files

.env

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+SUPABASE_URL=https://hgsdcoqgvdjuxvcscqzn.supabase.co
+SUPABASE_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6Imhnc2Rjb3FndmRqdXh2Y3NjcXpuIiwicm9sZSI6ImFub24iLCJpYXQiOjE3NDkxNTMxNDEsImV4cCI6MjA2NDcyOTE0MX0.pYigfNha5pge2DMj9sMOwQ1RUqwh2Cy_zQws3A5IwRo

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import torch
+from chronos import ChronosPipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from supabase import create_client, Client
+import os
+import plotly.express as px
+
+# Initialize Supabase client with API key from environment variables
+SUPABASE_URL = os.getenv("SUPABASE_URL")
+SUPABASE_KEY = os.getenv("SUPABASE_KEY")
+if not SUPABASE_URL or not SUPABASE_KEY:
+    raise ValueError("SUPABASE_URL and SUPABASE_KEY must be set as environment variables.")
+supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
+
+# Initialize Chronos-T5-Large for forecasting
+chronos_pipeline = ChronosPipeline.from_pretrained(
+    "amazon/chronos-t5-large",
+    device_map="cuda" if torch.cuda.is_available() else "cpu",
+    torch_dtype=torch.bfloat16
+)
+
+# Initialize Prophet-Qwen3-4B-SFT for Arabic reports
+qwen_tokenizer = AutoTokenizer.from_pretrained("radm/prophet-qwen3-4b-sft")
+qwen_model = AutoModelForCausalLM.from_pretrained(
+    "radm/prophet-qwen3-4b-sft",
+    device_map="cuda" if torch.cuda.is_available() else "cpu",
+    torch_dtype=torch.bfloat16
+)
+
+def fetch_supabase_data(table_name: str = "sentiment_data") -> pd.DataFrame:
+    """Fetch time series data from Supabase using the provided API key."""
+    try:
+        response = supabase.table(table_name).select("date, sentiment").order("date", desc=False).execute()
+        if response.data:
+            df = pd.DataFrame(response.data)
+            df['date'] = pd.to_datetime(df['date'])
+            return df
+        else:
+            raise ValueError("No data found in Supabase table.")
+    except Exception as e:
+        raise Exception(f"Error fetching Supabase data: {str(e)}")
+
+def forecast_and_report(data_source: str, csv_file=None, prediction_length: int = 30, table_name: str = "sentiment_data"):
+    """Run forecasting with Chronos-T5-Large and generate Arabic report with Qwen3-4B-SFT."""
+    try:
+        # Load data
+        if data_source == "Supabase":
+            df = fetch_supabase_data(table_name)
+        else:
+            if not csv_file:
+                return {"error": "Please upload a CSV file."}, None, None
+            df = pd.read_csv(csv_file)
+            if "sentiment" not in df.columns or "date" not in df.columns:
+                return {"error": "CSV must contain 'date' and 'sentiment' columns."}, None, None
+            df['date'] = pd.to_datetime(df['date'])
+
+        # Prepare time series
+        context = torch.tensor(df["sentiment"].values, dtype=torch.float32)
+
+        # Run forecast
+        forecast = chronos_pipeline.predict(context, prediction_length)
+        low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0)
+
+        # Format forecast results
+        forecast_dates = pd.date_range(start=df["date"].iloc[-1] + pd.Timedelta(days=1), periods=prediction_length, freq="D")
+        forecast_df = pd.DataFrame({
+            "date": forecast_dates,
+            "low": low,
+            "median": median,
+            "high": high
+        })
+
+        # Create forecast plot
+        plot_df = forecast_df.copy()
+        fig = px.line(plot_df, x="date", y=["median", "low", "high"], title="Sentiment Forecast")
+        fig.update_traces(line=dict(color="blue"), selector=dict(name="median"))
+        fig.update_traces(line=dict(color="red", dash="dash"), selector=dict(name="low"))
+        fig.update_traces(line=dict(color="green", dash="dash"), selector=dict(name="high"))
+
+        # Generate Arabic report
+        prompt = (
+            "اكتب تقريراً رسمياً بالعربية يلخص توقعات المشاعر للأيام الثلاثين القادمة بناءً على البيانات التالية:\n"
+            f"- متوسط التوقعات: {median[:5].tolist()} (أول 5 أيام)...\n"
+            f"- الحد الأدنى (10%): {low[:5].tolist()}...\n"
+            f"- الحد الأعلى (90%): {high[:5].tolist()}...\n"
+            "التقرير يجب أن يكون موجزاً (200-300 كلمة)، يشرح الاتجاهات، ويستخدم لغة رسمية."
+        )
+        inputs = qwen_tokenizer(prompt, return_tensors="pt").to(qwen_model.device)
+        outputs = qwen_model.generate(
+            inputs["input_ids"],
+            max_new_tokens=500,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9
+        )
+        report = qwen_tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        return forecast_df.to_dict(), fig, report
+
+    except Exception as e:
+        return {"error": f"An error occurred: {str(e)}"}, None, None
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Sentiment Forecasting and Arabic Reporting")
+    data_source = gr.Radio(["Supabase", "CSV Upload"], label="Data Source", value="Supabase")
+    csv_file = gr.File(label="Upload CSV (if CSV selected)")
+    table_name = gr.Textbox(label="Supabase Table Name", value="sentiment_data")
+    prediction_length = gr.Slider(1, 60, value=30, step=1, label="Prediction Length (days)")
+    submit = gr.Button("Run Forecast and Generate Report")
+    output = gr.JSON(label="Forecast Results")
+    plot = gr.Plot(label="Forecast Plot")
+    report = gr.Textbox(label="Arabic Report", lines=10)
+
+    submit.click(
+        fn=forecast_and_report,
+        inputs=[data_source, csv_file, prediction_length, table_name],
+        outputs=[output, plot, report]
+    )
+
+demo.launch()

requirements.txt

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+torch>=2.0.0
+transformers>=4.35.0
+gradio>=4.0.0
+pandas>=2.0.0
+numpy>=1.24.0
+supabase>=2.0.0
+git+https://github.com/amazon-science/chronos-forecasting.git