extra_filters), 
-                                                            value='All', 
-                                                            label="Filter data by label", 
-                                                            visible=True)
-
-
-def calculate_accuracy(flood_selections, fire_selections, none_selections, num_posts, text_field, data_df):
-    posts = data_df[text_field].to_list()
-    selections = flood_selections + fire_selections + none_selections
-    eval = []
-    for post in posts:
-        if post in selections:
-            eval.append("incorrect")
-        else:
-            eval.append("correct")
-
-    data_df["model_eval"] = eval
-    incorrect = len(selections)
-    correct = num_posts - incorrect
-    accuracy = (correct/num_posts)*100
-
-    data_df.to_csv("output.csv")
-    return incorrect, correct, accuracy, data_df, gr.DownloadButton(label=f"Download CSV", value="output.csv", visible=True) 
-    
-def init_queries(history):
-    history = history or []
-    if not history:
-        history = [
-        "What areas are being evacuated?",
-        "What areas are predicted to be impacted?",
-        "What areas are without power?",
-        "What barriers are hindering response efforts?",
-        "What events have been canceled?",
-        "What preparations are being made?",
-        "What regions have announced a state of emergency?",
-        "What roads are blocked / closed?",
-        "What services have been closed?",
-        "What warnings are currently in effect?",
-        "Where are emergency services deployed?",
-        "Where are emergency services needed?",
-        "Where are evacuations needed?",
-        "Where are people needing rescued?",
-        "Where are recovery efforts taking place?",
-        "Where has building or infrastructure damage occurred?",
-        "Where has flooding occured?"
-        "Where are volunteers being requested?",
-        "Where has road damage occured?",
-        "What area has the wildfire burned?",
-        "Where have homes been damaged or destroyed?"]
-    
-    return gr.CheckboxGroup(choices=history), history
-
-def add_query(to_add, history):
-    if to_add not in history:
-        history.append(to_add)
-    return gr.CheckboxGroup(choices=history), history
-
-def qa_summarise(selected_queries, qa_llm_model, text_field, data_df):
-    
-    qa_input_df = data_df[data_df["model_label"] != "none"].reset_index()
-    texts = qa_input_df[text_field].to_list()
-    
-    summary = generate_answer(qa_llm_model, texts, selected_queries[0], selected_queries, mode="multi_summarize")
-
-    doc_df = pd.DataFrame()
-    doc_df["number"] = [i+1 for i in range(len(texts))]
-    doc_df["text"] = texts
-    
-    return summary, doc_df
-
-            
-with gr.Blocks(fill_width=True) as demo:
-    
-    demo.load(None,None,None,js=js)
-    
-    event_models = ["jayebaku/distilbert-base-multilingual-cased-crexdata-relevance-classifier",
-                    "jayebaku/distilbert-base-multilingual-cased-weather-classifier-2",
-                    "jayebaku/twitter-xlm-roberta-base-crexdata-relevance-classifier",
-                    "jayebaku/twhin-bert-base-crexdata-relevance-classifier"]
-    
-    T_data_ss_state = gr.State(value=pd.DataFrame())
-    
-    
-    with gr.Tab("Event Type Classification"):
-        gr.Markdown(
-        """
-        # T4.5 Relevance Classifier Demo 
-        This is a demo created to explore floods and wildfire classification in social media posts.\n
-        Usage:\n
-            - Upload .tsv or .csv data file (must contain a text column with social media posts).\n
-            - Next, type the name of the text column.\n
-            - Then, choose a BERT classifier model from the drop down.\n
-            - Finally, click the 'start prediction' buttton.\n
-        """)
-        with gr.Row():
-            with gr.Column(scale=4):
-                T_file_input = gr.File(label="Upload CSV or TSV File", file_types=['.tsv', '.csv'])
-                
-            with gr.Column(scale=6):
-                T_text_field = gr.Textbox(label="Text field name", value="tweet_text")
-                T_event_model = gr.Dropdown(event_models, value=event_models[0], label="Select classification model")
-                T_predict_button = gr.Button("Start Prediction")
