Update app.py

app.py

@@ -1,108 +1,85 @@
-import os
-import requests
-import pandas as pd
-import streamlit as st
-
-# Set API keys
-os.environ['OPENAI_API_KEY'] = 'AIzaSyC8tIrUlJcCRnyksbykluUZVvER6ynHQeI'
-os.environ['SERPAPI_API_KEY'] = '533b5ecf4f322a3e1fb6c6341fc922267805add7e804f7783949a17175ed2f69'
-
-# Define the directory to save CSV files
-SAVE_DIR = r'C:\Users\satya\DeloitteResearchAgent'  # Absolute directory to save CSV files
-os.makedirs(SAVE_DIR, exist_ok=True)  # Create directory if it doesn't exist
-
-def industry_research(company_name, industry_name):
-    """Fetch AI trends and use cases for the specified company and industry."""
-    query_trends = f"{company_name} {industry_name} AI trends"
-    query_use_cases = f"{company_name} {industry_name} AI use cases"
-    
-    params = {
-        "api_key": os.environ['SERPAPI_API_KEY'],
-        "q": query_trends,
-        "num": 5
-    }
-    
-    response_trends = requests.get("https://serpapi.com/search.json", params=params)
-    if response_trends.status_code == 200:
-        trends_results = response_trends.json().get('organic_results', [])
-    else:
-        trends_results = {"error": response_trends.text}
-    
-    params['q'] = query_use_cases  # Update query for use cases
-    response_use_cases = requests.get("https://serpapi.com/search.json", params=params)
-    if response_use_cases.status_code == 200:
-        use_cases_results = response_use_cases.json().get('organic_results', [])
-    else:
-        use_cases_results = {"error": response_use_cases.text}
-    
-    return trends_results, use_cases_results
-
-def summarize_trends(trends_results):
-    """Summarize industry trends from the search results."""
-    trends = []
-    for result in trends_results:
-        title = result.get('title')
-        link = result.get('link')
-        description = f"{title} can be explored further at [this link]({link})."
-        trends.append({"Title": title, "Link": link, "Description": description})
-    return trends
-
-def propose_use_cases(use_cases_results):
-    """Propose use cases based on search results."""
-    use_cases = []
-    for result in use_cases_results:
-        title = result.get('title')
-        link = result.get('link')
-        description = f"Consider implementing: {title}. More details can be found at [this link]({link})."
-        use_cases.append({"Title": title, "Link": link, "Description": description})
-    return use_cases
-
-def save_results_to_csv(findings, company_name):
-    """Save the findings to a CSV file and return the filename."""
-    df = pd.DataFrame(findings)
-    filename = os.path.join(SAVE_DIR, f"{company_name}_research_results.csv")  # Save to the specified directory
-    df.to_csv(filename, index=False)
-    return filename
-
-def main():
-    st.title("Multi-Agent Workflow for AI Use Cases")
-
-    # Input fields for company and industry
-    company_name = st.text_input("Enter the Company Name", "Deloitte")
-    industry_name = st.text_input("Enter the Industry Name", "Supply Chain Optimization")
-
-    if st.button("Research"):
-        if company_name and industry_name:
-            # Conduct industry research
-            trends_results, use_cases_results = industry_research(company_name, industry_name)
-            
-            # Display results
-            if 'error' not in trends_results and 'error' not in use_cases_results:
-                st.subheader(f"Industry Trends for {company_name} in {industry_name}:")
-                trends = summarize_trends(trends_results)
-                for trend in trends:
-                    st.write(trend["Description"])
-
-                # Propose Use Cases Section
-                st.subheader("Proposed Use Cases")
-                use_cases = propose_use_cases(use_cases_results)
-                for i, use_case in enumerate(use_cases):
-                    st.write(f"{i + 1}. Use Case {i + 1}: {use_case['Description']}")
-
-                # Resource Links Section
-                st.subheader("Resource Links")
-                for result in trends_results + use_cases_results:
-                    st.write(f"- [Resource: {result.get('title')}]({result.get('link')})")
-                
-                # Save results to CSV
-                combined_findings = trends + use_cases  # Combine both findings
-                csv_filename = save_results_to_csv(combined_findings, company_name)
-                st.success(f"Results saved to {csv_filename} in the directory '{SAVE_DIR}'.")
