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Update app.py
Browse files
app.py
CHANGED
@@ -86,7 +86,7 @@ def chat(message, history):
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response = random.choice(responses)
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break
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else:
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response = "I'm sorry, I didn't understand that. Could
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except Exception as e:
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response = f"An error occurred: {str(e)}"
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@@ -132,7 +132,7 @@ def get_place_details(place_id, api_key):
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return response.json()
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def fetch_nearby_health_professionals(location):
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api_key = "
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query = "therapist OR counselor OR mental health professional OR marriage and family therapist OR psychotherapist OR psychiatrist OR psychologist OR nutritionist OR wellness doctor OR holistic practitioner OR integrative medicine OR chiropractor OR naturopath"
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radius = 50000 # 50 km radius
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@@ -165,7 +165,8 @@ with gr.Blocks() as demo:
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# Model prediction for emotion detection
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def predict_emotion(text):
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# Show suggestions based on the detected emotion
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def show_suggestions(emotion):
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@@ -241,7 +242,8 @@ with gr.Blocks() as demo:
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# Prediction button for sentiment analysis
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def predict_sentiment(text):
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sentiment_output = gr.Textbox(label="Predicted Sentiment")
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submit_sentiment.click(predict_sentiment, inputs=user_input_sentiment, outputs=sentiment_output)
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response = random.choice(responses)
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break
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else:
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response = "I'm sorry, I didn't understand that. Could you please rephrase?"
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except Exception as e:
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response = f"An error occurred: {str(e)}"
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return response.json()
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def fetch_nearby_health_professionals(location):
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api_key = "YOUR_GOOGLE_API_KEY" # Replace with your actual Google API key
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query = "therapist OR counselor OR mental health professional OR marriage and family therapist OR psychotherapist OR psychiatrist OR psychologist OR nutritionist OR wellness doctor OR holistic practitioner OR integrative medicine OR chiropractor OR naturopath"
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radius = 50000 # 50 km radius
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# Model prediction for emotion detection
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def predict_emotion(text):
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result = emotion_pipeline(text)
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return result[0]['label']
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# Show suggestions based on the detected emotion
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def show_suggestions(emotion):
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# Prediction button for sentiment analysis
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def predict_sentiment(text):
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result = sentiment_pipeline(text)
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return result[0]['label']
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sentiment_output = gr.Textbox(label="Predicted Sentiment")
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submit_sentiment.click(predict_sentiment, inputs=user_input_sentiment, outputs=sentiment_output)
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