Update app.py

app.py

@@ -1,157 +1,62 @@
-from pathlib import Path
-from typing import List, Dict, Tuple
-import matplotlib.colors as mpl_colors
-
-import pandas as pd
-import seaborn as sns
-import shinyswatch
-
-import shiny.experimental as x
-from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
-
-sns.set_theme()
-
-www_dir = Path(__file__).parent.resolve() / "www"
-
-df = pd.read_csv(Path(__file__).parent / "penguins.csv", na_values="NA")
-numeric_cols: List[str] = df.select_dtypes(include=["float64"]).columns.tolist()
-species: List[str] = df["Species"].unique().tolist()
-species.sort()
-
-app_ui = x.ui.page_fillable(
-    shinyswatch.theme.minty(),
-    x.ui.layout_sidebar(
-        x.ui.sidebar(
-            # Artwork by @allison_horst
-            ui.input_selectize(
-                "xvar",
-                "X variable",
-                numeric_cols,
-                selected="Bill Length (mm)",
-            ),
-            ui.input_selectize(
-                "yvar",
-                "Y variable",
-                numeric_cols,
-                selected="Bill Depth (mm)",
-            ),
-            ui.input_checkbox_group(
-                "species", "Filter by species", species, selected=species
-            ),
-            ui.hr(),
-            ui.input_switch("by_species", "Show species", value=True),
-            ui.input_switch("show_margins", "Show marginal plots", value=True),
-        ),
-        ui.output_ui("value_boxes"),
-        x.ui.output_plot("scatter", fill=True),
-        ui.help_text(
-            "Artwork by ",
-            ui.a("@allison_horst", href="https://twitter.com/allison_horst"),
-            class_="text-end",
-        ),
-        fill=True,
-        fillable=True,
-    ),
-)
-
-
-def server(input: Inputs, output: Outputs, session: Session):
-    @reactive.Calc
-    def filtered_df() -> pd.DataFrame:
-        """Returns a Pandas data frame that includes only the desired rows"""
-
-        # This calculation "req"uires that at least one species is selected
-        req(len(input.species()) > 0)
-
-        # Filter the rows so we only include the desired species
-        return df[df["Species"].isin(input.species())]
-
-    @output
-    @render.plot
-    def scatter():
-        """Generates a plot for Shiny to display to the user"""
-
-        # The plotting function to use depends on whether margins are desired
-        plotfunc = sns.jointplot if input.show_margins() else sns.scatterplot
-
-        plotfunc(
-            data=filtered_df(),
-            x=input.xvar(),
-            y=input.yvar(),
-            palette=palette,
-            hue="Species" if input.by_species() else None,
-            hue_order=species,
-            legend=False,
+import os
+import tempfile
+from flask import Flask, request, jsonify, send_file
+from gradio_client import Client
+from openai_chat_module import OpenaiChatModule
+from text2speech import VITSApiTTS
+
+app = Flask(__name__)
+
+@app.route('/process_speech', methods=['POST'])
+def upload_and_return_temp_file():
+    # try:
+        if 'audio' not in request.files:
+            return jsonify({"error": "No audio file provided"})
+
+        file = request.files['audio']
+        filename = request.form.get('filename')
+        modelid = request.form.get('modelid', default=2)
+
+        if not filename:
+            return jsonify({"error": "No filename parameter provided"})
+
+        # Create a temporary directory
+        temp_dir = tempfile.mkdtemp()
+
+        # Save the audio file to the temporary directory with the specified filename and .wav extension
+        destination_path = os.path.join(temp_dir, f"{filename}.wav")
+        file.save(destination_path)
+        print(destination_path)
+        # from gradio_client import Client
+
+        client = Client()
+        result = client.predict(
+                        destination_path,	# str (filepath or URL to file) in 'Input' Audio component
+                        api_name="/predict"
         )
 
-    @output
-    @render.ui
-    def value_boxes():
-        df = filtered_df()
-
-        def penguin_value_box(title: str, count: int, bgcol: str, showcase_img: str):
-            return x.ui.value_box(
-                title,
-                count,
-                {"class_": "pt-1 pb-0"},
-                showcase=x.ui.bind_fill_role(
-                    ui.tags.img(
-                        {"style": "object-fit:contain;"},
-                        src=showcase_img,
-                    ),
-                    item=True,
-                ),
-                theme_color=None,
-                style=f"background-color: {bgcol};",
-                height="90px",
-                full_screen=True,
-            )
-
-        if not input.by_species():
-            return penguin_value_box(
-                "Penguins",
-                len(df.index),
-                bg_palette["default"],
-                # Artwork by @allison_horst
-                showcase_img="penguins.png",
-            )
-
-        value_boxes = [
-            penguin_value_box(
-                name,
-                len(df[df["Species"] == name]),
-                bg_palette[name],
-                # Artwork by @allison_horst
-                showcase_img=f"{name}.png",
-            )
-            for name in species
-            # Only include boxes for _selected_ species
-            if name in input.species()
-        ]
-
-        return x.ui.layout_column_wrap(1 / len(value_boxes), *value_boxes)
-
+        openai_chat_module = OpenaiChatModule('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')
+        text = openai_chat_module.chat_with_origin_model(result)
+        print(text)
+        vits_tts = VITSApiTTS(modelid)
+        audio_data = vits_tts.text_to_speech_and_play(text)
 
-# "darkorange", "purple", "cyan4"
-colors = [[255, 140, 0], [160, 32, 240], [0, 139, 139]]
-colors = [(r / 255.0, g / 255.0, b / 255.0) for r, g, b in colors]
+        # Save audio data as a file in the temporary directory
+        audio_file_path = os.path.join(temp_dir, ""+filename+".mp3")
+        with open(audio_file_path, "wb") as f:
+            f.write(audio_data)
+        print(audio_file_path)
+        # Return the audio file as a response with the correct content type
+        response = send_file(audio_file_path, mimetype="audio/mp3")
 
-palette: Dict[str, Tuple[float, float, float]] = {
-    "Adelie": colors[0],
-    "Chinstrap": colors[1],
-    "Gentoo": colors[2],
-    "default": sns.color_palette()[0],  # type: ignore
-}
+        # # # Cleanup: Delete temporary files and directory
+        os.remove(destination_path)
+        os.rmdir(temp_dir)
 
-bg_palette = {}
-# Use `sns.set_style("whitegrid")` to help find approx alpha value
-for name, col in palette.items():
-    # Adjusted n_colors until `axe` accessibility did not complain about color contrast
-    bg_palette[name] = mpl_colors.to_hex(sns.light_palette(col, n_colors=7)[1])  # type: ignore
+        return response
 
+    # except Exception as e:
+    #     return jsonify({"error": str(e)})
 
-app = App(
-    app_ui,
-    server,
-    static_assets=str(www_dir),
-)
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000, debug=True)