Rename main.py to app.py

main.py → app.py

@@ -1,73 +1,71 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-from dotenv import load_dotenv
-import os
-import google.generativeai as genai
-import joblib
-
-# Load environment variables
-load_dotenv()
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-
-# Load the machine learning model
-try:
-    model = joblib.load('./movie_review_classifier.joblib')
-except Exception as e:
-    raise ImportError(f"Failed to load model: {e}")
-
-app = FastAPI()
-
-# Define models for requests
-class QueryRequest(BaseModel):
-    question: str
-
-class Review(BaseModel):
-    text: str
-
-# Initialize the Gemini chat model
-gemini_model = genai.GenerativeModel("gemini-pro")
-chat = gemini_model.start_chat(history=[])
-
-mental_health_prompt = """
-You are an expert in providing mental health support. When a user describes their mental health issues,
-you should provide relevant articles or blog posts to assist them.
-"""
-
-# Gemini response function
-def get_gemini_response(question, prompt):
-    response = chat.send_message(f"{prompt} {question}", stream=True)
-    return [chunk.text for chunk in response]
-
-# Function to retrieve articles from a database or external source
-def get_articles(query):
-    return [
-        {"title": "Understanding Anxiety", "url": "https://newsinhealth.nih.gov/2016/03/understanding-anxiety-disorders", "summary": "A comprehensive guide on anxiety disorders."},
-        {"title": "Coping with Depression", "url": "https://www.helpguide.org/articles/depression/coping-with-depression.htm", "summary": "Effective strategies for dealing with depression."}
-    ]
-
-# Mental health support endpoint
-@app.post("/rag")
-async def mental_health_support(request: QueryRequest):
-    try:
-        responses = get_gemini_response(request.question, mental_health_prompt)
-        articles = get_articles(request.question)
-        result = {"responses": responses, "articles": articles}
-        return result
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-# Classification endpoint
-@app.post("/classification")
-async def classify_review(review: Review):
-    try:
-        prediction = model.predict([review.text])
-        return {"predicted_sentiment": prediction[0]}
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-# Main function to run the server
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)
-
-
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from dotenv import load_dotenv
+import os
+import google.generativeai as genai
+import joblib
+
+# Load environment variables
+load_dotenv()
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+# Load the machine learning model
+try:
+    model = joblib.load('./movie_review_classifier.joblib')
+except Exception as e:
+    raise ImportError(f"Failed to load model: {e}")
+
+app = FastAPI()
+
+# Define models for requests
+class QueryRequest(BaseModel):
+    question: str
+
+class Review(BaseModel):
+    text: str
+
+# Initialize the Gemini chat model
+gemini_model = genai.GenerativeModel("gemini-pro")
+chat = gemini_model.start_chat(history=[])
+
+mental_health_prompt = """
+You are an expert in providing mental health support. When a user describes their mental health issues,
+you should provide relevant articles or blog posts to assist them.
+"""
+
+# Gemini response function
+def get_gemini_response(question, prompt):
+    response = chat.send_message(f"{prompt} {question}", stream=True)
+    return [chunk.text for chunk in response]
+
+# Function to retrieve articles from a database or external source
+def get_articles(query):
+    return [
+        {"title": "Understanding Anxiety", "url": "https://newsinhealth.nih.gov/2016/03/understanding-anxiety-disorders", "summary": "A comprehensive guide on anxiety disorders."},
+        {"title": "Coping with Depression", "url": "https://www.helpguide.org/articles/depression/coping-with-depression.htm", "summary": "Effective strategies for dealing with depression."}
+    ]
+
+# Mental health support endpoint
+@app.post("/rag")
+async def mental_health_support(request: QueryRequest):
+    try:
+        responses = get_gemini_response(request.question, mental_health_prompt)
+        articles = get_articles(request.question)
+        result = {"responses": responses, "articles": articles}
+        return result
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# Classification endpoint
+@app.post("/classification")
+async def classify_review(review: Review):
+    try:
+        prediction = model.predict([review.text])
+        return {"predicted_sentiment": prediction[0]}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# Main function to run the server
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)