Update app.py

app.py

@@ -1,64 +1,55 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
+import pandas as pd
+from sklearn.svm import SVC
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+
+# Sample mock data (Morningstar-like)
+data = {
+    "5Y_Return": [14.0, 7.5, 13.2, 6.0, 15.0, 8.0, 12.0, 6.5, 10.5, 7.2],
+    "Volatility": [8.0, 6.5, 7.8, 9.0, 7.0, 6.2, 7.1, 8.5, 6.8, 7.9],
+    "Risk_Score": [2, 3, 2, 4, 1, 3, 2, 4, 2, 3],
+    "Rating": ["Good", "Bad", "Good", "Bad", "Good", "Bad", "Good", "Bad", "Good", "Bad"]
+}
+
+df = pd.DataFrame(data)
+
+# Convert labels to binary (Good = 1, Bad = 0)
+df['Label'] = df['Rating'].map({'Good': 1, 'Bad': 0})
+
+# Features and labels
+X = df[["5Y_Return", "Volatility", "Risk_Score"]]
+y = df["Label"]
+
+# Scale features
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
+# Train an SVM classifier
+model = SVC(kernel="linear", probability=True)
+model.fit(X_scaled, y)
+
+# Prediction function for Gradio
+def classify_fund(return_5y, volatility, risk_score):
+    input_data = [[return_5y, volatility, risk_score]]
+    input_scaled = scaler.transform(input_data)
+    prediction = model.predict(input_scaled)[0]
+    confidence = model.predict_proba(input_scaled)[0][prediction]
+    result = "Good Investment" if prediction == 1 else "Bad Investment"
+    return f"{result} (Confidence: {confidence:.2f})"
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 SVM Classifier: Is this a Good Mutual Fund?")
+    with gr.Row():
+        return_input = gr.Number(label="5-Year Return (%)", value=10.0)
+        vol_input = gr.Number(label="Volatility (%)", value=7.0)
+        risk_input = gr.Number(label="Risk Score (1=Low, 5=High)", value=3)
+    output = gr.Textbox(label="Prediction")
+
+    classify_btn = gr.Button("Classify Fund")
+    classify_btn.click(fn=classify_fund, inputs=[return_input, vol_input, risk_input], outputs=output)
+
+# Launch app
 if __name__ == "__main__":
     demo.launch()