-        with gr.Accordion("Prediction threshold", open=False):        
-            T_threshold = gr.Slider(0, 1, value=0, step=0.01, label="Prediction threshold", show_label=False, 
-                              info="This value sets a threshold by which texts classified flood or fire are accepted, \
-                                  higher values makes the classifier stricter (CAUTION: A value of 1 will set all predictions as none)", interactive=True)
-        
-        with gr.Row(): 
-            with gr.Column(scale=8):
-                T_data = gr.DataFrame(wrap=True,
-                                      show_fullscreen_button=True, 
-                                      show_copy_button=True,
-                                      show_row_numbers=True,
-                                      show_search="filter",
-                                      column_widths=["49%","17%","17%","17%"])
-                
-            with gr.Column(scale=2):
-                T_data_filter = gr.Dropdown(visible=False)
-                T_tweet_embed = gr.HTML("<h1>Select a Tweet ID to view Tweet</h1>")
-
-    
-
-    with gr.Tab("Event Type Classification Eval"):
-        gr.Markdown(
-        """
-        # T4.5 Relevance Classifier Demo 
-        This is a demo created to explore floods and wildfire classification in social media posts.\n
-        Usage:\n
-            - Upload .tsv or .csv data file (must contain a text column with social media posts).\n
-            - Next, type the name of the text column.\n
-            - Then, choose a BERT classifier model from the drop down.\n
-            - Finally, click the 'start prediction' buttton.\n
-        Evaluation:\n
-            - To evaluate the model's accuracy select the INCORRECT classifications using the checkboxes in front of each post.\n
-            - Then, click on the 'Calculate Accuracy' button.\n
-            - Then, click on the 'Download data as CSV' to get the classifications and evaluation data as a .csv file.
-        """)
-        with gr.Row():
-            with gr.Column(scale=4):
-                file_input = gr.File(label="Upload CSV or TSV File", file_types=['.tsv', '.csv'])
-                
-            with gr.Column(scale=6):
-                text_field = gr.Textbox(label="Text field name", value="tweet_text")
-                event_model = gr.Dropdown(event_models, value=event_models[0], label="Select classification model")
-                ETCE_predict_button = gr.Button("Start Prediction")
-        with gr.Accordion("Prediction threshold", open=False):        
-            threshold = gr.Slider(0, 1, value=0, step=0.01, label="Prediction threshold", show_label=False, 
-                              info="This value sets a threshold by which texts classified flood or fire are accepted, \
-                                  higher values makes the classifier stricter (CAUTION: A value of 1 will set all predictions as none)", interactive=True)
-        
-        with gr.Row(): # XXX confirm this is not a problem later --equal_height=True
-            with gr.Column():
-                gr.Markdown("""### Flood-related""")
-                flood_checkbox_output = gr.CheckboxGroup(label="Select ONLY incorrect classifications", interactive=True)
-                
-            with gr.Column():
-                gr.Markdown("""### Fire-related""")
-                fire_checkbox_output = gr.CheckboxGroup(label="Select ONLY incorrect classifications", interactive=True)
-                
-            with gr.Column():
-                gr.Markdown("""### None""")
-                none_checkbox_output = gr.CheckboxGroup(label="Select ONLY incorrect classifications", interactive=True) 
-
-        with gr.Row():
-            with gr.Column(scale=5):
-                gr.Markdown(r"""
-                Accuracy: is the model's ability to make correct predicitons.
-                It is the fraction of correct prediction out of the total predictions.
-                
-                $$
-                \text{Accuracy} = \frac{\text{Correct predictions}}{\text{All predictions}} * 100
-                $$
-                
-                Model Confidence: is the mean probabilty of each case 
-                belonging to their assigned classes. A value of 1 is best.
