Update app.py

app.py

@@ -1,46 +1,53 @@
 import gradio as gr
-import requests
-
-API_KEY = "AIzaSyAr__CqumxiYScFXx2QG191SYvwZeWj2H8"
-
-def fetch_reviews(place_id: str, reviews_limit: int, ranking: str):
-    # Build the request URL for Google Maps Places API
-    base_url = "https://maps.googleapis.com/maps/api/place/details/json"
-    params = {
-        "fields": "name,rating,formatted_phone_number,reviews",
-        "place_id": place_id,
-        "key": API_KEY
-    }
-    response = requests.get(base_url, params=params)
+import os
+from langchain.agents import initialize_agent
+from langchain.llms import OpenAI
+from gradio_tools import BaseTool
+from langchain.memory import ConversationBufferMemory
+
+# Set up OpenAI API Key
+if not os.getenv("OPENAI_API_KEY"):
+    os.environ["OPENAI_API_KEY"] = "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"
+
+# Simulated method to fetch a review from Google Maps (You'll replace this with an actual implementation)
+def fetch_gmaps_review(location):
+    # Simulated review
+    review = "The location was outstanding with beautiful views and amazing food. Service could be better though."
+    return review
+
+class GMapsReviewSummarizationTool(BaseTool):
+
+    def __init__(self) -> None:
+        super().__init__(name="GMapsReviewSummarizer",
+                         description="A tool that fetches a Google Maps review based on a location and then summarizes it.",
+                         src="your_gradio_app_url_or_id_here")
+
+    def create_job(self, query: str) -> str:
+        review = fetch_gmaps_review(query)
+        return review
+
+    def postprocess(self, output: str) -> str:
+        llm = OpenAI(temperature=0.5)
+        response = llm.query(f"Summarize the following Google Maps review: '{output}'")
+        return response
+
+def main():
+    llm = OpenAI(temperature=0.5)
+    memory = ConversationBufferMemory(memory_key="review_history")
+    tools = [GMapsReviewSummarizationTool().langchain]
+
+    agent = initialize_agent(tools, llm, memory=memory, agent="conversational-review-summarizer", verbose=True)
+
+    def gr_interface(location_input):
+        return agent.run(input=f"Fetch and summarize a review for the location: {location_input}")
+
+    interface = gr.Interface(fn=gr_interface, 
+                             inputs="textbox", 
+                             outputs="textbox",
+                             live=True,
+                             title="GMaps Review Summarizer")
     
-    if response.status_code == 200:
-        data = response.json()
-        reviews = data.get("result", {}).get("reviews", [])[:reviews_limit]
-        
-        # Format the reviews for display
-        formatted_reviews = []
-        for review in reviews:
-            review_text = f"Author: {review['author_name']}\nRating: {review['rating']}\n{review.get('text', '')}\n\n"
-            formatted_reviews.append(review_text)
-
-        return "\n".join(formatted_reviews)
-
-    else:
-        return "Error fetching reviews. Please try again."
-
-# Define Gradio Interface
-interface = gr.Interface(
-    fn=fetch_reviews,
-    inputs=[
-        gr.components.Textbox(label="Google Maps Place ID", placeholder="ChIJN1t_tDeuEmsRUsoyG83frY4"),
-        gr.components.Slider(minimum=1, maximum=5, label="Number of Reviews", value=3),
-        gr.components.Dropdown(choices=["most_relevant", "newest"], label="Reviews Ranking Type", value="most_relevant")
-    ],
-    outputs=gr.components.Textbox(),
-    live=True,
-    title="Google Maps Reviews Fetcher",
-    description="Fetch reviews from Google Maps based on your criteria."
-)
-
-# If you're testing locally:
-interface.launch()
+    interface.launch()
+
+if __name__ == "__main__":
+    main()