Update app.py

app.py

@@ -1,34 +1,15 @@
 import os
 import gradio as gr
-from groq import Groq
 from dotenv import load_dotenv
 import json
 from deep_translator import GoogleTranslator
 import google.generativeai as genai
-load_dotenv()
-
-api1 = os.getenv("GEMINI_API_KEY")
-# api2 = os.getenv("Groq_key")
-# api3 = os.getenv("GRoq_key")
-# api2 = os.getenv("Groq_key")
-# api2 = os.getenv("Groq_key")
-# api2 = os.getenv("Groq_key")
-# api2 = os.getenv("Groq_key")
-
-apis = [
-    api1
-]
-# from google import genai
-
-# client = genai.Client()
 
-# response = client.models.generate_content(
-#     model="gemini-2.5-flash",
-#     contents="Explain how AI works in a few words",
-# )
-
-# print(response.text)
+load_dotenv()
 
+# Configure the Gemini API with your API key
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+genai.configure(api_key=GEMINI_API_KEY)
 
 def make_call(data):
     print(data)
@@ -38,45 +19,124 @@ def make_call(data):
     query = items['text']
     query = query.lower()
     answer = None
+
+    # Use the GenerativeModel class to interact with Gemini
+    model = genai.GenerativeModel('gemini-pro')  # You can choose a different Gemini model
+
     while True:
-        for api in apis:
-            client = Groq(
-                    api_key=api,
-                )  # Configure the model with the API key
-            # query = st.text_input("Enter your query")
-            prmptquery= f"Answer this query in a short message with wisdom, love and compassion, in context to bhagwat geeta, that feels like chatting to a person and provide references of shloks from chapters of bhagwat geeta which is relevant to the query. keep the answer short, precise and simple. Query= {query}"
-            try:
-                response = client.chat.completions.create(
-                messages=[
-                    {
-                        "role": "user",
-                        "content": prmptquery,
-                    }
-                ],
-                model="mixtral-8x7b-32768",
-                )
-                answer = response.choices[0].message.content
-                translated = GoogleTranslator(source='auto', target=language).translate(answer)
-            except Exception as e:
-                print(f"API call failed for: {e}")
-            if answer:
-                break
+        try:
+            # Craft your prompt specific for Gemini and desired output format
+            prompt_query = f"Answer this query in a short message with wisdom, love, and compassion, in context to Bhagavad Gita, that feels like chatting to a person and provide references of shlokas from chapters of Bhagavad Gita which are relevant to the query. Keep the answer short, precise, and simple. Query: {query}"
+
+            # Generate content using Gemini
+            response = model.generate_content(prompt_query)
+
+            # Access the generated text from the response
+            answer = response.text  # The generated content is in the 'text' attribute
+
+            # Translate the answer
+            translated = GoogleTranslator(source='auto', target=language).translate(answer)
+        except Exception as e:
+            print(f"API call failed for: {e}")
         if answer:
-                break
+            break
+
     respo = {
-                "message": translated,
-                "action": "nothing",
-                "function": "nothing",
-            }
+        "message": translated,
+        "action": "nothing",
+        "function": "nothing",
+    }
     print(translated)
     return json.dumps(respo)
 
-
-
 gradio_interface = gr.Interface(fn=make_call, inputs="text", outputs="text")
 gradio_interface.launch()
 