-                """, latex_delimiters=[{ "left": "$$", "right": "$$", "display": True }])
-                gr.Markdown("\n\n\n")
-                model_confidence = gr.Number(label="Model Confidence")
-                
-            with gr.Column(scale=5):
-                correct = gr.Number(label="Number of correct classifications")
-                incorrect = gr.Number(label="Number of incorrect classifications")
-                accuracy = gr.Number(label="Model Accuracy (%)")
-
-        ETCE_accuracy_button = gr.Button("Calculate Accuracy")
-        download_csv = gr.DownloadButton(visible=False)
-        num_posts = gr.Number(visible=False)
-        data = gr.DataFrame(visible=False) 
-        data_eval = gr.DataFrame(visible=False)
-        
-
-    qa_tab = gr.Tab("Question Answering")
-    with qa_tab:
-        gr.Markdown(
-        """
-        # Question Answering Demo
-        This section uses RAG to answer questions about the relevant social media posts identified by the relevance classifier\n 
-        Usage:\n
-            - Select queries from predefined\n
-            - Parameters for QA can be editted in sidebar\n
-            
-        Note: QA process is disabled untill after the relevance classification is done
-        """)
-        
-        with gr.Accordion("Parameters", open=False):
-            with gr.Row():
-                with gr.Column():
-                    qa_llm_model = gr.Dropdown(["mistral", "solar", "phi3mini"], label="QA model", value="phi3mini", interactive=True)
-                    aggregator = gr.Dropdown(["linear", "outrank"], label="Aggregation method", value="linear", interactive=True)
-                with gr.Column():
-                    batch_size = gr.Slider(50, 500, value=150, step=1, label="Batch size", info="Choose between 50 and 500", interactive=True)
-                    topk = gr.Slider(1, 10, value=5, step=1, label="Number of results to retrieve", info="Choose between 1 and 10", interactive=True)
-                    
-        selected_queries = gr.CheckboxGroup(label="Select at least one query using the checkboxes", interactive=True)
-        queries_state = gr.State()
-        qa_tab.select(init_queries, inputs=queries_state, outputs=[selected_queries, queries_state])
-
-        query_inp = gr.Textbox(label="Add custom queries like the one above, one at a time")
-        QA_addqry_button = gr.Button("Add to queries", interactive=False)
-        QA_run_button = gr.Button("Start QA", interactive=False)
-        hsummary = gr.Textbox(label="Summary")
-        
-        qa_df = gr.DataFrame()
-
-
-    with gr.Tab("Single Text Classification"):
-        gr.Markdown(
-        """
-        # Event Type Prediction Demo
-        In this section you test the relevance classifier with written texts.\n 
-        Usage:\n
-            - Type a tweet-like text in the textbox.\n
-            - Then press Enter.\n
-        """)
-        with gr.Row():
-            with gr.Column(scale=3):
-                model_sing_classify = gr.Dropdown(event_models, value=event_models[0], label="Select classification model")
-            with gr.Column(scale=7):
-                threshold_sing_classify = gr.Slider(0, 1, value=0, step=0.01, label="Prediction threshold", 
-                              info="This value sets a threshold by which texts classified flood or fire are accepted, \
-                                  higher values makes the classifier stricter (CAUTION: A value of 1 will set all predictions as none)", interactive=True)
-                
-        text_to_classify = gr.Textbox(label="Text", info="Enter tweet-like text", submit_btn=True)
-        text_to_classify_examples = gr.Examples([["The streets are flooded, I can't leave #BostonStorm"], 
-                                                 ["Controlado el incendio de Rodezno que ha obligado a desalojar a varias bodegas de la zona."], 
-                                                 ["Cambrils:estació Renfe inundada 19 persones dins d'un tren. FGC a Capellades, petit descarrilament 5 passatgers #Inuncat @emergenciescat"],
