Update main.py

main.py

@@ -1,89 +1,89 @@
-from fastapi import FastAPI, HTTPException 
-from fastapi.middleware.cors import CORSMiddleware
-import google.generativeai as genai
-import os
-import requests
-import pandas as pd
-import validators
-from sklearn.feature_extraction.text import TfidfVectorizer
-
-from dotenv import load_dotenv
-load_dotenv()
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-df=pd.read_csv("salaries.csv")
-
-app = FastAPI()
-
-origins=[
-    "http://localhost:5173",
-    
-]
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=origins,
-    allow_credentials=True,
-    allow_methods=['*'],
-    allow_headers=['*']
-)
-
-model=genai.GenerativeModel("gemini-1.5-flash")
-
-
-df_combined = df.astype(str).apply(lambda x: ' '.join(x), axis=1).tolist()
-
-vectorizer = TfidfVectorizer()
-X = vectorizer.fit_transform(df_combined)
-
-def genetate_gemini_content(prompt,content):
-    response=model.generate_content([prompt,content])
-    return response.text
- 
- 
-work_year = df['work_year']
-job_titles = df['job_title']
-salaries = df['salary']
-experience_level = df['experience_level']
-employment_type = df['employment_type']
-salary_in_usd = df['salary_in_usd']
-company_size = df['company_size']
-
-# Combine all columns as strings for vectorization (if needed)
-df_combined = df.astype(str).apply(lambda x: ' '.join(x), axis=1).tolist()
-
-# Use TF-IDF Vectorizer for embeddings
-vectorizer = TfidfVectorizer()
-X = vectorizer.fit_transform(df_combined)
-
-prompt = f"""
-
-I have a dataset with the following salary information:
-
-Work Year: {work_year.tolist()}
-Job Titles: {job_titles.tolist()}
-Salaries (in USD): {salary_in_usd.tolist()}
-Experience Level: {experience_level.tolist()}
-Employment Type: {employment_type.tolist()}
-Company Size: {company_size.tolist()}
-
-Based on this data, can you answer the following question:
- 
-
-Please provide a short and direct answer based on the data. No extra explanations are needed, just the answer less than 2 lines and dont use "\ n" or  any special charcher other than related to the data .
-"""
-
-@app.get("/hi")
-async def ee():
-    return {"de":"hehe"}
-
-@app.post("/webqa")
-async def webchat(question:str):
-    try:
-        response=model.generate_content([prompt,question])
-        print(response.text)
-        return response.text 
-       
-    except:
-        raise HTTPException(status_code=500, detail="Internal Server Error")
-        
-
+from fastapi import FastAPI, HTTPException 
+from fastapi.middleware.cors import CORSMiddleware
+import google.generativeai as genai
+import os
+import requests
+import pandas as pd
+import validators
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+from dotenv import load_dotenv
+load_dotenv()
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+df=pd.read_csv("salaries.csv")
+
+app = FastAPI()
+
+origins=[
+    "http://localhost:5173",
+    
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=['*'],
+    allow_credentials=True,
+    allow_methods=['*'],
+    allow_headers=['*']
+)
+
+model=genai.GenerativeModel("gemini-2.0-flash-lite")
+
+
+df_combined = df.astype(str).apply(lambda x: ' '.join(x), axis=1).tolist()
+
+vectorizer = TfidfVectorizer()
+X = vectorizer.fit_transform(df_combined)
+
+def genetate_gemini_content(prompt,content):
+    response=model.generate_content([prompt,content])
+    return response.text
+ 
+ 
+work_year = df['work_year']
+job_titles = df['job_title']
+salaries = df['salary']
+experience_level = df['experience_level']
+employment_type = df['employment_type']
+salary_in_usd = df['salary_in_usd']
+company_size = df['company_size']
+
+# Combine all columns as strings for vectorization (if needed)
+df_combined = df.astype(str).apply(lambda x: ' '.join(x), axis=1).tolist()
+
+# Use TF-IDF Vectorizer for embeddings
+vectorizer = TfidfVectorizer()
+X = vectorizer.fit_transform(df_combined)
+
+prompt = f"""
+
+I have a dataset with the following salary information:
+
+Work Year: {work_year.tolist()}
+Job Titles: {job_titles.tolist()}
+Salaries (in USD): {salary_in_usd.tolist()}
+Experience Level: {experience_level.tolist()}
+Employment Type: {employment_type.tolist()}
+Company Size: {company_size.tolist()}
+
+Based on this data, can you answer the following question:
+ 
+
+Please provide a short and direct answer based on the data. No extra explanations are needed, just the answer less than 2 lines and dont use "\ n" or  any special charcher other than related to the data .
+"""
+
+@app.get("/hi")
+async def ee():
+    return {"de":"hehe"}
+
+@app.post("/webqa")
+async def webchat(question:str):
+    try:
+        response=model.generate_content([prompt,question])
+        print(response.text)
+        return response.text 
+       
+    except:
+        raise HTTPException(status_code=500, detail="Internal Server Error")
+        
+