-
-            else:
-                st.error("Error: " + (trends_results.get('error') or use_cases_results.get('error')))
-        else:
-            st.warning("Please enter both a company name and an industry name.")
-
-if __name__ == "__main__":
-    main()
+import os
+import requests
+import streamlit as st
+import pandas as pd
+
+# Set API keys
+os.environ['OPENAI_API_KEY'] = 'AIzaSyC8tIrUlJcCRnyksbykluUZVvER6ynHQeI'
+os.environ['SERPAPI_API_KEY'] = '533b5ecf4f322a3e1fb6c6341fc922267805add7e804f7783949a17175ed2f69'
+
+# Define the path for saving CSV files
+HUGGING_FACE_FOLDER = 'https://huggingface.co/spaces/Satyam0077/MultiAgentWorkflowAI/tree/main'  # Update this to your actual Hugging Face directory
+SAVE_DIR = os.path.join(HUGGING_FACE_FOLDER, 'DeloitteResearchAgent')
+
+# Ensure the save directory exists
+os.makedirs(SAVE_DIR, exist_ok=True)
+
+# Define the functions for your workflow
+def industry_research(company_name, industry_name):
+    query = f"{company_name} {industry_name} AI trends"
+    params = {
+        "api_key": os.environ['SERPAPI_API_KEY'],
+        "q": query,
+        "num": 5
+    }
+    response = requests.get("https://serpapi.com/search.json", params=params)
+    if response.status_code == 200:
+        search_results = response.json()
+        return search_results.get('organic_results', [])
+    else:
+        return {"error": response.text}
+
+def summarize_findings(search_results):
+    findings = []
+    for result in search_results:
+        title = result.get('title')
+        link = result.get('link')
+        description = f"{title} can be explored further at [this link]({link})."
+        findings.append(description)
+    return findings
+
+def save_results_to_csv(results, company_name):
+    df = pd.DataFrame(results)
+    csv_file_path = os.path.join(SAVE_DIR, f"{company_name}_research_results.csv")
+    df.to_csv(csv_file_path, index=False)
+    st.success(f"Results saved to {csv_file_path}")
+
+def main():
+    st.title("Multi-Agent Workflow for AI Use Cases")
+
+    # Input fields for company and industry
+    company_name = st.text_input("Enter the Company Name", "Deloitte")
+    industry_name = st.text_input("Enter the Industry Name", "Supply Chain Optimization")
+
+    if st.button("Research"):
+        if company_name and industry_name:
+            # Conduct industry research
+            research_results = industry_research(company_name, industry_name)
+            
+            # Display results
+            if 'error' not in research_results:
+                st.subheader(f"Market Research Findings for {company_name} in {industry_name} Industry:")
+                findings = summarize_findings(research_results)
+                for finding in findings:
+                    st.write(finding)
+
+                # Proposed Use Cases Section
+                st.subheader("Proposed Use Cases")
+                for i, finding in enumerate(findings):
+                    st.write(f"{i + 1}. Use Case {i + 1}: {finding}")
+
+                # Resource Links Section
+                st.subheader("Resource Links")
+                for result in research_results:
+                    st.write(f"- [Resource: {result.get('title')}]({result.get('link')})")
+                
+                # Save results to CSV
+                save_results_to_csv(research_results, company_name)
+
+            else:
+                st.error("Error: " + research_results['error'])
+        else:
+            st.warning("Please enter both a company name and an industry name.")
+
+if __name__ == "__main__":
+    main()