-                                                 ["Anscheinend steht die komplette Neckarwiese unter Wasser! #Hochwasser"]], text_to_classify)
-        
-        with gr.Row():
-            with gr.Column():
-                classification = gr.Textbox(label="Classification")
-            with gr.Column():
-                classification_score = gr.Number(label="Classification Score")
- 
-        
-
-                
-
-
-         
-        
-    # Test event listeners
-    T_predict_button.click(
-        load_and_classify_csv_dataframe, 
-        inputs=[T_file_input, T_text_field, T_event_model, T_threshold],  
-        outputs=[T_data, T_data_ss_state, T_data_filter]
-        )
-    
-    T_data.select(T_on_select, None, T_tweet_embed).then(fn=None, js="reloadTwitterWidgets()")
-    
-    @T_data_filter.input(inputs=[T_data_ss_state, T_data_filter], outputs=T_data)
-    def filter_df(df, filter): 
-        if filter == "All":
-            result_df = df.copy()
-        elif filter.startswith("Not"):
-            result_df = df[df["event_label"]!=filter.split('-')[1]].copy()
-        else: 
-            result_df = df[df["event_label"]==filter].copy()     
-        return gr.update(value=result_df) 
-
-
-    # Button clicks ETC Eval
-    ETCE_predict_button.click(
-        load_and_classify_csv, 
-        inputs=[file_input, text_field, event_model, threshold], 
-        outputs=[flood_checkbox_output, fire_checkbox_output, none_checkbox_output, model_confidence, num_posts, data, QA_addqry_button, QA_run_button])
-    
-    ETCE_accuracy_button.click(
-        calculate_accuracy, 
-        inputs=[flood_checkbox_output, fire_checkbox_output, none_checkbox_output, num_posts, text_field, data], 
-        outputs=[incorrect, correct, accuracy, data_eval, download_csv])
-    
-    
-    # Button clicks QA
-    QA_addqry_button.click(add_query, inputs=[query_inp, queries_state], outputs=[selected_queries, queries_state])
-
-    QA_run_button.click(qa_summarise, 
-                    inputs=[selected_queries, qa_llm_model, text_field, data], ## XXX fix text_field 
-                    outputs=[hsummary, qa_df])      
-    
-    
-    # Event listener for single text classification
-    text_to_classify.submit(
-        single_classification, 
-        inputs=[text_to_classify, model_sing_classify, threshold_sing_classify], 
-        outputs=[classification, classification_score])
-        
+import os
+import gradio as gr
+import pandas as pd
+
+from classifier import classify
+from statistics import mean
+from qa_summary import generate_answer
+
+
+HFTOKEN = os.environ["HF_TOKEN"]
+
+
+
+js = """
+    async () => {
+        // Load Twitter Widgets script
+        const script = document.createElement("script");
+        script.onload = () => console.log("Twitter Widgets.js loaded");
+        script.src = "https://platform.twitter.com/widgets.js";
+        document.head.appendChild(script);
+
+        // Define a global function to reload Twitter widgets
+        globalThis.reloadTwitterWidgets = () => {
+            if (window.twttr && twttr.widgets) {
+                twttr.widgets.load();
+            }
+        };
+    }
+"""
+
+def T_on_select(evt: gr.SelectData):
+    
+    if evt.index[1] == 3:
+        html = """<blockquote class="twitter-tweet" data-dnt="true" data-theme="dark">""" + \
+               f"""\n<a href="https://twitter.com/anyuser/status/{evt.value}"></a></blockquote>"""
+    else:
+        html = f"""<h2>{evt.value}</h2>"""
+    return gr.update(value=html)
+
+def single_classification(text, event_model, threshold):
+    res = classify(text, event_model, HFTOKEN, threshold)
+    return res["event"], res["score"]
+    
+def load_and_classify_csv(file, text_field, event_model, threshold):
+    filepath = file.name
+    if ".csv" in filepath:
+        df = pd.read_csv(filepath)
+    else:
+        df = pd.read_table(filepath)
+    
+    if text_field not in df.columns:
+        raise gr.Error(f"Error: Enter text column'{text_field}' not in CSV file.")