-# print(chat_completion)
+
+
+# import os
+# import gradio as gr
+# from groq import Groq
+# from dotenv import load_dotenv
+# import json
+# from deep_translator import GoogleTranslator
+# import google.generativeai as genai
+# load_dotenv()
+
+
+# api1 = os.getenv("GEMINI_API_KEY")
+# genai.configure(api_key=api1)
+
+
+# # api2 = os.getenv("Groq_key")
+# # api3 = os.getenv("GRoq_key")
+# # api2 = os.getenv("Groq_key")
+# # api2 = os.getenv("Groq_key")
+# # api2 = os.getenv("Groq_key")
+# # api2 = os.getenv("Groq_key")
+
+# # apis = [
+# #     api1
+# # ]
+# # from google import genai
+
+# # client = genai.Client()
+
+# # response = client.models.generate_content(
+# #     model="gemini-2.5-flash",
+# #     contents="Explain how AI works in a few words",
+# # )
+
+# # print(response.text)
+
+
+# def make_call(data):
+#     print(data)
+#     newdata = data.replace("'", '"')
+#     items = json.loads(newdata)
+#     language = items['lang']
+#     query = items['text']
+#     query = query.lower()
+#     answer = None
+#     while True:
+#         for api in apis:
+#             client = genai.Client(
+#                     api_key=api,
+#                 )  # Configure the model with the API key
+#             # query = st.text_input("Enter your query")
+#             prmptquery= f"Answer this query in a short message with wisdom, love and compassion, in context to bhagwat geeta, that feels like chatting to a person and provide references of shloks from chapters of bhagwat geeta which is relevant to the query. keep the answer short, precise and simple. Query= {query}"
+#             try:
+#                 response = client.chat.completions.create(
+#                 messages=[
+#                     {
+#                         "role": "user",
+#                         "content": prmptquery,
+#                     }
+#                 ],
+#                 model="mixtral-8x7b-32768",
+#                 )
+#                 answer = response.choices[0].message.content
+#                 translated = GoogleTranslator(source='auto', target=language).translate(answer)
+#             except Exception as e:
+#                 print(f"API call failed for: {e}")
+#             if answer:
+#                 break
+#         if answer:
+#                 break
+#     respo = {
+#                 "message": translated,
+#                 "action": "nothing",
+#                 "function": "nothing",
+#             }
+#     print(translated)
+#     return json.dumps(respo)
+
+
+
+# gradio_interface = gr.Interface(fn=make_call, inputs="text", outputs="text")
+# gradio_interface.launch()
+
+# # print(chat_completion)
 
 
 
@@ -102,38 +162,38 @@ gradio_interface.launch()
 
 
 
-# # Text to 3D
+# # # Text to 3D
 
-# import streamlit as st
-# import torch
-# from diffusers import ShapEPipeline
-# from diffusers.utils import export_to_gif
+# # import streamlit as st
+# # import torch
+# # from diffusers import ShapEPipeline
+# # from diffusers.utils import export_to_gif
 
-# # Model loading (Ideally done once at the start for efficiency)
-# ckpt_id = "openai/shap-e"  
-# @st.cache_resource  # Caches the model for faster subsequent runs
-# def load_model():
-#     return ShapEPipeline.from_pretrained(ckpt_id).to("cuda")  
+# # # Model loading (Ideally done once at the start for efficiency)
+# # ckpt_id = "openai/shap-e"  
+# # @st.cache_resource  # Caches the model for faster subsequent runs
+# # def load_model():
+# #     return ShapEPipeline.from_pretrained(ckpt_id).to("cuda")  
 
-# pipe = load_model()
+# # pipe = load_model()
 
-# # App Title
-# st.title("Shark 3D Image Generator")
+# # # App Title
+# # st.title("Shark 3D Image Generator")
 
-# # User Inputs
-# prompt = st.text_input("Enter your prompt:", "a shark")
-# guidance_scale = st.slider("Guidance Scale", 0.0, 20.0, 15.0, step=0.5)
+# # # User Inputs
+# # prompt = st.text_input("Enter your prompt:", "a shark")
+# # guidance_scale = st.slider("Guidance Scale", 0.0, 20.0, 15.0, step=0.5)
 
-# # Generate and Display Images
-# if st.button("Generate"):
-#     with st.spinner("Generating images..."):
-#         images = pipe(
-#             prompt,
-#             guidance_scale=guidance_scale,
-#             num_inference_steps=64,
-#             size=256,
-#         ).images
-#         gif_path = export_to_gif(images, "shark_3d.gif")
+# # # Generate and Display Images
+# # if st.button("Generate"):
+# #     with st.spinner("Generating images..."):
+# #         images = pipe(
+# #             prompt,
+# #             guidance_scale=guidance_scale,
+# #             num_inference_steps=64,
+# #             size=256,
+# #         ).images
+# #         gif_path = export_to_gif(images, "shark_3d.gif")
 
-#         st.image(images[0])  # Display the first image
-#         st.success("GIF saved as shark_3d.gif")
+# #         st.image(images[0])  # Display the first image
+# #         st.success("GIF saved as shark_3d.gif")