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import gradio as gr
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
from fastapi import FastAPI, Request
import uvicorn
from fastapi.middleware.cors import CORSMiddleware
# Create FastAPI app
app = FastAPI()
# Add CORS middleware to allow requests from your React Native app
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # In production, specify your actual domain
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Load chatbot model
chatbot_model = "microsoft/DialoGPT-medium"
tokenizer = AutoTokenizer.from_pretrained(chatbot_model)
model = AutoModelForCausalLM.from_pretrained(chatbot_model)
# Load emotion detection model
emotion_pipeline = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")
def generate_response(user_input):
# Generate chatbot response
input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
output = model.generate(input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(output[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
# Detect emotion
emotion_result = emotion_pipeline(user_input)
emotion = emotion_result[0]["label"]
return response, emotion
# Create API endpoint
@app.post("/analyze")
async def analyze_text(request: Request):
data = await request.json()
user_input = data.get("text", "")
if not user_input:
return {"error": "No text provided"}
response, emotion = generate_response(user_input)
# Return structured response
return {
"response": response,
"emotion": emotion,
"score": emotion_pipeline(user_input)[0]["score"]
}
# Create Gradio interface (optional, can keep for web testing)
iface = gr.Interface(
fn=generate_response,
inputs=gr.Textbox(label="Enter your message"),
outputs=[gr.Textbox(label="Chatbot Response"), gr.Textbox(label="Emotion Detected")],
live=True
)
# Mount Gradio app to FastAPI
app = gr.mount_gradio_app(app, iface, path="/")
# Only needed if running directly
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860) |