+
+    labels, scores = [], []
+    for post in df[text_field].to_list():
+        res = classify(post, event_model, HFTOKEN, threshold)
+        labels.append(res["event"])
+        scores.append(res["score"])
+
+    df["model_label"] = labels
+    df["model_score"] = scores
+    
+    # model_confidence = round(mean(scores), 5)   
+    model_confidence = mean(scores)
+    fire_related = gr.CheckboxGroup(choices=df[df["model_label"]=="fire"][text_field].to_list()) 
+    flood_related = gr.CheckboxGroup(choices=df[df["model_label"]=="flood"][text_field].to_list())
+    not_related = gr.CheckboxGroup(choices=df[df["model_label"]=="none"][text_field].to_list())
+    
+    return flood_related, fire_related, not_related, model_confidence, len(df[text_field].to_list()), df, gr.update(interactive=True), gr.update(interactive=True)
+
+def load_and_classify_csv_dataframe(file, text_field, event_model, threshold): #, filter
+    
+    filepath = file.name
+    if ".csv" in filepath:
+        df = pd.read_csv(filepath)
+    else:
+        df = pd.read_table(filepath)
+    
+    if text_field not in df.columns:
+        raise gr.Error(f"Error: Enter text column'{text_field}' not in CSV file.")
+
+    labels, scores = [], []
+    for post in df[text_field].to_list():
+        res = classify(post, event_model, HFTOKEN, threshold)
+        labels.append(res["event"])
+        scores.append(round(res["score"], 5))
+
+    df["event_label"] = labels
+    df["model_score"] = scores
+    
+    result_df = df[[text_field, "event_label", "model_score", "tweet_id"]].copy()
+    result_df["tweet_id"] = result_df["tweet_id"].astype(str)
+    
+    filters = list(result_df["event_label"].unique())
+    extra_filters = ['Not-'+x for x in filters]+['All']
+    
+    return gr.update(value=result_df), result_df, gr.update(choices=sorted(filters+extra_filters), 
+                                                            value='All', 
+                                                            label="Filter data by label", 
+                                                            visible=True)
+
+
+def calculate_accuracy(flood_selections, fire_selections, none_selections, num_posts, text_field, data_df):
+    posts = data_df[text_field].to_list()
+    selections = flood_selections + fire_selections + none_selections
+    eval = []
+    for post in posts:
+        if post in selections:
+            eval.append("incorrect")
+        else:
+            eval.append("correct")
+
+    data_df["model_eval"] = eval
+    incorrect = len(selections)
+    correct = num_posts - incorrect
+    accuracy = (correct/num_posts)*100
+
+    data_df.to_csv("output.csv")
+    return incorrect, correct, accuracy, data_df, gr.DownloadButton(label=f"Download CSV", value="output.csv", visible=True) 
+    
+def init_queries(history):
+    history = history or []
+    if not history:
+        history = [
+        "What areas are being evacuated?",
+        "What areas are predicted to be impacted?",
+        "What areas are without power?",
+        "What barriers are hindering response efforts?",
+        "What events have been canceled?",
+        "What preparations are being made?",
+        "What regions have announced a state of emergency?",
+        "What roads are blocked / closed?",
+        "What services have been closed?",
+        "What warnings are currently in effect?",
+        "Where are emergency services deployed?",
+        "Where are emergency services needed?",
+        "Where are evacuations needed?",
+        "Where are people needing rescued?",
+        "Where are recovery efforts taking place?",
+        "Where has building or infrastructure damage occurred?",
+        "Where has flooding occured?"
+        "Where are volunteers being requested?",
+        "Where has road damage occured?",
+        "What area has the wildfire burned?",
+        "Where have homes been damaged or destroyed?"]
