Update app.py

app.py

@@ -1,104 +1,62 @@
 
+pip install streamlit transformers huggingface_hub
 import streamlit as st
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LinearRegression
-from sklearn.metrics import mean_squared_error, r2_score
-from sklearn import datasets
-import io
-
-def main():
-    st.title("California Housing Analysis")
-
-    # Load the California housing dataset
-    california = datasets.fetch_california_housing()
-    df = pd.DataFrame(california.data, columns=california.feature_names)
-    df['MedHouseVal'] = california.target
-
-    # Displaying initial data information
-    st.write("## Data Sample")
-    st.write(df.head())
-
-    st.write("## Data Statistics")
-    st.write(df.describe())
-
-    st.write("## Data Info")
-    buffer = io.StringIO()
-    df.info(buf=buffer)
-    s = buffer.getvalue()
-    st.text(s)
-
-    st.write("## Missing Values")
-    st.write(df.isnull().sum())
-
-    # Fixed target variable
-    target = 'MedHouseVal'
-    st.write(f"## Target Variable: {target}")
-
-    # Drop the target from the predictors list
-    predictor_options = df.columns.drop(target).tolist()
-    
-    # Multiselect widget to select predictor variables for regression
-    predictors = st.multiselect(
-        'Select predictor variables for regression:',
-        options=predictor_options,
-        default=predictor_options  # default to all predictors for MLR
-    )
-
-    # Splitting data for regression
-    X = df[predictors]
-    y = df[target]
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-    # Perform multilinear regression
-    mlr_model = LinearRegression()
-    mlr_model.fit(X_train, y_train)
-    mlr_y_pred = mlr_model.predict(X_test)
-    mlr_rmse = np.sqrt(mean_squared_error(y_test, mlr_y_pred))
-    mlr_r2 = r2_score(y_test, mlr_y_pred)
-
-    # Perform simple linear regression using only one predictor if possible
-    if 'AveRooms' in predictors:
-        slr_model = LinearRegression()
-        slr_X_train = X_train[['AveRooms']]
-        slr_X_test = X_test[['AveRooms']]
-        slr_model.fit(slr_X_train, y_train)
-        slr_y_pred = slr_model.predict(slr_X_test)
-        slr_rmse = np.sqrt(mean_squared_error(y_test, slr_y_pred))
-        slr_r2 = r2_score(y_test, slr_y_pred)
-
-        # Display RMSE and R-squared comparisons
-        st.write("## Regression Performance Comparison")
-        st.write(f"### Multilinear Regression (using all selected predictors)")
-        st.write(f'RMSE: {mlr_rmse}')
-        st.write(f'R-squared: {mlr_r2}')
-        
-        st.write(f"### Simple Linear Regression (using 'AveRooms')")
-        st.write(f'RMSE: {slr_rmse}')
-        st.write(f'R-squared: {slr_r2}')
-
-        # Plotting both regressions
-        fig, ax = plt.subplots(1, 2, figsize=(15, 6))
-        
-        ax[0].scatter(y_test, mlr_y_pred, color='blue')
-        ax[0].plot(y_test, y_test, color='red')
-        ax[0].set_title('Multilinear Regression: Actual vs Predicted')
-        ax[0].set_xlabel('Actual Values')
-        ax[0].set_ylabel('Predicted Values')
-        
-        ax[1].scatter(y_test, slr_y_pred, color='green')
-        ax[1].plot(y_test, y_test, color='red')
-        ax[1].set_title("Simple Linear Regression ('AveRooms'): Actual vs Predicted")
-        ax[1].set_xlabel('Actual Values')
-        ax[1].set_ylabel('Predicted Values')
-        
-        st.pyplot(fig)
-
-if __name__ == "__main__":
-    main()
-
+from huggingface_hub import InferenceApi, AsyncInferenceApi, InferenceTimeoutError, ModelStatus
+import asyncio
+
+# Initialize the Inference API
+model_id = "gpt2"  # Replace with your desired model
+api_token = "YOUR_HUGGING_FACE_TOKEN"  # Replace with your Hugging Face API token
+
+api = InferenceApi(repo_id=model_id, token=api_token)
+
+# Function to get response synchronously
+def get_response_sync(prompt):
+    try:
+        response = api(inputs=prompt)
+        return response[0]['generated_text']
+    except InferenceTimeoutError as e:
+        return f"Request timed out: {e}"
+
+# Function to get response asynchronously
+async def get_response_async(prompt):
+    async with AsyncInferenceApi(repo_id=model_id, token=api_token) as async_api:
+        try:
+            response = await async_api(inputs=prompt)
+            return response[0]['generated_text']
+        except InferenceTimeoutError as e:
+            return f"Request timed out: {e}"
+
+# Function to check model status
+def check_model_status():
+    status = api.status()
+    return status
+
+# Streamlit App
+st.title("Hugging Face Chatbot with Streamlit")
+st.write("This is a chatbot powered by Hugging Face's Inference API.")
+
+# Model status
+status = check_model_status()
+st.write(f"Model Status: {status}")
+
+# Input and button
+user_input = st.text_input("You: ", "")
+if st.button("Send"):
+    if user_input:
+        # Synchronous response
+        response = get_response_sync(user_input)
+        st.write(f"Bot: {response}")
+
+# Asynchronous interaction using asyncio
+async def async_interaction():
+    if user_input:
+        response = await get_response_async(user_input)
+        st.write(f"Bot: {response}")
+
+if st.button("Send (Async)"):
+    if user_input:
+        asyncio.run(async_interaction())