+    
+    return gr.CheckboxGroup(choices=history), history
+
+def add_query(to_add, history):
+    if to_add not in history:
+        history.append(to_add)
+    return gr.CheckboxGroup(choices=history), history
+
+def qa_summarise(selected_queries, qa_llm_model, text_field, data_df):
+    
+    qa_input_df = data_df[data_df["model_label"] != "none"].reset_index()
+    texts = qa_input_df[text_field].to_list()
+    
+    summary = generate_answer(qa_llm_model, texts, selected_queries[0], selected_queries, mode="multi_summarize")
+
+    doc_df = pd.DataFrame()
+    doc_df["number"] = [i+1 for i in range(len(texts))]
+    doc_df["text"] = texts
+    
+    return summary, doc_df
+
+            
+with gr.Blocks(fill_width=True) as demo:
+    
+    demo.load(None,None,None,js=js)
+    
+    event_models = ["jayebaku/distilbert-base-multilingual-cased-crexdata-relevance-classifier",
+                    "jayebaku/distilbert-base-multilingual-cased-weather-classifier-2",
+                    "jayebaku/twitter-xlm-roberta-base-crexdata-relevance-classifier",
+                    "jayebaku/twhin-bert-base-crexdata-relevance-classifier"]
+    
+    T_data_ss_state = gr.State(value=pd.DataFrame())
+    
+    
+    with gr.Tab("Event Type Classification"):
+        gr.Markdown(
+        """
+        # T4.5 Relevance Classifier Demo 
+        This is a demo created to explore floods and wildfire classification in social media posts.\n
+        Usage:\n
+            - Upload .tsv or .csv data file (must contain a text column with social media posts).\n
+            - Next, type the name of the text column.\n
+            - Then, choose a BERT classifier model from the drop down.\n
+            - Finally, click the 'start prediction' buttton.\n
+        """)
+        with gr.Row():
+            with gr.Column(scale=4):
+                T_file_input = gr.File(label="Upload CSV or TSV File", file_types=['.tsv', '.csv'])
+                
+            with gr.Column(scale=6):
+                T_text_field = gr.Textbox(label="Text field name", value="tweet_text")
+                T_event_model = gr.Dropdown(event_models, value=event_models[0], label="Select classification model")
+                T_predict_button = gr.Button("Start Prediction")
+        with gr.Accordion("Prediction threshold", open=False):        
+            T_threshold = gr.Slider(0, 1, value=0, step=0.01, label="Prediction threshold", show_label=False, 
+                              info="This value sets a threshold by which texts classified flood or fire are accepted, \
+                                  higher values makes the classifier stricter (CAUTION: A value of 1 will set all predictions as none)", interactive=True)
+        
+        with gr.Row(): 
+            with gr.Column(scale=8):
+                T_data = gr.DataFrame(wrap=True,
+                                      show_fullscreen_button=True, 
+                                      show_copy_button=True,
+                                      show_row_numbers=True,
+                                      show_search="filter",
+                                      column_widths=["49%","17%","17%","17%"])
+                
+            with gr.Column(scale=2):
+                T_data_filter = gr.Dropdown(visible=False)
+                T_tweet_embed = gr.HTML("<h1>Select a Tweet ID to view Tweet</h1>")
+
+    
+
+    with gr.Tab("Event Type Classification Eval"):
+        gr.Markdown(
+        """
+        # T4.5 Relevance Classifier Demo 
+        This is a demo created to explore floods and wildfire classification in social media posts.\n
+        Usage:\n
+            - Upload .tsv or .csv data file (must contain a text column with social media posts).\n
+            - Next, type the name of the text column.\n
+            - Then, choose a BERT classifier model from the drop down.\n
+            - Finally, click the 'start prediction' buttton.\n
+        Evaluation:\n
+            - To evaluate the model's accuracy select the INCORRECT classifications using the checkboxes in front of each post.\n
+            - Then, click on the 'Calculate Accuracy' button.\n
+            - Then, click on the 'Download data as CSV' to get the classifications and evaluation data as a .csv file.
+        """)
+        with gr.Row():
+            with gr.Column(scale=4):
+                file_input = gr.File(label="Upload CSV or TSV File", file_types=['.tsv', '.csv'])
+                
+            with gr.Column(scale=6):
+                text_field = gr.Textbox(label="Text field name", value="tweet_text")
+                event_model = gr.Dropdown(event_models, value=event_models[0], label="Select classification model")
+                ETCE_predict_button = gr.Button("Start Prediction")
+        with gr.Accordion("Prediction threshold", open=False):        
+            threshold = gr.Slider(0, 1, value=0, step=0.01, label="Prediction threshold", show_label=False, 
+                              info="This value sets a threshold by which texts classified flood or fire are accepted, \
+                                  higher values makes the classifier stricter (CAUTION: A value of 1 will set all predictions as none)", interactive=True)
+        
+        with gr.Row(): # XXX confirm this is not a problem later --equal_height=True
+            with gr.Column():
+                gr.Markdown("""### Flood-related""")
+                flood_checkbox_output = gr.CheckboxGroup(label="Select ONLY incorrect classifications", interactive=True)
+                
+            with gr.Column():
+                gr.Markdown("""### Fire-related""")
+                fire_checkbox_output = gr.CheckboxGroup(label="Select ONLY incorrect classifications", interactive=True)
+                
+            with gr.Column():
+                gr.Markdown("""### None""")
+                none_checkbox_output = gr.CheckboxGroup(label="Select ONLY incorrect classifications", interactive=True) 
+
+        with gr.Row():
+            with gr.Column(scale=5):
+                gr.Markdown(r"""
+                Accuracy: is the model's ability to make correct predicitons.
+                It is the fraction of correct prediction out of the total predictions.
+                
+                $$
+                \text{Accuracy} = \frac{\text{Correct predictions}}{\text{All predictions}} * 100
+                $$
+                
+                Model Confidence: is the mean probabilty of each case 
+                belonging to their assigned classes. A value of 1 is best.
+                """, latex_delimiters=[{ "left": "$$", "right": "$$", "display": True }])
+                gr.Markdown("\n\n\n")
+                model_confidence = gr.Number(label="Model Confidence")
+                
+            with gr.Column(scale=5):
+                correct = gr.Number(label="Number of correct classifications")
+                incorrect = gr.Number(label="Number of incorrect classifications")
+                accuracy = gr.Number(label="Model Accuracy (%)")
+
+        ETCE_accuracy_button = gr.Button("Calculate Accuracy")
+        download_csv = gr.DownloadButton(visible=False)
+        num_posts = gr.Number(visible=False)
+        data = gr.DataFrame(visible=False) 
+        data_eval = gr.DataFrame(visible=False)
+        
+
+    qa_tab = gr.Tab("Question Answering")
+    with qa_tab:
+        gr.Markdown(
+        """
+        # Question Answering Demo
+        This section uses RAG to answer questions about the relevant social media posts identified by the relevance classifier\n 
+        Usage:\n
+            - Select queries from predefined\n
+            - Parameters for QA can be editted in sidebar\n
+            
+        Note: QA process is disabled untill after the relevance classification is done
+        """)
+        
+        with gr.Accordion("Parameters", open=False):
+            with gr.Row():
+                with gr.Column():
+                    qa_llm_model = gr.Dropdown(["mistral", "solar", "phi3mini"], label="QA model", value="phi3mini", interactive=True)
+                    aggregator = gr.Dropdown(["linear", "outrank"], label="Aggregation method", value="linear", interactive=True)
+                with gr.Column():
+                    batch_size = gr.Slider(50, 500, value=150, step=1, label="Batch size", info="Choose between 50 and 500", interactive=True)
+                    topk = gr.Slider(1, 10, value=5, step=1, label="Number of results to retrieve", info="Choose between 1 and 10", interactive=True)
+                    
+        selected_queries = gr.CheckboxGroup(label="Select at least one query using the checkboxes", interactive=True)
+        queries_state = gr.State()
+        qa_tab.select(init_queries, inputs=queries_state, outputs=[selected_queries, queries_state])
+
+        query_inp = gr.Textbox(label="Add custom queries like the one above, one at a time")
+        QA_addqry_button = gr.Button("Add to queries", interactive=False)
+        QA_run_button = gr.Button("Start QA", interactive=False)
+        hsummary = gr.Textbox(label="Summary")
+        
+        qa_df = gr.DataFrame()
+
+
+    with gr.Tab("Single Text Classification"):
+        gr.Markdown(
+        """
+        # Event Type Prediction Demo
+        In this section you test the relevance classifier with written texts.\n 
+        Usage:\n
+            - Type a tweet-like text in the textbox.\n
+            - Then press Enter.\n
+        """)
+        with gr.Row():
+            with gr.Column(scale=3):
+                model_sing_classify = gr.Dropdown(event_models, value=event_models[0], label="Select classification model")
+            with gr.Column(scale=7):
+                threshold_sing_classify = gr.Slider(0, 1, value=0, step=0.01, label="Prediction threshold", 
+                              info="This value sets a threshold by which texts classified flood or fire are accepted, \
+                                  higher values makes the classifier stricter (CAUTION: A value of 1 will set all predictions as none)", interactive=True)
+                
+        text_to_classify = gr.Textbox(label="Text", info="Enter tweet-like text", submit_btn=True)
+        text_to_classify_examples = gr.Examples([["The streets are flooded, I can't leave #BostonStorm"], 
+                                                 ["Controlado el incendio de Rodezno que ha obligado a desalojar a varias bodegas de la zona."], 
+                                                 ["Cambrils:estació Renfe inundada 19 persones dins d'un tren. FGC a Capellades, petit descarrilament 5 passatgers #Inuncat @emergenciescat"],
+                                                 ["Anscheinend steht die komplette Neckarwiese unter Wasser! #Hochwasser"]], text_to_classify)
+        
+        with gr.Row():
+            with gr.Column():
+                classification = gr.Textbox(label="Classification")
+            with gr.Column():
+                classification_score = gr.Number(label="Classification Score")
+ 
+        
+
+                
+
+
+         
+        
+    # Test event listeners
+    T_predict_button.click(
+        load_and_classify_csv_dataframe, 
+        inputs=[T_file_input, T_text_field, T_event_model, T_threshold],  
+        outputs=[T_data, T_data_ss_state, T_data_filter]
+        )
+    
+    T_data.select(T_on_select, None, T_tweet_embed)#.then(fn=None, js="reloadTwitterWidgets()")
+    
+    @T_data_filter.input(inputs=[T_data_ss_state, T_data_filter], outputs=T_data)
+    def filter_df(df, filter): 
+        if filter == "All":
+            result_df = df.copy()
+        elif filter.startswith("Not"):
+            result_df = df[df["event_label"]!=filter.split('-')[1]].copy()
+        else: 
+            result_df = df[df["event_label"]==filter].copy()     
+        return gr.update(value=result_df) 
+
+
+    # Button clicks ETC Eval
+    ETCE_predict_button.click(
+        load_and_classify_csv, 
+        inputs=[file_input, text_field, event_model, threshold], 
+        outputs=[flood_checkbox_output, fire_checkbox_output, none_checkbox_output, model_confidence, num_posts, data, QA_addqry_button, QA_run_button])
+    
+    ETCE_accuracy_button.click(
+        calculate_accuracy, 
+        inputs=[flood_checkbox_output, fire_checkbox_output, none_checkbox_output, num_posts, text_field, data], 
+        outputs=[incorrect, correct, accuracy, data_eval, download_csv])
+    
+    
+    # Button clicks QA
+    QA_addqry_button.click(add_query, inputs=[query_inp, queries_state], outputs=[selected_queries, queries_state])
+
+    QA_run_button.click(qa_summarise, 
+                    inputs=[selected_queries, qa_llm_model, text_field, data], ## XXX fix text_field 
+                    outputs=[hsummary, qa_df])      
+    
+    
+    # Event listener for single text classification
+    text_to_classify.submit(
+        single_classification, 
+        inputs=[text_to_classify, model_sing_classify, threshold_sing_classify], 
+        outputs=[classification, classification_score])
+        
 demo.launch()